CN102046076A - Non-contact physiological motion sensor and method of use thereof - Google Patents
Non-contact physiological motion sensor and method of use thereofDownload PDFInfo
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本申请根据35U.S.C.§119(e)要求以下申请的优先权：2008年4月3日提交的标题为“Doppler Radar System for Local and Remote Respiration Signals Monitoring”的第61/072,983号美国临时专利申请(代理机构案卷号KSENS.021PR)；2008年4月3日提交的标题为“Method for Detection of Cessation of Breathing”的第61/072,982号美国临时专利申请(代理机构案卷号KSENS.023PR)；2008年4月3日提交的标题为“Method for Detection of Motion Interfering with Respiration”的第61/123,017号美国临时专利申请(代理机构案卷号KSENS.024PR)；2008年4月3日提交的标题为“Method for Detection of Presence of Subject”的第61/123,135号美国临时专利申请(代理机构案卷号KSENS.025PR)；2008年4月21日提交的标题为“Non-contact Spirometry with a Doppler Radar”的第61/125,021号美国临时专利申请(代理机构案卷号KSENS.028PR)；2008年4月21日提交的标题为“Monitoring Physical Activity with a Physiologic Monitor”的第61/125,019号美国临时专利申请(代理机构案卷号KSENS.029PR)；2008年4月21日提交的标题为“Non-contact Method for Calibrating Tidal Volume Measured with Displacement Sensors”的第61/125,018号美国临时专利申请(代理机构案卷号KSENS.030PR)；2008年4月21日提交的标题为“Use of Empirical Mode Decomposition to Extract Physiological Signals from Motion Measured with a Doppler Radar”的第61/125,013号美国临时专利申请(代理机构案卷号KSENS.032PR)；2008年4月21日提交的标题为“Use of Direction of Arrival and Empirical Mode Decomposition Algorithms to Isolate and Extract Physiological Motion Measured with a Doppler Radar”的第61/125,027号美国临时专利申请(代理机构案卷号KSENS.033PR)；2008年4月21日提交的标题为“Data Access Architectures for Doppler Radar Patient Monitoring Systems”的第61/125,022号美国临时专利申请(代理机构案卷号KSENS.034PR)；2008年4月21日提交的标题为“Use of Direction of Arrival Algorithms to Isolate and Separate Physiological Motion Measured with a Doppler Radar”的第61/125,020号美国临时专利申请(代理机构案卷号KSENS.035PR)；2008年4月22日提交的标题为“Biometric Signature Collection Using Doppler Radar System”的第61/125,164号美国临时专利申请(代理机构案卷号KSENS.036PR)；2008年5月23日提交的标题为“Doppler Radar Based Vital Signs Spot Checker”的第61/128,743号美国临时专利申请(代理机构案卷号KSENS.037PR)；2008年7月30日提交的标题为“DoppJer Radar Based Monitoring of Physiological Motion Using Direction of Arrival”的第61/137,519号美国临时专利申请(代理机构案卷号KSENS.039PR)；2008年7月30日提交的标题为“Doppler Radar Respiration Spot Checker with Narrow Bean Antenna Array”的第61/137,532号美国临时专利申请(代理机构案卷号KSENS.040PR)；2008年9月29日提交的标题为“Doppler Radar-Based Body Worn Respiration Sensor”的第61/194,838号美国临时专利申请(代理机构案卷号KSENS.041PR)；2008年9月29日提交的标题为“Wireless Sleep Monitor Utilizing Non-Contact Monitoring of Respiration Motion”的第61/194,836号美国临时专利申请(代理机构案卷号KSENS.042PR)；2008年9月29日提交的标题为“Continuous Respiratory Rate and Pulse Oximetry Monitoring System”的第61/194,839号美国临时专利申请(代理机构案卷号KSENS.043PR)；2008年9月29日提交的标题为“Separation of Multiple Targets′Physiological Signals Using Doppler Radar with DOA Processing”的第61/194,840号美国临时专利申请(代理机构案卷号KSENS.044PR)；2008年9月30日提交的标题为“Detection of Paradoxical Breathing with a Doppler Radar System”的第61/194,848号美国临时专利申请(代理机构案卷号KSENS.045PR)；2008年10月17日提交的标题为“Monitoring of Chronic Illness Using a Non-contact Respiration Monitor”的第61/196,762号美国临时专利申请(代理机构案卷号KSENS.046PR)；2008年12月2日提交的标题为“Detection of Paradoxical Breathing with a Paradoxical Breathing Indicator with a Doppler Radar System”的第61/200,761号美国临时专利申请(代理机构案卷号KSENS.047PR)；2008年12月3日提交的标题为“Doppler Radar Based Monitoring of Physiological Motion Using Direction of Arrival and An Identification Tag”的第61/200,876号美国临时专利申请(代理机构案卷号KSENS.048PR)；2008年12月29日提交的标题为“A Non-Contact Cardiopulmonary Sensor Device for Medical and Security Applications”的第61/141,213号美国临时专利申请(代理机构案卷号KSENS.049PR)；2009年1月9日提交的标题为“Doppler Radar Based Continuous Monitoring of Physiological Motion”的第61/204,881号美国临时专利申请(代理机构案卷号KA1-00050)；2009年1月9日提交的标题为“Doppler Radar Respiration Spot Checker with Narrow Beam Antenna Array”的第61/204,880号美国临时专利申请(代理机构案卷号KA1-00051)；2009年1月30日提交的标题为“Doppler Radar Respiration Spot Check Device with Narrow Beam Antenna Array：Kai Sensors Non-Contact Respiratory Rate Spot Check”的第61/206,356号美国临时专利申请(代理机构案卷号KA1-00052)；2009年2月20日提交的标题为“A Non-Contact Cardiopulmonary Monitoring Device for Medical Imaging System Applications”的第61/154,176号美国临时专利申请(代理机构案卷号KA1-00053)；2009年2月23日提交的标题为“Doppler Radar-Based Measurement of Vital Signs for Battlefield Triage”的第61/154,728号美国临时专利申请(代理机构案卷号KA1-00054)；2009年2月23日提交的标题为“Doppler Radar-Based Measurement of Presence and Vital Signs of Subjects for Home Healthcare”的第61/154,732号美国临时专利申请(代理机构案卷号KA1-00055)。上述每个申请都通过全文引用并入本文。This application claims priority under 35 U.S.C. §119(e) to U.S. Provisional Patent Application No. 61/072,983, filed April 3, 2008, entitled "Doppler Radar System for Local and Remote Respiration Signals Monitoring" (Attorney Docket No. KSENS.021PR); U.S. Provisional Patent Application No. 61/072,982, entitled "Method for Detection of Cessation of Breathing," filed April 3, 2008 (Attorney Docket No. KSENS.023PR); 2008 U.S. Provisional Patent Application No. 61/123,017, filed April 3, 2008, entitled "Method for Detection of Motion Interfering with Respiration" (Attorney Docket No. KSENS.024PR); U.S. Provisional Patent Application No. 61/123,135 (Attorney Docket No. KSENS.025PR) for "Method for Detection of Presence of Subject"; U.S. Provisional Patent Application No. 61/125,021 (Attorney Docket No. KSENS.028PR); U.S. Provisional Patent Application No. 61/125,019, filed April 21, 2008, entitled "Monitoring Physical Activity with a Physiologic Monitor" (Attorney Docket No. KSENS.029PR); U.S. Provisional Patent Application No. 61/125,018, filed April 21, 2008, entitled "Non-contact Method for Calibrating Tidal Volume Measured with Displacement Sensors" (Attorney Docket No. KSENS.030PR) ; U.S. Provisional Patent Application No. 61/125,013, entitled "Use of Empirical Mode Decomposition to Extract Physiological Signals from Motion Measured with a Doppler Radar," filed April 21, 2008 (Agency Docket No. KSENS.032PR); U.S. Provisional Patent No. 61/125,027, filed April 21, 2008, entitled "Use of Direction of Arrival and Empirical Mode Decomposition Algorithms to Isolate and Extract Physiological Motion Measured with a Doppler Radar" Application (Attorney Docket No. KSENS.033PR); U.S. Provisional Patent Application No. 61/125,022, entitled "Data Access Architectures for Doppler Radar Patient Monitoring Systems," filed April 21, 2008 (Attorney Docket No. KSENS.034PR ); U.S. Provisional Patent Application No. 61/125,020, entitled "Use of Direction of Arrival Algorithms to Isolate and Separate Physiological Motion Measured with a Doppler Radar," filed April 21, 2008 (Attorney Docket No. KSENS.035PR) ; U.S. Provisional Patent Application No. 61/125,164, filed April 22, 2008, entitled "Biometric Signature Collection Using Doppler Radar System" (Attorney Docket No. KSENS.036PR); filed May 23, 2008, entitled U.S. Provisional Patent Application No. 61/128,743 (Attorney Docket KSENS.037PR) for "Doppler Radar Based Vital Signs Spot Checker"; filed July 30, 2008, entitled "DoppJer Radar Based Monitoring of Physiological Motion Using Direction of U.S. Provisional Patent Application No. 61/137,519 (Attorney Docket No. KSENS.039PR) for "Arrival"; 61/137,532, filed July 30, 2008, entitled "Doppler Radar Respiration Spot Checker with Narrow Bean Antenna Array" U.S. Provisional Patent Application (Agency Docket No. KSENS.040PR); U.S. Provisional Patent Application No. 61/194,838, filed September 29, 2008, entitled "Doppler Radar-Based Body Worn Respiration Sensor" (Attorney Docket No. KSENS.041PR); September 2008 U.S. Provisional Patent Application No. 61/194,836 (Attorney Docket No. KSENS.042PR), entitled "Wireless Sleep Monitoring Utilizing Non-Contact Monitoring of Respiration Motion," filed September 29, 2008; U.S. Provisional Patent Application No. 61/194,839 (Attorney Docket No. KSENS.043PR) for "Continuous Respiratory Rate and Pulse Oximetry Monitoring System"; filed September 29, 2008, entitled "Separation of Multiple Targets' Physiological Signals Using Doppler Radar with DOA Processing," U.S. Provisional Patent Application No. 61/194,840 (Attorney Docket No. KSENS.044PR); 61/19, filed September 30, 2008, entitled "Detection of Paradoxical Breathing with a Doppler Radar System" U.S. Provisional Patent Application No. 194,848 (Attorney Docket No. KSENS.045PR); U.S. Provisional Patent Application No. 61/196,762, filed October 17, 2008, entitled "Monitoring of Chronic Illness Using a Non-contact Respiration Monitor" ( Attorney Docket No. KSENS.046PR); U.S. Provisional Patent Application No. 61/200,761, filed December 2, 2008, entitled "Detection of Paradoxical Breathing with a Paradoxical Breathing Indicator with a Doppler Radar System" (Attorney Docket No. KSENS.047PR); filed December 3, 2008, entitled "Doppler Radar Based Monitoring of U.S. Provisional Patent Application No. 61/200,876 (Attorney Docket KSENS.048PR) for Physiological Motion Using Direction of Arrival and An Identification Tag; filed December 29, 2008, entitled "A Non-Contact Cardiopulmonary Sensor Device for Medical and Security Applications," U.S. Provisional Patent Application No. 61/141,213 (Attorney Docket No. KSENS.049PR); 61/204,881, filed January 9, 2009, entitled "Doppler Radar Based Continuous Monitoring of Physiological Motion" U.S. Provisional Patent Application No. (Attorney Docket No. KA1-00050); U.S. Provisional Patent Application No. 61/204,880, filed January 9, 2009, entitled "Doppler Radar Respiration Spot Checker with Narrow Beam Antenna Array" (Attorney Docket No. KA1-00051); U.S. Provisional Patent Application No. 61/206,356, filed January 30, 2009, entitled "Doppler Radar Respiration Spot Check Device with Narrow Beam Antenna Array: Kai Sensors Non-Contact Respiratory Rate Spot Check" (Attorney Docket No. KA1-00052); U.S. Provisional Patent Application No. 61/154,176, entitled "A Non-Contact Cardiopulmonary Monitoring Device for Medical Imaging System Applications," filed February 20, 2009 (Attorney Docket No. KA1 -00053); U.S. Provisional Patent Application No. 61/154,728, filed Feb. 23, 2009, entitled "Doppler Radar-Based Measurement of Vital Signs for Battlefield Triage" (Attorney Docket No. KA1-00054); 2009 Feb. The 23rd submission is titled "Doppler Radar-Based U.S. Provisional Patent Application No. 61/154,732, "Measurement of Presence and Vital Signs of Subjects for Home Healthcare" (Attorney Docket No. KA1-00055). Each of the above applications is hereby incorporated by reference in its entirety.
技术领域technical field
一般地，本申请涉及能够评估主体的生理和心理状态的监测器，具体地，本申请涉及非接触的且基于雷达的生理传感器，以及使用该传感器的方法。In general, the present application relates to monitors capable of assessing the physical and psychological state of a subject, and in particular, the present application relates to non-contact and radar-based physiological sensors, and methods of using the same.
背景技术Background technique
可获取主体的生理信息的运动传感器可用于多种医学应用中，所述的生理信息例如连续或间歇的呼吸活动、心博、心血管活动、心肺活动。然而，这些生理活动通常在存在其他运动的时产生，例如，睡觉时的翻身等。因此，从这样的运动传感器得到的数据通常包括与被测量的生理活动相应的期望部分，和与其他运动、噪声等相应的干扰部分。现有的系统不能适当地将期望部分从干扰部分中分离出来。Motion sensors that can acquire physiological information of a subject, such as continuous or intermittent respiratory activity, heartbeat, cardiovascular activity, cardiopulmonary activity, can be used in a variety of medical applications. However, these physiological activities usually occur in the presence of other movements, for example, turning over during sleep, etc. Accordingly, data obtained from such motion sensors typically includes a desired portion corresponding to the physiological activity being measured, and a disturbing portion corresponding to other motion, noise, and the like. Existing systems cannot properly separate the desired part from the disturbing part.
发明内容Contents of the invention
使用感测生理运动的基于雷达的传感器以及处理系统解决上述问题，其中所述处理系统分析来自雷达的数据，以将与各种生理运动相应的期望的数据分量与由于其他活动、运动、噪声等而产生的非期望的数据分量区分开来。该系统可用于获取呼吸速率、心率以及生理波形，生理波形包括但不限于心率波形、脉搏波形和/或呼吸波形。分析上述数据和波形可获得多种生理参数和医学参数，例如呼吸速率、心率、呼吸努力、呼吸深度、一次换气量、生命体征、医学状况、生理状态或主体位置等。上述波形还可用于同步供气或对呼吸运动和/或心肺运动进行医学成像。可在多种实施方式中利用上述数据和波形中的信息，包括生命体征估计、呼吸暂停监测、普通患者监测、新生儿监测、烧伤患者监测、老人或残疾人的家庭监测、分类、慢性病管理、术后监测、医疗成像扫描期间对患者的监测、疾病检测、生理状态估计、生理或心理估计、预复苏估计、复苏后估计和/或测谎。运动传感器的多种实施方式可应用于多种场合，包括但不限于医院、诊所、家庭、护理设备、辅助生活设施、健康数据亭、急救室、应急输送、患者转运、灾区以及战场。运动传感器的多种实施方式可用于安全应用，包括但不限于机场、边境、体育事件以及其他公共时间的安全筛查，或者用作测谎仪。运动传感器的多种实施方式可从干扰、噪声或其他运动中区分心脏的有效测量和呼吸活动，并能提供连续的数据、时间点上的数据、间断的数据和/或逐点的数据，根据这些数据可识别速率、特征以及关键的变化。生理运动传感器的多种实施方式可以非接触且距离主体一定距离的方式进行操作。生理运动传感器的一些实施方式还可以放置于主体胸部与身体接触的方式进行操作。生理运动传感器的多种实施方式可对任何体位的主体进行操作，包括躺下、横卧、坐姿或站姿。生理运动传感器的多种实施方式对主体进行操作，可处于主体不同的位置，包括在主体上、主题侧面、主题背面、主体上面以及主体下面。The above problems are addressed using a radar-based sensor that senses physiological motion and a processing system that analyzes the data from the radar to separate desired data components corresponding to various physiological motions from other activities, motion, noise, etc. The resulting undesired data components are distinguished. The system can be used to obtain respiration rate, heart rate, and physiological waveforms, including but not limited to heart rate waveforms, pulse waveforms, and/or respiration waveforms. Various physiological and medical parameters can be obtained by analyzing the above data and waveforms, such as respiration rate, heart rate, respiration effort, respiration depth, ventilation volume, vital signs, medical condition, physiological state or subject position, etc. The waveforms described above can also be used to synchronize air delivery or for medical imaging of respiratory motion and/or cardiopulmonary motion. Information in the data and waveforms described above can be utilized in a variety of embodiments, including vital sign estimation, apnea monitoring, general patient monitoring, neonatal monitoring, burn patient monitoring, home monitoring of the elderly or disabled, triage, chronic disease management, Post-operative monitoring, monitoring of patients during medical imaging scans, disease detection, physiological status assessment, physiological or psychological assessment, pre-resuscitation assessment, post-resuscitation assessment, and/or polygraph detection. Various embodiments of motion sensors may be used in a variety of settings including, but not limited to, hospitals, clinics, homes, nursing facilities, assisted living facilities, health data kiosks, emergency rooms, emergency transport, patient transport, disaster areas, and battlefields. Various embodiments of motion sensors may be used in security applications including, but not limited to, security screening at airports, borders, sporting events, and other public times, or as a polygraph. Various embodiments of the motion sensor can distinguish valid measurements of cardiac and respiratory activity from interference, noise, or other motion, and can provide continuous data, point-in-time data, intermittent data, and/or point-by-point data, according to These data identify rates, characteristics, and key changes. Various embodiments of the physiological motion sensor can operate in a non-contact manner and at a distance from the subject. Some embodiments of the biomotion sensor may also operate by being placed on the subject's chest in contact with the body. Various embodiments of the physiological motion sensor can operate on a subject in any position, including lying down, lying down, sitting or standing. Various embodiments of the biomotion sensor operate on the subject and can be located in various locations on the subject, including on the subject, on the side of the subject, on the back of the subject, above the subject, and below the subject.
本发明的一个实施方式包括一种利用运动传感器检测运动的方法，该方法包括：辐射源生成电磁辐射，其中所述电磁辐射的频率在无线频率范围内；利用一个或多个发射器向主体发射所述电磁辐射；利用一个或多个接收器接收至少被所述主体散射的辐射；从所散射的辐射中提取多普勒频移信号；将所述多普勒频移信号转换成数字化运动信号，所述数字化运动信号包括一个或多个帧，其中所述一个或多个帧包括所述数字化运动信号的时间采样正交值；利用处理器执行的解调算法解调所述一个或多个帧，以隔离与所述主体的生理运动相应的信号，或隔离与主体的部分的生理运动相应的信号；分析所述信号，以获得与非心肺运动或其他干扰信号相应的信息；处理所述信号，以获得与所述主体的所述生理运动相应的或与所述主体一部分的所述生理运动相应的、基本上与所述非心肺运动或其他干扰信号分离的信息；以及将所述信息发送至输出系统，所述输出系统执行输出动作。One embodiment of the invention includes a method of detecting motion using a motion sensor, the method comprising: generating electromagnetic radiation from a radiation source, wherein the frequency of the electromagnetic radiation is in the radio frequency range; the electromagnetic radiation; receiving radiation scattered by at least the subject with one or more receivers; extracting a Doppler-shifted signal from the scattered radiation; converting the Doppler-shifted signal into a digitized motion signal , the digitized motion signal includes one or more frames, wherein the one or more frames include time-sampled quadrature values of the digitized motion signal; the one or more frames are demodulated using a demodulation algorithm executed by a processor frames to isolate signals corresponding to physiological motion of the subject, or to isolate signals corresponding to physiological motion of parts of the subject; analyze the signals to obtain information corresponding to non-cardiopulmonary motion or other interfering signals; process the signal to obtain information corresponding to said physiological movement of said subject or corresponding to said physiological movement of said subject, substantially separated from said non-cardiopulmonary movement or other interfering signals; and said information is sent to the output system, which performs the output action.
在本发明的一个实施方式中，所述输出系统包括显示单元，所述显示单元用于显示所述信息。在本发明的一个实施方式中，所述输出系统包括音响系统，所述音响系统基于所述信息发出能听到的信息或警报。在本发明的一个实施方式中，所述输出系统包括外部医疗系统，所述外部医疗系统基于所述信息执行动作。在本发明的一个实施方式中，所述解调算法包括线性解调算法、基于弧的解调算法或非线性解调算法。在本发明的一个实施方式中，所述信息至少以字符、图形或波形进行显示。In one embodiment of the present invention, the output system includes a display unit for displaying the information. In one embodiment of the invention, the output system comprises an audio system that emits an audible message or an alarm based on the information. In one embodiment of the invention, said output system comprises an external medical system that performs an action based on said information. In one embodiment of the present invention, the demodulation algorithm includes a linear demodulation algorithm, an arc-based demodulation algorithm or a nonlinear demodulation algorithm. In one embodiment of the present invention, the information is at least displayed in characters, graphics or waveforms.
在本发明的一个实施方式中，所述主体是人或动物，且所述生理运动包括由所述主体的呼吸活动引起的运动、由所述主体的心肺活动引起的运动、由所述主体的心搏引起的运动、由所述主体的心血管活动引起的运动、或由所述主体的生理活动引起的运动。In one embodiment of the present invention, the subject is a human or an animal, and the physiological movement includes movement caused by the breathing activity of the subject, movement caused by the cardiopulmonary activity of the subject, movement caused by the subject's Movement caused by a heartbeat, movement caused by cardiovascular activity of the subject, or movement caused by physiological activity of the subject.
在多种实施方式中，解调算法包括将所述复平面中的信号映射到最佳拟合线上，将复平面中的信号映射到主向量上，或将信号弧校准至最佳拟合圆并利用最佳拟合圆参数从所述信号弧中提取角距信息。In various embodiments, the demodulation algorithm includes mapping the signal in the complex plane onto a best fit line, mapping the signal in the complex plane onto principal vectors, or calibrating the signal arc to a best fit circle and extract angular distance information from the signal arc using the best fit circle parameter.
在多种实施方式中，解调包括在处理器中计算选自所述一个或多个帧的第一子集的第一组协方差矩阵；确定第一A矩阵，其中所述第一A矩阵包括第一组协方差矩阵的加权和；确定与所述第一A矩阵的第一初始值相应的第一参数向量；在与所述处理器通信的存储装置中存储所述第一参数向量。在本发明的一个实施方式中，解调包括在所述处理器中计算选自所述一个或多个帧的第二子集的第二组协方差矩阵；确定第二A矩阵，其中所述第二A矩阵包括第二组协方差矩阵的加权和；确定与所述第二A矩阵的第二初始值相应的第二参数向量；计算所述第一参数向量与所述第二参数向量的内积；在与所述处理器通信的存储装置中存储所述第一参数向量；所述第一参数向量与所述内积的符号相乘；以及将所述第二帧的值映射到所述第二参数向量上，以获取解调信号。在一个实施方式中，所述第一初始值包括所述第一A矩阵的最大特征值，且第一初始向量包括与所述第一特征值相应的特征向量。在一个实施方式中，所述第二初始值包括所述第二A矩阵的最大特征值，且第二初始向量包括与所述第二特征值相应的第二初始向量。In various embodiments, demodulating includes computing in a processor a first set of covariance matrices selected from a first subset of said one or more frames; determining a first A matrix, wherein said first A matrix including a weighted sum of a first set of covariance matrices; determining a first parameter vector corresponding to a first initial value of the first A matrix; storing the first parameter vector in a storage device in communication with the processor. In one embodiment of the invention, demodulating comprises calculating in said processor a second set of covariance matrices selected from a second subset of said one or more frames; determining a second A matrix, wherein said The second A matrix includes the weighted sum of the second group of covariance matrices; determine the second parameter vector corresponding to the second initial value of the second A matrix; calculate the first parameter vector and the second parameter vector storing the first parameter vector in a storage device in communication with the processor; multiplying the first parameter vector by the sign of the inner product; and mapping the value of the second frame to the on the second parameter vector to obtain the demodulated signal. In one embodiment, the first initial value includes the largest eigenvalue of the first A matrix, and the first initial vector includes an eigenvector corresponding to the first eigenvalue. In one embodiment, the second initial value includes the largest eigenvalue of the second A matrix, and the second initial vector includes a second initial vector corresponding to the second eigenvalue.
在一个实施方式中，所述辐射源包括振荡器。在一个实施方式中，所述一个或多个发射器包括一个或多个天线。在一个实施方式中，所述一个或多个接收器包括一个或多个天线或天线阵列。在一个实施方式中，所述发射天线和接收天线是同一天线。在一个实施方式中，所述接收器包括零拍接收机。在一个实施方式中，所述接收器包括外差接收机。在一个实施方式中，所述接收器包括低中频接收机，所述低中频接收机用于将所述多普勒频移信号转换为频率在低中频范围内的数字化的多普勒频移信号，所述数字化的多普勒频移信号被数字地转化为数字化的运动信号。In one embodiment, the radiation source comprises an oscillator. In one embodiment, the one or more transmitters include one or more antennas. In one embodiment, the one or more receivers comprise one or more antennas or antenna arrays. In one embodiment, the transmit antenna and the receive antenna are the same antenna. In one embodiment, the receiver comprises a zero-beat receiver. In one embodiment, the receiver comprises a heterodyne receiver. In one embodiment, the receiver comprises a low-IF receiver for converting the Doppler-shifted signal into a digitized Doppler-shifted signal with a frequency in the low-IF range , the digitized Doppler shifted signal is digitally converted into a digitized motion signal.
在一个实施方式中，所述处理器至少包括数字信号处理器、微处理器或计算机。In one embodiment, the processor includes at least a digital signal processor, a microprocessor or a computer.
在一个实施方式中，所述输出系统包括显示单元，所述显示单元用于在远程地点显示与用户生理运动相关的信息。In one embodiment, the output system includes a display unit for displaying information related to the user's physiological movement at a remote location.
在一个实施方式中，分析所述信号的步骤包括执行非心肺运动检测算法，如果非心肺运动检测算法在所述信号包括单一稳定信号源时，则非心肺运动检测算法检测所检测的非心肺运动的不存在；或者如果至少所述信号不稳定或至少所述信号具有多个信号源，则非心肺运动检测算法检测非心肺运动信号的存在。In one embodiment, the step of analyzing said signal comprises executing a non-cardiopulmonary motion detection algorithm that detects detected non-cardiopulmonary motion if said signal comprises a single stable source or if at least the signal is unstable or at least the signal has multiple signal sources, the non-cardiopulmonary motion detection algorithm detects the presence of a non-cardiopulmonary motion signal.
在一个实施方式中，分析所述信号的步骤包括执行非心肺运动检测算法，如果所述信号指示偏移量大于主体根据心肺活动的最大胸腔偏移量，则所述非心肺运动检测算法检测非心肺运动的存在。In one embodiment, the step of analyzing said signal comprises executing a non-cardiopulmonary motion detection algorithm that detects a non-cardiopulmonary motion detection algorithm if said signal indicates an excursion greater than the subject's maximum thoracic excursion according to cardiopulmonary activity. The presence of cardiopulmonary exercise.
在一个实施方式中，分析所述信号的步骤包括执行非心肺运动检测算法，如果与线性解调相关的最佳拟合向量出现显著变化，则所述非心肺运动检测算法检测非心肺运动的存在。In one embodiment, the step of analyzing said signal comprises performing a non-cardiopulmonary motion detection algorithm that detects the presence of non-cardiopulmonary motion if a significant change occurs in the best fit vector associated with linear demodulation .
在一个实施方式中，分析所述信号的步骤包括执行非心肺运动检测算法，如果所述信号的复系数星座图和与线性解调相关的最佳拟合向量之间的均方根(RMS)差值显著变化，则所述非心肺运动检测算法检测非心肺运动的存在。In one embodiment, the step of analyzing said signal comprises performing a non-cardiopulmonary motion detection algorithm if the root mean square (RMS) between the complex coefficient constellation of said signal and the best fit vector associated with linear demodulation If the difference changes significantly, the non-cardiopulmonary motion detection algorithm detects the presence of non-cardiopulmonary motion.
在一个实施方式中，分析所述信号的步骤包括执行非心肺运动检测算法，如果与基于弧解调相关的最佳拟合圆的原点或半径发生显著变化，则所述非心肺运动检测算法检测非心肺运动的存在。In one embodiment, the step of analyzing said signal comprises implementing a non-cardiopulmonary motion detection algorithm that detects a significant change in the origin or radius of the best-fit circle associated with arc-based demodulation. Presence of non-cardiopulmonary exercise.
在一个实施方式中，分析所述信号的步骤包括执行非心肺运动检测算法，如果所述信号的复系数星座图和与基于弧解调的最佳拟合圆之间的均方根差值显著变化，则所述非心肺运动检测算法检测非心肺运动的存在。In one embodiment, the step of analyzing said signal comprises performing a non-cardiopulmonary motion detection algorithm if the root mean square difference between the complex coefficient constellation of said signal and the circle of best fit based on arc demodulation is significant changes, the non-cardiopulmonary motion detection algorithm detects the presence of non-cardiopulmonary motion.
在一个实施方式中，分析所述信号的步骤包括：由处理器执行非心肺运动检测算法，以检测非心肺运动或来自所述数字化的运动信号中的其他信号干扰的存在或不存在，其中所述非心肺运动检测算法包括第一模式和第二模式，所述第一模式检测非心肺运动或其他信号干扰的存在，所述第二模式检测非心肺运动或其他信号干扰的中断。In one embodiment, the step of analyzing said signal comprises: executing by a processor a non-cardiopulmonary motion detection algorithm to detect the presence or absence of non-cardiopulmonary motion or other signal interference from said digitized motion signal, wherein said The non-cardiopulmonary motion detection algorithm includes a first mode that detects the presence of non-cardiopulmonary motion or other signal interference, and a second mode that detects an interruption of non-cardiopulmonary motion or other signal interference.
在一个实施方式中，包括基于非心肺运动或其他信号干扰的存在、非心肺运动或其他信号干扰的不存在、非心肺运动或其他信号干扰的程度、信噪比的估算、检测到低信号功率、或检测到信号削波或其他信号干扰将与非心肺运动信号的信号质量相关的信息发送至用于输出所述信息的输出系统。In one embodiment, includes estimation based on presence of non-cardiopulmonary or other signal interference, absence of non-cardiopulmonary or other signal interference, degree of non-cardiopulmonary or other signal interference, signal-to-noise ratio, detection of low signal power , or detection of signal clipping or other signal disturbance, sending information related to the signal quality of the non-cardiopulmonary exercise signal to an output system for outputting said information.
在一个实施方式中，所述第一模式包括：选择帧的第一子集，其中所述帧来自所述一个或多个帧，并在处理器中计算被低通滤波器滤波后的帧的第一子集的第一组协方差矩阵；确定第一A矩阵，其中所述A矩阵包括第一组协方差矩阵的加权和；确定与所述第一A矩阵的第一初始值相应的第一参数向量；以及在与所述处理器通信的存储装置中存储所述第一参数向量。在一个实施方式中，进一步包括：在所述处理器中计算被所述低通滤波器滤波的帧的第二子集的第二组协方差矩阵；确定与第二A矩阵，其中所述第二A矩阵包括第二组协方差矩阵的的加权和值；确定所述第二A矩阵的第一初始值和第二初始值；计算所述第一参数向量与所述第二参数向量的内积；计算所述第二A矩阵的第一初始值与第二初始值的比率；计算与高通滤波器滤波的帧的第三子集的平均能量相应的第一能量，以及与高通滤波器滤波的帧的第四子集的平均能量相应的第二能量；以及计算所述第二能量与所述第一能量的比率。在一个实施方式中，所述第一A矩阵的第一初始值包括所述第一A矩阵的最大特征值，且所述第一初始向量包括与所述最大特征值相应的特征向量。在一个实施方式中，所述第二A矩阵的第一初始值包括所述第二A矩阵的最大特征值，所述第二矩阵A的第二初始值包括所述第二A矩阵的第二最大特征值，且所述第二A矩阵的第二初始向量包括与所述第二A矩阵的第一初始值相应的特征向量。In one embodiment, said first mode comprises: selecting a first subset of frames from said one or more frames, and calculating in a processor the The first set of covariance matrices of the first subset; determine the first A matrix, wherein the A matrix includes a weighted sum of the first set of covariance matrices; determine the first initial value corresponding to the first A matrix a parameter vector; and storing the first parameter vector in a storage device in communication with the processor. In one embodiment, further comprising: calculating in the processor a second set of covariance matrices of a second subset of frames filtered by the low-pass filter; determining a second A matrix, wherein the first The second A matrix includes the weighted sum of the second group of covariance matrices; determine the first initial value and the second initial value of the second A matrix; calculate the interior of the first parameter vector and the second parameter vector calculating the ratio of the first initial value to the second initial value of the second A matrix; calculating the first energy corresponding to the average energy of the third subset of frames filtered by the high-pass filter, and filtering with the high-pass filter an average energy of a fourth subset of frames corresponding to a second energy; and calculating a ratio of the second energy to the first energy. In one embodiment, the first initial value of the first A matrix includes the largest eigenvalue of the first A matrix, and the first initial vector includes an eigenvector corresponding to the largest eigenvalue. In one embodiment, the first initial value of the second A matrix includes the largest eigenvalue of the second A matrix, and the second initial value of the second matrix A includes the second The largest eigenvalue, and the second initial vector of the second A matrix includes an eigenvector corresponding to the first initial value of the second A matrix.
在一个实施方式中，包括：在处理器中计算第一条件，所述第一条件是指所述内积小于第一阈值，或所述第二A矩阵的第一初始值与第二A矩阵的第二初始值的比率小于第二阈值，或者第二信号能量与第一信号能量的比率大于第三阈值，其中，如果第一条件为真且所述第二能量与所述第一能量的比率大于第四阈值，则检测到非心肺运动或其他信号干扰的存在。在一个实施方式中，所述第一阈值约在0.6-1之间。在一个实施方式中，所述第二阈值约在4-12之间。在一个实施方式中，所述第三阈值约在4-20之间。在一个实施方式中，所述第四阈值约在0.1-0.8之间。In one embodiment, it includes: calculating a first condition in the processor, the first condition refers to that the inner product is smaller than a first threshold, or the first initial value of the second A matrix and the second A matrix The ratio of the second initial value of is less than a second threshold, or the ratio of the second signal energy to the first signal energy is greater than a third threshold, wherein, if the first condition is true and the ratio of the second energy to the first energy The ratio is greater than a fourth threshold, the presence of non-cardiopulmonary exercise or other signal interference is detected. In one embodiment, the first threshold is approximately between 0.6-1. In one embodiment, the second threshold is between about 4-12. In one embodiment, the third threshold is approximately between 4-20. In one embodiment, the fourth threshold is approximately between 0.1-0.8.
在一个实施方式中，所述第二模式包括：在处理器中选择帧的第五子集中的帧的每一个连续子集；在处理器中计算帧的每个子集的协方差矩阵；在处理器中计算帧的每个子集的A′矩阵，其中所述A′矩阵是所述子集中协方差矩阵的加权平均值；在处理器中计算ρ矩阵，其中所述ρ矩阵的每个元素都与相应的A′矩阵的第一初始向量相应；计算所述ρ矩阵中每对初始向量的内积，并从所述内积中选出最小绝对值；计算A矩阵，所述A矩阵是帧的第六子集中的协方差矩阵的和；In one embodiment, said second mode comprises: selecting in a processor each successive subset of frames in the fifth subset of frames; computing in the processor a covariance matrix for each subset of frames; The A' matrix of each subset of frames is calculated in the processor, wherein the A' matrix is the weighted average of the covariance matrices in the subset; the p matrix is calculated in the processor, wherein each element of the p matrix is Corresponding to the first initial vector of the corresponding A' matrix; calculate the inner product of each pair of initial vectors in the ρ matrix, and select the minimum absolute value from the inner product; calculate the A matrix, and the A matrix is the frame The sum of the covariance matrices in the sixth subset of ;
确定所述A矩阵的第一初始值和第二初始值；以及计算所述A矩阵的第一初始值与所述A矩阵的第二初始值的比率。determining a first initial value and a second initial value of the A matrix; and calculating a ratio of the first initial value of the A matrix to the second initial value of the A matrix.
在一个实施方式中，包括：在所述处理器中计算第二条件，所述第二条件是指所述内积中的所述最小绝对值大于第一阈值，且所述第一初始值与所述第二初始值的比率大于第二阈值，其中，如果第二条件为真，则检测到非心肺运动或其他信号干扰的中断。在一个实施方式中，所述第五阈值约在0.6-1之间。在一个实施方式中，所述第六阈值约在4-12之间。在一个实施方式中，所述第一初始向量包括与相应的A′矩阵的最大特征值相应的特征向量。在一个实施方式中，所述第一初始值包括所述A矩阵的最大特征值，且所述第二初始值包括所述A矩阵的第二最大特征值。在一个实施方式中，包括回溯步骤，在所述非心肺运动基本停止时确定来自所述一个或多个帧中的帧。在一个实施方式中，在所述帧之前的一个或多个帧被丢弃。In one embodiment, it includes: calculating a second condition in the processor, the second condition means that the minimum absolute value in the inner product is greater than a first threshold, and the first initial value and The ratio of the second initial value is greater than a second threshold, wherein a non-cardiopulmonary or other signal disturbance interruption is detected if the second condition is true. In one embodiment, the fifth threshold is approximately between 0.6-1. In one embodiment, the sixth threshold is approximately between 4-12. In one embodiment, the first initial vector includes an eigenvector corresponding to the largest eigenvalue of the corresponding A' matrix. In one embodiment, the first initial value includes the largest eigenvalue of the A matrix, and the second initial value includes the second largest eigenvalue of the A matrix. In one embodiment, a step of backtracking is included to determine a frame from said one or more frames when said non-cardiopulmonary exercise substantially ceases. In one embodiment, one or more frames preceding said frame are discarded.
一个实施方式包括一种利用运动传感器估算生理运动的方法，所述方法包括：辐射源生成电磁辐射，其中所述电磁辐射的频率在无线电频率范围内；利用一个或多个发射器向主体发射所述电磁辐射；利用一个或多个接收器接收至少被所述主体散射的辐射；从所散射的辐射中提取多普勒频移信号；将所述多普勒频移信号转换并数字化为数字化运动信号，所述数字化运动信号包括一个或多个帧，其中所述一个或多个帧包括所述数字化运动信号的时间采样正交值；所述处理器执行非心肺运动检测算法，以从所述数字化运动信号中识别出与非心肺运动或其他信号干扰相应的一个或多个非心肺运动或其他信号干扰事件；处理器执行速率估算算法以估算所述生理运动的速率；以及向输出单元提供至少与所述主体的生理运动的速率相关或与所述主体的部分的生理运动的速率相关的信息，所述输出单元用于输出所述信息。One embodiment includes a method of estimating physiological motion using a motion sensor, the method comprising: generating electromagnetic radiation from a radiation source, wherein the frequency of the electromagnetic radiation is in the radio frequency range; transmitting the electromagnetic radiation to a subject using one or more transmitters receiving the radiation scattered by at least the subject with one or more receivers; extracting a Doppler-shifted signal from the scattered radiation; converting and digitizing the Doppler-shifted signal into digitized motion signal, the digitized motion signal includes one or more frames, wherein the one or more frames include time-sampled quadrature values of the digitized motion signal; the processor executes a non-cardiopulmonary motion detection algorithm to obtain from the identifying one or more non-cardiopulmonary or other signal disturbance events in the digitized motion signal corresponding to the non-cardiopulmonary movement or other signal disturbance; the processor executing a velocity estimation algorithm to estimate the velocity of said physiological movement; and providing at least Information related to the rate of physiological movement of the subject or the rate of physiological movement of a part of the subject, the output unit for outputting the information.
在一个实施方式中，所述速率估算算法包括：从解调的帧中采集多个样本；从所述多个样本中识别与非心肺运动检测事件相应的一个或多个样本，并将来自所述多个样本的所述一个或多个样本设为零，以至少获取所述多个样本的第一子集；以及在所述处理器中从所述第一子集中减去所述第一子集的平均值。在一个实施方式中，包括在所述处理器中对包括在所述第一子集中的样本进行傅里叶变换，以获取所述第一子集中的所述样本最大幅度。在一个实施方式中，所述估算的生理运动的频域速率与所述第一子集的波幅中的最大部分相应。在一个实施方式中，包括：在所述第一子集中识别至少三个正零交叉或至少三个负零交叉；识别用于第一零交叉和第二零交叉中的样本的第一值，所述第一值是指最大幅正值或最大幅负值；识别用于第二零交叉和第三零交叉中的样本的第二值，所述第二值是指最大幅正值或最大幅负值；所述第一值和所述第二值与阈值进行比较；如果所述第一值大于阈值，则至少识别出第一呼吸事件；如果所述第二值大于阈值，则至少识别出第二呼吸事件；以及至少基于所述第一呼吸事件和第二呼吸事件以及第一呼吸事件、第二呼吸事件以及第三零交叉之间的时间间隔估算时域呼吸速率。在一个实施方式中，包括：在所述处理器中对所述第一子集中包括的样本进行傅里叶转换，以获取所述第一子集中的所述样本的最大幅度；比较时域率和频域率以调整所述时域率和所述频域率的准确度。In one embodiment, the rate estimation algorithm includes: collecting a plurality of samples from the demodulated frame; identifying from the plurality of samples one or more samples corresponding to a non-cardiopulmonary motion detection event, and setting said one or more samples of said plurality of samples to zero to obtain at least a first subset of said plurality of samples; and subtracting said first subset from said first subset in said processor average of the subset. In one embodiment, comprising performing Fourier transform in the processor on the samples included in the first subset to obtain the maximum magnitude of the samples in the first subset. In one embodiment, said estimated frequency-domain rate of physiological motion corresponds to a largest fraction of the amplitudes of said first subset. In one embodiment, comprising: identifying at least three positive zero crossings or at least three negative zero crossings in said first subset; identifying first values for samples in the first zero crossing and the second zero crossing, The first value refers to a maximum magnitude value or a maximum magnitude value; a second value is identified for samples in the second zero crossing and the third zero crossing, the second value refers to a maximum magnitude value or a maximum magnitude value a substantially negative value; said first value and said second value are compared to a threshold; if said first value is greater than a threshold, at least a first respiratory event is identified; if said second value is greater than a threshold, at least generating a second respiratory event; and estimating a time-domain respiration rate based at least on the first and second respiration events and a time interval between the first respiration event, the second respiration event, and the third zero crossing. In one embodiment, it includes: performing Fourier transform on the samples included in the first subset in the processor, so as to obtain the maximum amplitude of the samples in the first subset; comparing the time domain rate and the frequency domain rate to adjust the accuracy of the time domain rate and the frequency domain rate.
在一个实施方式中，速率估算算法包括：从所述多个样本中识别至少三个连续峰，从而使两个连续峰之间包括谷；以及基于检测的连续峰的数量和第一峰与最末峰之间的时间间隔来确定呼吸速率。In one embodiment, the rate estimation algorithm comprises: identifying at least three consecutive peaks from the plurality of samples such that a valley is included between two consecutive peaks; The time interval between peaks is used to determine the respiration rate.
在一个实施方式中，速率估算算法包括：从所述多个样本中识别至少三个连续谷，从而使两个连续谷之间包括峰；以及基于检测的连续谷的数量和第一谷与最末谷之间的时间间隔来确定呼吸速率。在一个实施方式中，所述速率估算算法基于峰先出现还是谷先出现来选择识别峰还是识别谷。在一个实施方式中，所述速率估算算法对基于连续峰数量的呼吸速率和基于连续谷数量的呼吸速率求平均值，以提高速率估计的鲁棒性。In one embodiment, the rate estimation algorithm comprises: identifying at least three consecutive valleys from said plurality of samples such that a peak is included between two consecutive valleys; The time interval between the last troughs was used to determine the respiration rate. In one embodiment, the rate estimation algorithm chooses whether to identify a peak or a valley based on whether the peak comes first or the valley comes first. In one embodiment, the rate estimation algorithm averages the respiration rate based on the number of consecutive peaks and the respiration rate based on the number of consecutive troughs to increase the robustness of the rate estimate.
一个实施方式包括一种用于感测生理运动的系统，所述系统包括：一个或多个天线，发送电磁辐射；一个或多个天线，接收电磁辐射；至少一个处理器，通过执行解调算法、非心肺运动检测算法、速率估算算法、反常呼吸算法以及波达方向算法中至少一个来提取与心肺运动相关的信息；以及通信系统，与输出装置进行通信，所述输出装置输出与所述心肺运动相关的信息。在一个实施方式中，所述生命体征监测器至少监测一个或多个主体的呼吸速率、心率、呼吸深度、呼吸波形、心率波形、一次换气量活动或异步呼吸程度。在一个实施方式中，所述呼吸暂停检测系统至少监测一个或多个主体的呼吸速率、呼吸努力、心率、呼吸深度、一次换气量、反常呼吸、活动、或位置，还用于检测一个或多个主体的呼吸的存在或不存在。在一个实施方式中，睡眠监测器至少监测一个或多个主体的呼吸速率、心率、呼吸深度、一次换气量、反常呼吸或生理运动。在一个实施方式中，生命体征测量系统至少测量一个或多个主体的测量呼吸速率、心率、吸气时间与呼气时间的比率、一次换气量或呼吸深度。在一个实施方式中，所述生命体征测量系统及时地在一点或间歇点执行测量。One embodiment includes a system for sensing physiological motion, the system comprising: one or more antennas, transmitting electromagnetic radiation; one or more antennas, receiving electromagnetic radiation; at least one processor, executing a demodulation algorithm , a non-cardiopulmonary motion detection algorithm, a rate estimation algorithm, an abnormal breathing algorithm, and a direction of arrival algorithm to extract information related to cardiopulmonary exercise; and a communication system that communicates with an output device that outputs information related to the cardiopulmonary Sports related information. In one embodiment, the vital sign monitor monitors at least one or more subjects' respiration rate, heart rate, respiration depth, respiration waveform, heart rate waveform, tidal volume activity, or degree of asynchronous respiration. In one embodiment, the apnea detection system monitors at least one or more subjects' respiration rate, respiration effort, heart rate, respiration depth, tidal volume, abnormal respiration, activity, or position, and is configured to detect one or more Presence or absence of breath of multiple subjects. In one embodiment, the sleep monitor monitors at least one or more subjects' breathing rate, heart rate, breathing depth, ventilation volume, abnormal breathing, or physiological movement. In one embodiment, the vital sign measurement system measures at least one or more subjects' measured respiration rate, heart rate, ratio of inspiratory time to expiratory time, ventilation volume, or respiration depth. In one embodiment, the vital sign measurement system performs measurements at one or intermittent points in time.
一个实施方式包括一种心理-生理状态监测器，所述心理-生理状态监测器至少监测一个或多个主体响应于一个或多个外部刺激的呼吸速率、呼吸波形、心率波形、活动、心率、呼吸深度、一次换气量、吸气时间、呼气时间或吸气时间与呼气时间的比率。One embodiment includes a psycho-physiological state monitor that monitors at least one or more subjects' breathing rate, breathing waveform, heart rate waveform, activity, heart rate, Respiration depth, tidal volume, inspiratory time, expiratory time, or the ratio of inspiratory time to expiratory time.
在一个实施方式中，所述系统将信息发送至成像系统，所述成像系统将所述主体成像，所述信息使所述成像系统与所述主体的生理运动同步。In one embodiment, the system sends information to an imaging system that images the subject, the information synchronizing the imaging system with physiological movements of the subject.
在一个实施方式中，所述系统将信息发送至医疗装置，所述信息用于操作所述医疗装置。在一个实施方式中，所述医疗装置包括除颤器。在一个实施方式中，所述系统至少获取呼吸运动的存在或不存在，或者心跳运动的存在或不存在。In one embodiment, the system sends information to a medical device, the information being used to operate the medical device. In one embodiment, the medical device includes a defibrillator. In one embodiment, the system captures at least the presence or absence of respiratory motion, or the presence or absence of heartbeat motion.
一个实施方式包括一种生理活动监测器，至少监测一个或多个主体的呼吸速率、心率、呼吸深度、一次换气量、非心肺运动的频率或非心肺运动的持续时间。One embodiment includes a physiological activity monitor that monitors at least one or more subjects' breathing rate, heart rate, breathing depth, ventilation volume, frequency of non-cardiopulmonary exercise, or duration of non-cardiopulmonary exercise.
在一个实施方式中，加权和为算数平均数。In one embodiment, the weighted sum is an arithmetic mean.
在一个实施方式中，医疗装置包括除颤器。In one embodiment, the medical device includes a defibrillator.
一个实施方式包括一种利用运动传感器估算反常呼吸是否存在的方法，所述方法包括：辐射源生成电磁辐射，其中所述电磁辐射的频率在无线频率范围内；利用一个或多个发射器向主体发射所述电磁辐射；利用一个或多个接收器接收至少被所述主体散射的辐射；从所散射的辐射中提取多普勒频移信号；将所述多普勒频移信号转换成数字化运动信号，所述数字化运动信号包括一个或多个帧，其中所述一个或多个帧包括所述数字化运动信号的时间采样正交值；由处理器执行非心肺运动检测算法，以从数字化的信号中识别出与非心肺运动或其他信号干扰的存在或不存在相应的一个或多个非心肺运动检测事件或其他信号干扰事件；处理器执行反常呼吸识别算法以估算反常呼吸的存在或不存在；以及至少提供反常呼吸存在、不存在或反常呼吸程度信息。在一个实施方式中，反常呼吸识别算法包括：选择帧的子集；利用低通滤波器过滤所述帧；以及获取所过滤的帧的复系数星座曲线图。One embodiment includes a method of estimating the presence of abnormal breathing using a motion sensor, the method comprising: generating electromagnetic radiation from a radiation source, wherein the frequency of the electromagnetic radiation is in the radio frequency range; transmitting said electromagnetic radiation; receiving radiation scattered by at least said subject with one or more receivers; extracting a Doppler-shifted signal from the scattered radiation; converting said Doppler-shifted signal into digitized motion signal, the digitized motion signal includes one or more frames, wherein the one or more frames include time-sampled quadrature values of the digitized motion signal; and a non-cardiopulmonary motion detection algorithm is executed by a processor to extract from the digitized signal identifying one or more non-cardiopulmonary motion detection events or other signal interference events corresponding to the presence or absence of non-cardiopulmonary motion or other signal interference; the processor executes an abnormal breathing identification algorithm to estimate the presence or absence of abnormal breathing; And at least providing information on the presence, absence or degree of abnormal breathing. In one embodiment, the abnormal breathing identification algorithm includes: selecting a subset of frames; filtering the frames using a low-pass filter; and obtaining a complex coefficient constellation of the filtered frames.
在一个实施方式中，如果所述复系数星座曲线图近似线性，使得所述复系数星座曲线图的第一面积大于所述复系数星座曲线图的第二面积，则检测到反常呼吸不存在。In one embodiment, the absence of abnormal breathing is detected if the complex coefficient constellation is approximately linear such that a first area of the complex coefficient constellation is greater than a second area of the complex coefficient constellation.
在一个实施方式中，如果所述复系数星座曲线图具有第一面积和第二面积，使得所述第一面积和第二面积相似，则检测到反常呼吸的存在。In one embodiment, the presence of abnormal breathing is detected if the complex coefficient constellation has a first area and a second area such that the first area and the second area are similar.
在一个实施方式中，计算奇异因子来估算反常呼吸的程度。在一个实施方式中，所述奇异因子可通过下述步骤进行估算：在处理器中计算所述子集的协方差矩阵；计算所述协方差矩阵的第一初始值和第二初始值；计算与所述第一初始值相应的第一初始向量以及与第二初始值相应的第二初始向量；将所述信号投射在所述第一初始向量上，并确定与映射在所述第一初始向量上的信号的最大峰-峰值相应的第一幅度；将所述信号投射在所述第二初始向量上，并确定与映射在所述第二初始向量上的信号的最大峰-峰值相应的第二幅度；计算所述第一幅度与所述第二幅度的第一比率；计算所述第一初始值与所述第二初始值的第二比率；以及计算所述第一比率与所述第二比率的积。在一个实施方式中，所述第一初始值和第二初始值包括所述协方差矩阵的特征值，且所述第一初始向量和第二初始向量包括与所述第一初始值和第二初始值相应的特征向量。In one embodiment, a singularity factor is calculated to estimate the degree of abnormal breathing. In one embodiment, the singularity factor can be estimated by the following steps: calculating the covariance matrix of the subset in the processor; calculating the first initial value and the second initial value of the covariance matrix; calculating a first initial vector corresponding to the first initial value and a second initial vector corresponding to the second initial value; projecting the signal onto the first initial vector, and determining and mapping on the first initial The first amplitude corresponding to the maximum peak-to-peak value of the signal on the vector; project the signal on the second initial vector, and determine the maximum peak-to-peak value corresponding to the signal mapped on the second initial vector second magnitude; calculating a first ratio of the first magnitude to the second magnitude; computing a second ratio of the first initial value to the second initial value; and computing the first ratio to the The product of the second ratio. In one embodiment, the first initial value and the second initial value include the eigenvalues of the covariance matrix, and the first initial vector and the second initial vector include the same The eigenvector corresponding to the initial value.
在一个实施方式中，利用在奇异因子上执行价值函数来计算反常指标。在一个实施方式中，通过比较价值函数的输出与阈值来确定反常呼吸是否存在。In one embodiment, the anomaly index is calculated using a cost function performed on the singularity factors. In one embodiment, the presence or absence of abnormal breathing is determined by comparing the output of the value function to a threshold.
在一个实施方式中，分析所述反常呼吸指标以提供指示反常呼吸不存在的第一指示，只是不确定结果的第二指示以及指示反常呼吸存在的第三指示。In one embodiment, the abnormal breathing indicator is analyzed to provide a first indication indicating the absence of abnormal breathing, a second indication of only an inconclusive result and a third indication indicating the presence of abnormal breathing.
一个实施方式包括一种利用运动传感器估算波达方向的方法，所述方法包括：辐射源生成电磁辐射，其中所述电磁辐射的频率在无线频率范围内；利用一个或多个发射器向主体发射所述电磁辐射；利用一个或多个接收器接收至少被所述主体散射的辐射；从所散射的辐射中提取多普勒频移信号；将所述多普勒频移信号转换成数字化运动信号，所述数字化运动信号包括一个或多个帧，其中所述一个或多个帧包括来自每个接收器的所述数字化运动信号的时间采样正交值；处理器执行波达方向算法以估算目标的数量和相应的角度；以及至少将与一个或多个主体的心肺运动、或与一个或多个主体的一部分的心肺运动、主体的数量或一个或多个主体的方向相关的信息提供至输出单元，所述输出单元输出所述信息。在一个实施方式中，所述波达方向算法包括：利用低通滤波器对选自所述一个或多个帧的帧的子集进行过滤，每个帧都包括来自所述多个接收天线阵列中的多个接收通道的信号；对低通滤波的帧的子集计算全部通道的功率谱密度；利用所计算的功率谱密度中频率分量的能量来确定最可能包含来自一个或多个主体的心肺信号的频率分量；识别每个频率分量的角度方向；至少识别第一角度方向和第二角度方向，从而使每个角度方向以大于或等于所述一个或多个接收器的角分辨率的角距离与其他角度方向分离；将以小于所述一个或多个接收器的角分辨率的角距离与其他角度方向分离的一个或多个角去除；以及对所述角方向中的每个目标生成一个或多个具有单位模值的DOA向量；以及采用当前DOA向量的加权平均数和缓存中的先前的DOA向量对DOA向量进行平滑处理。一个实施方式进一步包括通过将空间上的零位导向其它角方向来分离来自每个角方向的信号；处理器执行非心肺运动检测算法，以检测每个分离的信号中是否存在非心肺运动或其他信号干扰；以及如果检测到非心肺运动不存在，则处理器执行解调算法以对所分离的全部信号进行解调，并处理所解调的信号，以获取与所述心肺运动相应的信息。一个实施方式进一步包括通过将空间上的零位导向其它角方向来隔离来自所期望主体的信号；所述处理器执行非心肺运动检测算法，以检测在所隔离的信号中是否存在非心肺运动或其他信号干扰；以及如果检测到不存在非心肺运动，则所述处理器质性解调算法来解调所隔离的信号，并处理所隔离的信号，以获取与所述主体的心肺运动相关的信息。One embodiment includes a method of estimating a direction of arrival using a motion sensor, the method comprising: generating electromagnetic radiation from a radiation source, wherein the frequency of the electromagnetic radiation is in the radio frequency range; transmitting to a subject using one or more transmitters the electromagnetic radiation; receiving radiation scattered by at least the subject with one or more receivers; extracting a Doppler-shifted signal from the scattered radiation; converting the Doppler-shifted signal into a digitized motion signal , the digitized motion signal includes one or more frames, wherein the one or more frames include time-sampled quadrature values of the digitized motion signal from each receiver; a processor executes a direction-of-arrival algorithm to estimate a target and corresponding angles; and providing at least information related to the cardiorespiratory exercise of the one or more subjects, or to a portion of the cardiorespiratory exercise of the one or more subjects, the number of subjects, or the orientation of the one or more subjects to the output unit, the output unit outputs the information. In one embodiment, the direction-of-arrival algorithm comprises: filtering a subset of frames selected from the one or more frames with a low-pass filter, each frame comprising Signals from multiple receive channels in ; calculate the power spectral density of all channels for a subset of low-pass filtered frames; use the energy of the frequency components in the calculated power spectral density to determine the most likely to contain from one or more subjects frequency components of the cardiopulmonary signal; identifying an angular orientation of each frequency component; identifying at least a first angular orientation and a second angular orientation such that each angular orientation is at a resolution greater than or equal to the angular resolution of the one or more receivers separating angular distances from other angular directions; removing one or more corners separated from other angular directions by an angular distance less than the angular resolution of the one or more receivers; and for each target in the angular direction generating one or more DOA vectors with a unit modulus; and smoothing the DOA vectors using a weighted average of the current DOA vectors and previous DOA vectors in the cache. One embodiment further includes separating signals from each angular direction by directing spatial nulls to other angular directions; the processor executes a non-cardiopulmonary motion detection algorithm to detect the presence of non-cardiopulmonary motion or other signal interference; and if the absence of non-cardiopulmonary exercise is detected, the processor executes a demodulation algorithm to demodulate all of the separated signals and process the demodulated signals to obtain information corresponding to said cardiopulmonary exercise. One embodiment further includes isolating signals from desired subjects by directing spatial nulls to other angular directions; the processor executes a non-cardiopulmonary motion detection algorithm to detect the presence of non-cardiopulmonary motion or non-cardiopulmonary motion in the isolated signal other signal interference; and if the absence of non-cardiopulmonary exercise is detected, the processor qualitative demodulation algorithm to demodulate the isolated signal and process the isolated signal to obtain information related to the subject's cardiopulmonary exercise information.
在一个实施方式中，所述波达方向算法包括：利用低通滤波器对选自所述一个或多个帧的帧的子集进行过滤，每个帧都包括来自所述多个接收天线阵列中的多个接收通道的信号；对低通滤波的帧的子集计算全部通道的功率谱密度；利用所计算的功率谱密度中频率分量的能量来确定最可能包含来自一个或多个主体的心肺信号的频率分量；识别每个频率分量的角度方向；至少识别第一角度方向和第二角度方向，从而使每个角度方向以大于或等于所述一个或多个接收器的角分辨率的角距离与其他角度方向分离；将以小于所述一个或多个接收器的角分辨率的角距离与其他角度方向分离的一个或多个角去除；以及对所述角方向中的每个目标生成一个或多个具有单位模值的DOA向量；采用当前DOA向量的加权平均数和缓存中的先前的DOA向量对DOA向量进行平滑处理；周期性地重复DOA算法并更新DOA向量；以及将与所述DOA向量相应的角度传送给所述输出单元。In one embodiment, the direction-of-arrival algorithm comprises: filtering a subset of frames selected from the one or more frames with a low-pass filter, each frame comprising Signals from multiple receive channels in ; calculate the power spectral density of all channels for a subset of low-pass filtered frames; use the energy of the frequency components in the calculated power spectral density to determine the most likely to contain from one or more subjects frequency components of the cardiopulmonary signal; identifying an angular orientation of each frequency component; identifying at least a first angular orientation and a second angular orientation such that each angular orientation is at a resolution greater than or equal to the angular resolution of the one or more receivers separating angular distances from other angular directions; removing one or more corners separated from other angular directions by an angular distance less than the angular resolution of the one or more receivers; and for each target in the angular direction Generate one or more DOA vectors with unit modulus; smooth the DOA vectors using the weighted average of the current DOA vectors and the previous DOA vectors in the cache; periodically repeat the DOA algorithm and update the DOA vectors; and The corresponding angle of the DOA vector is transmitted to the output unit.
附图说明Description of drawings
图1A概要地示出包括雷达的生理运动传感器系统的实施方式；Figure 1A schematically illustrates an embodiment of a physiological motion sensor system including radar;
图1B-1F示出由图1A所示的系统得到的测试结果；Figures 1B-1F show test results obtained by the system shown in Figure 1A;
图2概要地示出集成了远程界面的基于雷达的生理运动传感器系统的框图；Figure 2 schematically illustrates a block diagram of a radar-based physiological movement sensor system integrating a remote interface;
图3概要地示出包括基于雷达的生理运动传感器的系统的框图，该传感器包括扩展模块；Figure 3 schematically illustrates a block diagram of a system comprising a radar-based physiological motion sensor including an expansion module;
图4概要地示出与医院网络通信的独立式的基于雷达的传感器装置的框图；Figure 4 schematically illustrates a block diagram of a stand-alone radar-based sensor device in communication with a hospital network;
图5概要地示出具有无线连接的独立式的基于雷达的传感器装置的另一实施方式；FIG. 5 schematically illustrates another embodiment of a self-contained radar-based sensor device with a wireless connection;
图6概要地示出包括处理器和显示器的基于雷达的生理运动传感器的另一实施方式；Figure 6 schematically illustrates another embodiment of a radar-based physiological motion sensor including a processor and a display;
图7概要地示出包括发射器和多个接收器的基于雷达的生理运动传感器的实施方式；Figure 7 schematically illustrates an embodiment of a radar-based physiological motion sensor comprising a transmitter and multiple receivers;
图8示出用于实现直流消除的方法的实施方式的流程图；Figure 8 shows a flowchart of an embodiment of a method for implementing DC cancellation;
图9示出线性解调算法的实施方式；Figure 9 shows an embodiment of a linear demodulation algorithm;
图10A-10D示出包括频域速率估计和时域速率估计的速率估计算法的实施方式；Figures 10A-10D illustrate an embodiment of a rate estimation algorithm comprising frequency domain rate estimation and time domain rate estimation;
图11A和11B示出正常呼吸和反常呼吸的相量图；Figures 11A and 11B show phasor diagrams for normal and abnormal respiration;
图11C示出用于将反常因素转化成反常指示的价值函数的实施方式；Figure 11C illustrates an embodiment of a value function for converting anomaly factors into anomalous indications;
图11D和图11E示出当身体部位同时扩张和收缩时的具有多路径延时信号的基带输出；Figures 11D and 11E show the baseband output with multipath delayed signals when body parts expand and contract simultaneously;
图11F和11G示出当身体部位扩张或收缩具有不同相位延迟时的具有多路径延时信号的基带输出；Figures 11F and 11G show baseband output with multipath delayed signals when body parts expand or contract with different phase delays;
图12A到12D示出用于探测非心肺运动的方法的实施方式；12A to 12D illustrate an embodiment of a method for detecting non-cardiopulmonary motion;
图13概要地示出自测电路的实施方式的框图；Figure 13 schematically shows a block diagram of an embodiment of a self-test circuit;
图14(由图14A和14B组成)示出用于分离多个心肺信号的方法的实施方式；Figure 14 (consisting of Figures 14A and 14B) illustrates an embodiment of a method for separating multiple cardiopulmonary signals;
图15示出将来自两个目标的呼吸信号分离的测试；Figure 15 shows a test to separate the respiration signals from two targets;
图16(由图16A和图16B组成)示出用于跟踪一个或多个心肺信号方向的算法的实施方式；16 (consisting of FIGS. 16A and 16B ) illustrates an embodiment of an algorithm for tracking the direction of one or more cardiopulmonary signals;
图17示出基于雷达的生理运动传感器系统的可选的实施方式；Figure 17 illustrates an alternative embodiment of a radar-based physiological motion sensor system;
图18示出包括传感器单元、计算单元和显示单元的基于雷达的生理运动传感器的实施方式；Figure 18 shows an embodiment of a radar-based physiological motion sensor comprising a sensor unit, a calculation unit and a display unit;
图19示出用于输出心肺或心血管相关信息的界面(例如显示屏)的实施方式；Figure 19 shows an embodiment of an interface (e.g., a display screen) for outputting cardiopulmonary or cardiovascular related information;
图20示出展示出呼吸速率的显示装置的屏幕截图；Figure 20 shows a screenshot of a display device showing respiration rate;
图21示出包括传感器单元、计算单元和显示单元的基于雷达的生理运动传感器的可选的实施方式；Figure 21 shows an alternative embodiment of a radar-based physiological motion sensor comprising a sensor unit, a calculation unit and a display unit;
图22示出包括传感器单元和处理器的基于雷达的生理运动传感器的可选的实施方式；Figure 22 shows an alternative embodiment of a radar-based physiological motion sensor comprising a sensor unit and a processor;
图23示出用于除显示其他信息之外还显示呼吸信号和心脏信号的显示装置的实施方式的屏幕截图；Figure 23 shows a screenshot of an embodiment of a display device for displaying respiratory and cardiac signals in addition to other information;
图24是示出呼吸速率、活动指示器和睡眠主体的位置的显示装置或单元的屏幕截图；Figure 24 is a screen shot of a display device or unit showing breathing rate, activity indicator, and position of a sleeping subject;
图25A示出系统在医院环境中用以测量患者的呼吸和/或心脏活动的应用；Figure 25A shows the application of the system to measure the breathing and/or cardiac activity of a patient in a hospital environment;
图25B是图25A所示的显示装置的屏幕截图；Figure 25B is a screenshot of the display device shown in Figure 25A;
图26A和图26B示出可用于观察由装置提供的生命体征的显示装置的屏幕截图；26A and 26B illustrate screen shots of a display device that may be used to view vital signs provided by the device;
图27示出直流消除电路的实施方式；Figure 27 shows an embodiment of a DC cancellation circuit;
图28示出用于确定反常呼吸指示的方法的实施方式；Figure 28 illustrates an embodiment of a method for determining an indication of abnormal breathing;
图29和图30是用于显示探测反常呼吸的系统的输出的显示装置的屏幕截图；29 and 30 are screenshots of a display device for displaying the output of the system for detecting abnormal breathing;
图31示出包括密距型天线阵的系统的实施方式；Figure 31 shows an embodiment of a system including a close-pitch antenna array;
图32示出包括两个接收天线的系统的实施方式；Figure 32 shows an embodiment of a system comprising two receive antennas;
图33示出显示装置的屏幕截图，该显示装置用于输出对已分离的两人的呼吸信号进行DOA处理之后的心肺信息；Fig. 33 shows a screenshot of a display device for outputting cardiopulmonary information after DOA processing of respiratory signals of two people who have been separated;
图34示出用于显示呼吸波形和一次换气量的显示装置的屏幕截图；Figure 34 shows a screenshot of a display device for displaying a respiratory waveform and a tidal volume;
图35示出用于显示两个人的呼吸运动波形的显示装置的屏幕截图；Figure 35 shows a screen shot of a display device for displaying respiratory motion waveforms of two persons;
图36A示出信号的正交相分量和同相分量的复系数星座图；Fig. 36A shows the complex coefficient constellation diagram of the quadrature-phase component and the in-phase component of the signal;
图36B示出通过基于雷达的生理运动传感器和传统的运动传感器例如胸带而测量的呼吸深度对比时间曲线图；Figure 36B shows a graph of breathing depth versus time as measured by a radar-based physiological motion sensor and a traditional motion sensor such as a chest strap;
图36C示出显示一次换气量、对应于呼吸活动波形以及呼吸速率的显示装置的快照；Figure 36C shows a snapshot of a display device showing a tidal volume, a waveform corresponding to respiratory activity, and a respiratory rate;
图37示出包括发射天线和至少四个接收天线的阵列元件布局的示意图；Figure 37 shows a schematic diagram of an array element layout comprising a transmit antenna and at least four receive antennas;
图38A-38C示出由与主体相连的穿戴式多普勒雷达系统测量的心肺活动的相关信息；38A-38C illustrate information related to cardiorespiratory activity measured by a wearable Doppler radar system coupled to a subject;
图38D示出由非接触式多普勒雷达系统测量的心肺活动的相关信息；Figure 38D shows information related to cardiorespiratory activity measured by a non-contact Doppler radar system;
图38E-38J示出用于显示与心肺活动相关的测量和指示个体存在的测量的显示装置的实施方式。38E-38J illustrate an embodiment of a display device for displaying measurements related to cardiorespiratory activity and measurements indicative of the presence of an individual.
图39A和39B描述了包括基于雷达的生理运动传感器的多个群的网络拓扑结构实施方式。39A and 39B depict a network topology embodiment comprising multiple clusters of radar-based physiological motion sensors.
具体实施方式Detailed ways
图1A示出生理运动传感器系统100，其中雷达101检测主体102的运动和/或生理活动。将来自雷达101的数据提供到处理系统103，该处理系统分析雷达数据以确定多种期望的生理参数，并将关于生理参数的输出信息提供到用于执行输出动作的输出系统或装置。在多种实施方式中，输出装置可包括显示系统或医学装置，该显示系统用于显示报告信息或发出警报的发声系统，该医学装置基于该信息执行功能。系统100还可包括利用有线或无线的通信链路进行通信的通信系统。通信系统可使用标准或专有协议。图1B示出显示在显示单元上的系统100获取的测量结果示例。FIG. 1A shows a physiological
图1B-1F示出系统100获取的测量的示例。测量可包括由于主体102的心肺活动而显示在显示单元上的波形。1B-1F illustrate examples of measurements taken by
图1B示出上述系统100的实施方式用于体质指数(BMI)为23、患有高血压和充血性心力衰竭的54岁男性主体所获取的波形。图1B的曲线图104示出由基于雷达的生理运动传感器系统探测的生理运动信号(例如呼吸速率和呼吸波幅)。曲线图105示出由传统的接触式生理运动传感器(例如胸带)探测的生理运动信号。曲线图106示出基于雷达的生理运动传感器和标准的传统传感器探测的标准运动信号之间的对比。曲线图106示出两种信号之间良好的一致性。FIG. 1B shows waveforms acquired by an embodiment of the
图1C示出上述的系统的实施方式用于体质指数为40、患有糖尿病、高血压、冠状动脉性心脏病的44岁男性所获取的呼吸速率和呼吸波幅的变化。图1C的曲线图107示出由基于雷达的生理运动传感器系统探测的生理运动信号(例如呼吸速率和呼吸幅度)。曲线图108示出由传统的接触式生理运动传感器(例如胸带)探测的生理运动信号。曲线图109示出基于雷达的生理运动传感器和标准的传统传感器探测的标准运动信号之间的对比。如之前所观察到的，曲线图109示出两种信号之间良好的一致性。FIG. 1C shows the changes in respiratory rate and amplitude acquired by an embodiment of the system described above for a 44-year-old male with a body mass index of 40, diabetes, hypertension, and coronary heart disease.
图1D示出体质指数(BMI)为40、患有高胆固醇、高血压、冠状动脉性心脏病的55岁男性打鼾时的生理运动信号。曲线图110示出由基于雷达的生理运动传感器探测的运动信号，并示出对呼吸暂停(呼吸的停止)以及呼吸信号基线上的变化的检测。曲线图111是由传统的监测器获取的对应的测量结果，而曲线图112示出传统监测器和系统100之间的对比。FIG. 1D shows the physiological motion signal of a 55-year-old male with a body mass index (BMI) of 40, high cholesterol, high blood pressure, and coronary heart disease while snoring.
图1E示出体质指数为30、患有慢性阻塞性肺病和充血性心力衰竭的59岁女性的生理运动信号。曲线图113示出由系统100的生理运动传感器获取的测量结果。曲线图114示出由传统的传感器获取的对应的测量结果，而曲线图115示出两种测量之间的对比。Figure 1E shows the physiological motor signal of a 59-year-old female with a body mass index of 30, chronic obstructive pulmonary disease and congestive heart failure.
图1F示出体质指数为38、患有充血性心力衰竭和冠状动脉性心脏病的57岁女性的生理运动信号。曲线图116示出对主体的呼吸暂停(呼吸的停止)和在呼吸信号基线上的变化的检波。曲线图117示出由传统的传感器获取的对应的测量结果，而曲线图118展示出两种测量结果之间的对比。FIG. 1F shows the physiological motion signal of a 57-year-old female with a body mass index of 38, congestive heart failure and coronary heart disease.
在多种实施方式中，基于雷达的生理传感器可包括用户界面以允许用户输入信息，或允许用户输入命令和/或指令。在多种实施方式中，用户界面可包括启动按钮和停止按钮，如在此引入其全部内容作为参考的美国临时申请第61/128,743号中公开的所述启动和停止按钮。在多种实施方式中，用户界面可包括清除按钮。在多种实施方式中，用户界面可包括附加按钮(例如保存按钮、打印按钮等)或键盘。In various implementations, a radar-based physiological sensor may include a user interface to allow a user to input information, or to allow a user to input commands and/or instructions. In various embodiments, the user interface may include a start button and a stop button, such as those disclosed in US Provisional Application No. 61/128,743, the entire contents of which are incorporated herein by reference. In various implementations, the user interface can include a clear button. In various implementations, the user interface may include additional buttons (eg, a save button, a print button, etc.) or a keypad.
在多种实施方式中，系统100可与远程显示器和/或中央服务器或电脑通信。在某些实施方式中，简单对象访问协议(SOAP)网络服务与服务器交流数据。如在此引入其全部内容作为参考的美国临时申请第61/072,983号中所公开的，具有浏览器的远程客户端和互联网连接从服务器访问呼吸数据。图2示出集成了远程界面200的系统的框图。图2所示的系统包括与信号处理器202电气通信的基于雷达的生理传感器201。来自信号处理器的信息可显示在本地显示器203上，或通过网络服务204可存储在服务器205上。远程客户端207可通过使用互联网206或某些其他通信协议访问存储在服务器上的信息。In various implementations, the
在多种实施方式中，系统100可包括如在此引入其全部内容作为参考的美国临时申请第61/125,022号中所公开的具有无线连接的附加模块。图3示出包括具有附加模块的基于雷达的生理传感器的系统300的框图。如图3所示，通过使用个人局域网技术，例如蓝牙、超宽带、无线USB等，装置301与患者监护系统302联网。患者监护系统302可在其本地界面显示心肺运动信息，和/或将数据通过互联网304或医院网络303发送到远程数据库以使数据可由远程客户端305访问。In various embodiments,
图4示出用于与医院网络通信的独立式装置的框图。图4所示的系统400包括基于雷达的生理传感器系统401，类似于上述的包括数字信号处理器的系统100。系统401与接入点403无线通信。基于雷达的生理传感器系统401可与远程服务器交换与生理或心肺运动相关的信息，该远程服务器利用如蓝牙、无线USB等的无线通信技术经由接入点403与医院网络404连接。接入点403可通过有线或无线网络与医院网络404(例如医院局域网)连接。本地客户端402或405可无线地或通过医院网络404访问来自系统401或服务器的信息。远程客户端407还可通过互联网406访问信息。在多种实施方式中，通过互联网406可将来自系统403的信息发送至保留电子健康记录的中央数据库408。Figure 4 shows a block diagram of a stand-alone device for communicating with a hospital network. The
系统100的多种实施方式可使用传输控制协议/因特网互联协议(TCP/IP)通过以太网连接或利用串行RS-232连接来通信。图5示出独立式装置500的另一种实施方式，其具有如在此引入其全部内容作为参考的美国临时申请第61/125,022号中所公开的无线连接。与上述系统100类似的雷达系统501可使用任何一种无线技术与中央保健医生站、患者信息数据库和/或电子健康记录505连接。网络可用于通过互联网503发送数据，或在在个人电脑的、PDA的或远程客户端504的医疗写板上显示数据。在医院设置中，系统501可使用如802.11的通信协议或任何其他的医院为联网而使用的通信协议。如果系统501用于家庭场景或户外场景，3G手机或WiMax连接可用于替代局域网技术，以通过互联网503将数据发送到电子健康记录505或远程客户端504或其他数据库。在多种实施方式中，由系统501发送的信息可由保健医生查看。Various implementations of
在多种实施方式中，通过以下方案还使装置501符合由康体佳(Continua)健康联盟制定的标准，从而使装置使用蓝牙或USB与管理计算机连接，管理计算机将数据传播到医疗保健提供商的网络进行存储或检验。In various embodiments, the
图6示出包括类似于上述系统100的生理运动传感器601的系统600，其与包括控制台显示器603的计算机通信。在某些实施方式中，计算机603可与外部显示器602通信。在某些实施方式中，计算机603可与外部显示器602通信。在某些实施方式中，传感器601可将关于生理运动的信息传递到计算机进行存储和/或显示。远程客户端能够通过互联网访问电脑的信息。FIG. 6 shows a
在此所述的生理运动传感器系统100的多种实施方式可被用作持续监视装置和系统。系统100的多种实施方式可被用于测量从数米到接触身体的点的距离范围之内的心肺运动。系统100的多种实施方式提供生理波形、生理变化的显示、生理变化的历史曲线图、信号质量指示和/或特定条件的指示。多种实施方式可包括生理波形，其中包括呼吸波形、心跳波形和/或脉冲波形。多种实施方式可包括生理变化，其中生理变化包括呼吸速率、心跳速率、一次换气量、呼吸深度、吸气时间、呼气时间、吸气时间与呼气时间比率、气流速率、心搏间隔和/或心率变异性。多种实施方式可包括信号质量的指示，一般为例如好的质量或次的质量，或者具体包括低信号功率、信号干扰、非心肺运动或电路噪声。特定条件的指示可包括一般的健康指示、在正常范围之外的生理变化警报、异常型呼吸的指示，或反常呼吸的指示。Various embodiments of the physiological
如图21所示，在多种实施方式中，连续生命体征监测器可具有包括按钮和显示器的本地界面，并可与中央监视场所(例如中央护士站)或中央数据库(例如电子健康记录)电气通信。在多种实施方式中，系统100可以是独立的装置，或者可以是与其他生命体征监视装置(例如医院监视系统)结合的模块。如在此引入其全部内容作为参考的美国临时申请第61/154,176号中所公开的，连续生命体征监测器的多种实施方式在医院或诊所可用于监视一般患者、监视手术后的患者、监视采用了止痛药的患者(止痛药易使患者产生呼吸抑制)、监视患有呼吸系统疾病或失调的患者、监视使用有创呼吸机或无创呼吸机的患者以及在医疗成像扫描过程中的患者。连续的生命体征监视系统100的多种实施方式可用于医院的儿科和/或新生儿病房。As shown in Figure 21, in various embodiments, a continuous vital signs monitor can have a local interface including buttons and a display, and can be electronically connected to a central monitoring location (such as a central nursing station) or a central database (such as an electronic health record). communication. In various implementations, the
如在此引入其全部内容作为参考的美国临时申请第61/072,983号和第61/196,762号中所公开的，连续的生命体征监测器的多种实施方式可用于家庭。该装置的多种实施方式可本地操作、远程操作或二者皆可。该装置的多种实施方式可连接到其它装置，包括但不限于个人健康系统、其他医疗保健装置、个人电脑、移动电话、机顶盒或数据聚合器(data aggregator)。该装置的多种实施方式可通过有线或无线连接与远程位置(远离家)的中央站连接。在多种实施方式中，系统100可具有本地显示器，在显示器上显示获取的部分或全部数据。在多种实施方式中，系统100可与家庭的其他装置通信，和/或通过有线或无线连接与远程的(例如远离家的)中央数据库通信。在多种实施方式中，该装置可由本地控制操作，可由通过有线或无线连接的另一个装置控制，可自动运行，或可由远程的(例如远离家的)中央系统控制。在多种实施方式中，该家用装置可用于一般的生命体征监视，或用于监视影响心肺系统的慢性病，包括但不限于，糖尿病、慢性阻塞性肺疾病以及充血性心力衰竭。在多种实施方式中，非接触式连续生命体征监测器可以是集成入个人健康系统或其他家用保健装置中的模块，共享个人健康系统或其他家用保健装置的显示器和通信。系统100的多种实施方式可符合康体佳健康联盟的指导方针。Various embodiments of continuous vital signs monitors are available for use in the home as disclosed in US Provisional Application Nos. 61/072,983 and 61/196,762, the entire contents of which are hereby incorporated by reference. Various embodiments of the device can be operated locally, remotely, or both. Various embodiments of the device may be connected to other devices including, but not limited to, personal health systems, other healthcare devices, personal computers, mobile phones, set-top boxes, or data aggregators. Various embodiments of the device may be connected to a central station at a remote location (away from home) via a wired or wireless connection. In various implementations, the
在多种实施方式中，连续生命体征监测器还能以类似于医院监测器的实施方式用于专业理疗养老院。该装置的实施方式可用于一般的老人或病人的生命体征监视，还可用于肺炎的早期探测。连续生命体征监测器的实施方式还可用于紧急救援交通工具(例如救护车、直升机等)以监视紧急运送过程中的患者。系统100的多种实施方式还可确定主体活动的持续时间或主体活动时间的百分比。这样的信息可用于提供活动指数。活动指数的改变可用作健康状态改变的指示。在多种实施方式中，如在此引入其全部内容作为参考的美国临时申请第61/001,995号中所公开的，生理运动传感器可用于探测战场上的幸存者并监视其生理信号。在多种实施方式中，处理器可执行的基于阵列拓扑结构的软件可用于多普勒雷达以探测模式搜寻幸存者，并通过聚焦光束以目标模式追踪幸存者。可根据处理双频或倍频的波达方向确定幸存者的位置。In various embodiments, the continuous vital signs monitor can also be used in professional physical therapy nursing homes in an embodiment similar to hospital monitors. Embodiments of the device can be used for general monitoring of vital signs of the elderly or patients, and can also be used for early detection of pneumonia. Embodiments of the continuous vital signs monitor may also be used in emergency vehicles (eg, ambulances, helicopters, etc.) to monitor patients during emergency transport. Various embodiments of the
如下文更详细地说明，系统100可包括用于计算呼吸速率、呼吸速率的精度的算法，和识别失真数据、识别干扰运动、识别电子信号干扰、识别电子噪声、报告变化率、分析呼吸速率的规律性或不规律性以及在呼吸速率过高或过低时向用户发送信号或警告用户等的算法。As explained in more detail below, the
如下文更详细地说明，系统100可包括处理器可执行的硬件和/或软件以提高信号质量，例如，射频泄漏消除、直流消除、噪声消除、低中频架构、零差系统平衡等。在此描述的系统100的多种实施方式能够区别心肺运动和其它运动。在系统100的多种实施方式中，用于运动辨别和探测的方法和算法能够增加心肺数据的精确性。在此描述的多种实施方式采用减少事件发生与报告之间的延迟的方法，并且通过直流消除和高速数据采集显示该事件。一般而言，低延时对于其他装置采用报告的事件来启动或触发其他动作的应用来说是重要的。低延时还提高了与其它测量的同步性。其它系统可采用本文描述的多种实施方式产生的呼吸或心跳波形来触发动作。例如，多种实施方式描述基于心脏或呼吸器官位移(respiratory displacement)来触发医学影像(例如通过CT或磁共振成像扫描MRI)和基于自发呼吸努力(spontaneous respiratory effort)来触发辅助通气。本文描述的多种实施方式产生的呼吸或心跳波形可用于提供与其它系统的生理同步。例如，多种实施方式描述的心肺运动或其它运动对医学成像(例如CT扫描或磁共振成像)系统、辅助通气系统、测谎系统、安全检查系统、生物反馈系统、慢性疾病管理系统和运动器材的同步。As described in more detail below,
当主体在附近活动例如上下床时，系统100的多种实施方式可使用波达方向(DOA)的相关算法自动地跟踪主体的生理信号。当主体在附近移动例如上下床时，系统100的多种实施方式可使用DOA的相关算法自动地跟踪主体的位置。当提取心肺运动时，系统100的多种实施方式可消除外部运动从而使读数更精确。系统100的多种实施方式还可使用例如DOA的算法分离并监视或测量两种或多种心肺运动源(例如，可同时报告附近的第二个或多个主体的心肺运动)。系统100的多种实施方式还可使用例如DOA的算法分离且抑制两种或多种心肺运动源(例如，可抑制附近的第二个或多个主体的心肺运动从而仅测量目的主体)。系统100的多种实施方式还包括与DOA配合使用的射频识别(RFID)标签以确保跟踪期望主体。Various embodiments of the
在此描述的多种实施方式可使用多种用于运动补偿的方法，例如经验模式分解(EMD)、运用波达方向(DOA)处理对第二运动源的抑制、盲信号分离(BSS)、独立式成分分析(ICA)，以及接收的高频信号方向上的运动的抑制。Various embodiments described herein may use various methods for motion compensation, such as Empirical Mode Decomposition (EMD), suppression of a second motion source using Direction of Arrival (DOA) processing, Blind Signal Separation (BSS), Independent Component Analysis (ICA), and suppression of motion in the direction of the received high-frequency signal.
系统100的多种实施方式可包括能够正确识别被监视主体的射频识别(RFID)标签。系统100的多种实施方式还适合具有多种尺寸、形状因子，其物理尺寸适合容纳于床头单元、手持单元、PDA、更大医疗系统的一部分的模块等中。系统100的多种实施方式可包括一个或多个输出以便本地或远程查看并控制信息。在多种实施方式中，系统100可以是瘦客户端应用，从而使系统100将包括传感器，数据采集器、通信设备、解调器和处理器，并且输出系统将置于另一个装置中。例如，在某些实施方式中，为网络系统提供系统100，该网络系统中控制和处理集中在传感器网络，且传感器和网络/通信部分在现场并位于主体附近。在某些实施方式中，在某些预定的环境下，例如当探测到室内有人、在设定的时间间隔等，系统100自动启动测量。在多种实施方式中，系统100可用于执行呼吸深度和相对一次换气量或绝对一次换气量的非接触式测量。系统100的多种实施方式可用作心肺和/或活动监测器。Various embodiments of the
在多种实施方式中，系统100可与其它接触式或非接触式医疗监视装置结合，例如脉搏血氧仪、血压计等。在多种实施方式中，系统100可与空气流量传感器和脉搏血氧仪结合以满足类型3家庭睡眠测试的需求。在多种实施方式中，或者单独使用系统100，或者将其与其它装置联合以执行睡眠呼吸暂停探测。在某些实施方式中，系统100可用于测量对特定的刺激例如问题、图像、声音、娱乐、活动、教育的生理反应。在多种实施方式中，系统100可被兽医用作对动物的非接触式心肺监测器。在多种实施方式中，系统100可被研究者用作对动物的非接触式心肺监测器，例如，研究冬眠过程中的生命体征或监视手术后动物。系统100的某些实施方式可被用于鉴别伤员，例如战场伤员鉴别或灾难区域伤员鉴别。系统100的多种实施方式可被用于监视婴儿和新生儿的心脏、心肺、和/或呼吸活动。In various embodiments, the
根据在此所述的多种实施方式，非接触式生理运动传感器可作为连续呼吸监测器来获取呼吸运动的测量。这种连续呼吸监测器可以是独立式的装置，具有自己的显示器、按钮和/或外部通信，也可以是与其它生命体征监视装置或其它医疗装置集成的模块。这种连续呼吸监测器可提供连续的呼吸波形。这种连续呼吸监测器可提供呼吸值的当前值和历史曲线图，呼吸值包括呼吸速率、一次换气量、吸气时间、呼气时间、吸气时间和呼气时间比率、呼吸深度、腹腔偏移和胸腔偏移的比率、和/或气流速率。这种连续呼吸监测器可提供呼吸速率、一次换气量、吸气时间、呼气时间、吸气时间和呼气时间的比率、呼吸深度、腹腔偏移和胸腔偏移的比率、和/或气流速率在不同频段的变化和历史变化的信息。这种连续呼吸监测器可提供反常呼吸的出现和程度、阻塞性呼吸的出现和程度以及困难呼吸的出现和程度的指示和历史指示。这种连续呼吸监测器可提供喘息和叹息的频率、深度和长度信息。这种连续呼吸监测器可提供非心肺运动的频率和持续时间的信息。这种连续呼吸监测器可提供呼吸波形形状变化或呼吸波形的谐波含量变化的信息。连续呼吸监测器系统的多种实施方式包括界面，该界面提供了对于高、低呼吸速率的警报、速率历史、一次换气量历史、关于吸入/呼出间隔的信息、反常呼吸的指示、阻碍性呼吸的指示、主体位置、活动水平/监视，用于区别运动和测量的心肺活动、健康等级(例如高、中、低)以及信号质量等级(例如，当信号过低时警报)。系统100的多种实施方式可提供用于高呼吸速率的警报、低呼吸速率的警报、呼吸速率的高变化性的警报、呼吸速率的低变化性的警报、呼吸模式的不规则的警报、呼吸模式的转变的警报、高吸气时间和呼气时间的比率的警报、低吸气时间和呼气时间的比率的警报以及吸气时间和呼气时间的比率变化的警报。这些警报的阈值可是预先设定的值，可是由用户设置的值，可是以患者的呼吸速率的基线为基础计算所得的值，或是以患者的呼吸速率的基线和患者的速率的历史变化性为基础计算所得的值。According to various embodiments described herein, a non-contact physiological motion sensor can be used as a continuous respiration monitor to obtain measurements of respiration motion. Such a continuous respiration monitor can be a stand-alone device with its own display, buttons and/or external communications, or a module integrated with other vital signs monitoring devices or other medical devices. This continuous respiration monitor provides a continuous respiration waveform. This continuous respiration monitor provides current and historical graphs of respiration values including respiration rate, tidal volume, inspiratory time, expiratory time, ratio of inspiratory time to expiratory time, respiration depth, abdominal cavity Ratio of excursion to thoracic excursion, and/or airflow rate. This continuous respiration monitor provides respiration rate, tidal volume, inspiratory time, expiratory time, ratio of inspiratory time to exhalation time, respiration depth, ratio of abdominal excursion to thoracic excursion, and/or Information on the variation and historical variation of the airflow rate in different frequency bands. This continuous breathing monitor can provide an indication and a historical indication of the occurrence and extent of abnormal breathing, the occurrence and extent of obstructive breathing, and the occurrence and extent of difficult breathing. This continuous breathing monitor provides information on the rate, depth and length of gasps and sighs. This continuous respiration monitor provides information on the frequency and duration of non-cardiopulmonary exercise. This continuous respiration monitor provides information on changes in the shape of the respiration waveform or changes in the harmonic content of the respiration waveform. Various embodiments of the continuous respiration monitor system include an interface that provides alarms for high and low respiration rates, rate history, tidal volume history, information about inhalation/exhalation intervals, indication of abnormal respiration, obstructive Indication of respiration, subject position, activity level/monitoring to differentiate between exercise and measured cardiorespiratory activity, fitness level (e.g. high, medium, low), and signal quality level (e.g. alert when signal is too low). Various embodiments of the
系统100可用于监视主体睡眠的系统中。例如，在某些实施方式中，系统100可提供非接触式方法取代压电式和电感式胸带来测量呼吸努力和/或呼吸速率。在多种实施方式中，系统100可提供非接触式方法取代压电式和电感式胸带来测量身体不同部位的相关运动在呼吸中的区别(例如，作为反常呼吸指示)。在多种实施方式中，生理运动传感器或者可单独使用，或者可与其它装置结合来探测阻塞性睡眠呼吸暂停、中枢性睡眠呼吸暂停或其它睡眠障碍。在多种实施方式中，系统100可与空气流量传感器和/或用于类型3家庭睡眠测试的脉冲血氧仪共同使用。在多种实施方式中，系统100可与无线空气流量传感器和/或用于类型3家庭睡眠测试的无线脉冲血氧仪共同使用，从而使与患者的接触最小。在多种实施方式中，系统100可单独地用作类型4家庭睡眠测试。在多种实施方式中，系统100可单独地用作类型4家庭睡眠测试，其不与主体接触并以一定距离运行。在多种实施方式中，系统100可提供测量睡眠时心肺活动以及肢体和其它身体部分运动的非接触式方法。系统100的多种实施方式可符合康体佳健康联盟的指导方针。在多种实施方式中，系统100可用于婴儿猝死综合症(SIDS)的监视或筛查(如婴儿或新生儿)。系统100的多种实施方式可用于监视婴儿和新生儿的心肺和/或心脏活动。系统100的多种实施方式可用于新生儿、婴幼儿、儿童、成年人和老年人。
在此描述的生理运动传感器的多种实施方式可用于获取呼吸努力(respiratory effort)波形。因此，如在此引入其全部内容作为参考的美国临时申请第61/194,836号中所公开的，该生理运动传感器可被用作家庭睡眠测试的一部分，包括脉冲血氧仪和鼻腔气流传感器以探测中枢性呼吸暂停和阻塞性睡眠呼吸暂停，并区分中枢性呼吸暂停和阻塞性睡眠呼吸暂停。呼吸努力传感器的多种实施方式可用作睡眠实验室中睡眠评估的一部分，或用作家庭使用的睡眠呼吸暂停检查装置的一部分。如在此引入其全部内容作为参考的美国临时申请第61/200,761号中所公开的，呼吸努力信息还可包含关于反常呼吸的程度的信息。在此描述的非接触式生理运动传感器的多种实施方式可用于获取呼吸努力波形、呼吸频率、反常呼吸的指示、活动的指示以及心跳速率。如在此引入其全部内容作为参考的美国临时申请第61/194,836号和第61/200,761号中所公开的，系统100的多种实施方式可用作对阻塞性睡眠呼吸暂停的家庭检查测试。Various embodiments of the physiological motion sensors described herein can be used to acquire respiratory effort waveforms. Thus, as disclosed in U.S. Provisional Application No. 61/194,836, which is hereby incorporated by reference in its entirety, the physiological motion sensor can be used as part of a home sleep test, including pulse oximetry and nasal airflow sensors to detect Central apnea and obstructive sleep apnea, and distinguish between central apnea and obstructive sleep apnea. Various embodiments of the respiratory effort sensor can be used as part of sleep assessment in a sleep laboratory, or as part of a sleep apnea testing device for home use. Breathing effort information may also include information about the degree of abnormal breathing, as disclosed in US Provisional Application No. 61/200,761, the entire contents of which are incorporated herein by reference. Various embodiments of the non-contact physiological motion sensor described herein can be used to acquire respiratory effort waveforms, respiratory rate, indications of abnormal breathing, indications of activity, and heart rate. Various embodiments of the
在此描述的多种实施方式中，能够使用基于雷达的特别是用于测量生理运动的系统来测量呼吸运动而不接触主体，并且可根据生理运动信号得出呼吸运动。除了根据运动探测呼吸速率之外，呼吸运动还可提供呼吸努力的测量，并与设计用于测量呼吸努力的压电式或电感式胸带呼吸努力的测量相似。在多种实施方式中，呼吸努力的测量对于确定是中枢性呼吸暂停还是阻碍性呼吸暂停是必要的。在多种实施方式中，呼吸运动可由在此描述的基于雷达的系统整夜测量而不用考虑主体在床上的位置。In various embodiments described herein, respiratory motion can be measured using a radar-based system in particular for measuring physiological motion without contacting the subject, and respiratory motion can be derived from the physiological motion signal. In addition to motion-based detection of respiration rate, respiration motion provides a measure of respiratory effort similar to that of piezoelectric or inductive chest straps designed to measure respiratory effort. In various embodiments, the measurement of respiratory effort is necessary to determine whether apnea is central or obstructive. In various embodiments, respiratory motion can be measured overnight by the radar-based system described herein regardless of the subject's position on the bed.
在多种实施方式中，生理运动传感器可包括基于雷达的装置，可用于探测反常呼吸(例如，当腹腔收缩时胸腔扩张或胸腔收缩时腹腔扩张)。在大多数情况下，虽然反常呼吸不能指示气道阻塞，但在阻塞性呼吸暂停过程中会显示反常呼吸。在多种实施方式中，反常呼吸的指示和反常呼吸的水平有益于阻碍性呼吸暂停的探测。In various embodiments, the physiologic motion sensor may comprise a radar-based device that may be used to detect abnormal breathing (eg, expansion of the chest cavity when the abdominal cavity is contracted or abdominal cavity expansion when the chest cavity is contracted). In most cases, paradoxical breathing is indicated during obstructive apnea, although it does not indicate airway obstruction. In various embodiments, the indication of abnormal breathing and the level of abnormal breathing is useful in the detection of obstructive apnea.
基于雷达的生理运动传感器的多种实施方式还可测量非心肺运动(如在床上辗转反侧、失眠或睡眠中的不自主运动的活动)。活动的水平可用于估计睡眠质量，有助于确定主体的睡眠状态。系统100的多种实施方式也可用于确定人在床上还是床外，以跟踪主体在夜间离开床的频率等。系统100的多种实施方式也可测量心跳速率。在呼吸暂停中，心跳速率增加，在某些实施方式中，心跳速率可用来确认由其它测量指示的呼吸暂停。Various embodiments of the radar-based physiological motion sensor can also measure non-cardiopulmonary motion (such as tossing and turning in bed, insomnia, or involuntary motion activity during sleep). The level of activity can be used to estimate sleep quality, helping to determine the subject's sleep state. Various embodiments of the
系统100的多种实施方式可用于估计一次换气量或每次呼吸吸入和呼出的空气量。准确测量的一次换气量可用来估计气流。系统100的多种实施方式可包括处理器可执行的多天线硬件和软件，以便当他/她在夜间在床上移动时追踪主体。这可提供关于主体在床上作多少活动的信息，并能改善对呼吸和活动的基于雷达的测量。生理运动传感器可与其它传感器配合以提供对睡眠中的呼吸更全面的了解。系统100的多种实施方式可包括附加传感器，其包括但不限于鼻/口腔气流传感器和脉冲血氧仪。Various implementations of the
在多种实施方式中，鼻/口腔气流传感器可提供患者是否呼吸的指示，或使用更先进的传感器可提供对气流的速度估计。这可用来精确地探测呼吸暂停，而使用更先进的传感器还可探测呼吸不足(气流减少)。气流的精确测量对于确定是呼吸不足还是呼吸暂停是非常关键的。鼻/口腔气流传感器可包括一个或多个热敏电阻、热丝风速仪或压力传感器。在某些实施方式中，鼻/口腔气流传感器可用于独立地测量通过每个鼻孔和嘴的气流。在大多数的实施方式中，气流传感器并不能独自确定呼吸暂停是中枢性呼吸暂停或是阻塞性呼吸暂停。In various embodiments, a nasal/oral airflow sensor can provide an indication of whether the patient is breathing, or with more advanced sensors can provide an estimate of the velocity of the airflow. This can be used to accurately detect apnea and, with more advanced sensors, hypopnea (reduced airflow). Accurate measurement of airflow is critical for determining hypopnea or apnea. Nasal/oral airflow sensors may include one or more thermistors, hot wire anemometers, or pressure sensors. In certain embodiments, nasal/oral airflow sensors may be used to independently measure airflow through each nostril and mouth. In most embodiments, the airflow sensor alone cannot determine whether an apnea is a central apnea or an obstructive apnea.
在多种实施方式中，脉搏血氧仪可提供动脉血氧饱和度或血氧估计中的呼吸有效性的信息。血液中氧的降低能指示呼吸暂停或呼吸不足的严重性，并且对于临床决策很重要。脉搏血氧仪还可提供心跳速率。在多种实施方式中，脉搏血氧饱和度可记录在手指或耳朵上，尽管在大多数实施方式中，一般认为手指测量更准确。In various embodiments, a pulse oximeter may provide information on arterial oxygen saturation or respiratory effectiveness in blood oxygen estimation. A decrease in blood oxygen can indicate the severity of apnea or hypopnea and is important for clinical decision-making. Pulse oximeters also provide heart rate. In various embodiments, pulse oximetry may be recorded on a finger or ear, although finger measurements are generally considered more accurate in most embodiments.
在多种实施方式中，脉搏血氧仪和口/鼻腔气流传感器需要与患者接触。在多种实施方式中，脉搏血氧仪和口/鼻腔气流传感器可用于无线地将数据传输到数据记录装置。在多种实施方式中，该记录装置可与基于雷达的生理运动传感器装置集成。In various embodiments, the pulse oximeter and oral/nasal airflow sensor require contact with the patient. In various embodiments, pulse oximeters and oral/nasal airflow sensors can be used to wirelessly transmit data to a data logging device. In various embodiments, the recording device may be integrated with a radar-based physiological motion sensor device.
系统100的多种实施方式可包括无线家庭睡眠监测器，无线家庭睡眠监测器包括基于雷达的生理运动传感器、具有无线通信的脉搏血氧仪、具有无线通信的鼻/口腔气流传感器，该装置的运行不需要在患者身上设置导线并将与患者的接触最小化。家庭睡眠监测器的多种实施方式可提供对睡眠中的呼吸的全面了解(例如，气流、呼吸努力和氧化)。在多种实施方式中，家庭睡眠监测器系统100还可提供在睡眠中心跳速率、心跳速率的变化性以及关于运动的信息。在多种实施方式中，脉搏血氧仪和口/鼻腔气流传感器可用于独立地将其数据无线地发送到集线器，这样将不需要任何导线。这可提供优于其它商用家庭睡眠监测器的优点，所述其它商用家庭睡眠监测器需要连接记录装置的导线，或连接单独的随身携带装置的导线从而随后将数据无线地传输到记录装置。Various embodiments of the
生理运动传感器系统100的多种实施方式可用于获取在某个时间点或间歇性(如定期、在限定的时间内、按要求等)的生命体征的抽样调查，例如呼吸速率和心跳速率。在多种实施方式中，系统100具有可供用户选择的测量呼吸速率的不同时间间隔(例如，15秒、30秒、60秒等)、呼吸周期的选定数(例如，2、3、5等)、或测量时长的更普遍的指示(例如，“快”、“正常”、“延长”)。在多种实施方式中，系统100可使用信号质量、呼吸速率、呼吸速率变化性、呼吸波形的形状变化来自动地选择测量的时间间隔。在多种实施方式中，系统100能够鉴别非心肺运动、振动、其它无线电频率信号或电路噪声的干扰数据，并将其排除在速率计算外。这可提高速率读数的准确性。在多种实施方式中，还可通过包括精度检查的速率估计算法进一步改进速率读数的准确性。在提取心肺运动时，系统100的多种实施方式可用于鉴别主体的非心肺运动或主体附近的其它运动，这能使读数更准确和/或避免显示由于非心肺运动的探测而出现的错误。Various embodiments of the physiologic
在多种实施方式中，时域和频域方法可用于评估呼吸速率计算的有效性。在多种实施方式中，系统100可提供测量期间和测量之后的信号质量反馈系统。信号质量反馈可表明非心肺运动、信号干扰、低信号功率和/或由于信号过载的限幅。在多种实施方式中可在测量之前执行自检和环境检测。在多种实施方式中，系统100可使用空运行(free running)的信号源抑制射频干扰，例如随机频率漂移可抑制来自运行在相同频段内的源的干扰。在多种实施方式中，系统100可与用于家庭的慢性疾病管理和其它远程设置的其它装置、方法和外围设备集成。例如，系统100可与家庭健康管理单元中的血压计和温度计一起使用。作为健康数据亭(health kiosk)的一部分，系统100的多种实施方式可提供心肺信息。系统100的多种实施方式可用于测量每次呼吸的吸入/呼出的气量(相对一次换气量)和呼吸的深度。系统100的多种实施方式可提供过高或过低的心跳或呼吸速率或不规则的心跳或呼吸速率的警报。在多种实施方式中，系统100可用于探测心律失常或呼吸性窦性心律不齐。系统100的多种实施方式可具有调准或聚焦元件以帮助用户适当地调整系统从而进行准确测量。在多种实施方式中，提供按需的样本抽查测量。在多种实施方式中，可本地或远程地启动测量。系统100的多种实施方式可与视听或其它多媒体装置集成。In various embodiments, time-domain and frequency-domain methods can be used to assess the validity of respiration rate calculations. In various implementations,
系统100可用作非接触式的生命体征抽样检查，以获取一个或多个主体的呼吸速率和/或心跳速率。生命体征抽样检查系统100的实施方式可用于医院，或用于对住院患者进行定期的生命体征评估的专业护理设备，或用于对已登记检查处理的患者的生命体征评估的任何临床设置中。生命体征抽样检查系统100的实施方式可用于儿科或新生儿病房以监视婴幼儿和新生儿的心肺活动。系统100的多种实施方式可包括本地界面，其中本地界面包括按钮和显示器，并与中心站点(如中央护士站)或中央数据库(如电子医疗记录)电气通信。在多种实施方式中，系统100可以是独立式的装置，也可以是提供一个测量(如呼吸速率)或多个测量(例如呼吸速率与一次换气量或呼吸速率与心跳速率)的与其它生命体征抽样检查装置集成的模块。在多种实施方式中，生命体征抽样检查系统100只能显示测量的一个或多个速率。在某些实施方式中，系统100可用于显示心跳和/或呼吸波形的快照。在多种实施方式中，如在此引入其全部内容作为参考的美国临时申请第61/154,728号中所公开的，非接触式生命体征抽样检查可用于急诊室、灾区、战场中的治疗类选法。
如在此引入其全部内容作为参考的美国临时申请第61/196,762号中所公开的，此处所述的生命体征抽样检查系统的多种实施方式可用于家庭中监控慢性疾病，包括慢性阻塞性肺病、糖尿病和充血性心力衰竭的。如上所述，在多种实施方式中，系统100可连接到其它装置，包括但不限于个人保健系统、其他家庭医疗保健装置、个人电脑、手机、机顶盒、或数据聚合器。在系统的多种实施方式中，该装置可通过有线连接或无线连接与远程(例如，远离家庭)的中央站连接。在多种实施方式中，系统100可具有本地显示器，显示获取的部分或全部数据。在某些实施方式中，系统100可通过与远程(例如，远离家庭)中央数据库的有线或无线连接与其他装置通信。在多种实施方式中，系统100可由本地控制操作，也可由其它装置通过有线或无线连接进行控制。在多种实施方式中，系统100可自动运行，或由远程(例如，远离家庭)的中央系统控制。在系统的多种实施方式中，生命体征抽样检查系统100可以是与个人健康系统或其它家庭医疗保健装置集成的模块，共同分享显示器和通信。As disclosed in U.S. Provisional Application No. 61/196,762, which is hereby incorporated by reference in its entirety, various embodiments of the vital sign sampling system described herein can be used in the home to monitor chronic diseases, including chronic obstructive Lung disease, diabetes and congestive heart failure. As noted above, in various embodiments, the
在多种实施方式中，如在此引入其全部内容作为参考的美国临时申请第61/128,743号中所公开的，生命体征抽样检查系统100可设置在健康数据亭中。数据亭生命体征样本检查系统100的多种实施方式可以是独立式的装置，将生命体征的信息发送到数据亭计算机。系统100的多种实施方式需要位于本地的人按压装置上的按钮从而启动运行。在某些实施方式中，可由远程的医疗保健医生将启动信号通过数据亭计算机发送到装置来控制系统100。在某些实施方式中，当患者进入数据亭时，系统100可自动启动测量；系统100可感知患者的存在，或者，系统100可使用其它装置的数据，而该其它装置可感知患者的存在。数据亭生命体征抽样检查系统100的多种实施方式可以是集成于数据亭的模块，从而使患者不会意识到数据亭生命体征抽样检查系统100的存在。在这样的实施方式中，系统100可由数据亭计算机控制，或由远程保健医生启动测量，或者可能在患者进入数据亭后或坐下后的固定时间自动启动测量。在多种实施方式中，系统100仅能对呼吸速率测量一次，或当患者位于数据亭时可继续间歇性地测量，将患者在数据亭内的速率历史提供给远程医疗保健服务提供者。In various embodiments, the vital
在多种实施方式中，由系统100收集的心肺信息、活动和其它生理运动的数据可用于评估和监视心理或心理-生理状态的改变，或心理或心理-生理状态的改变。在多种实施方式中，系统100可监视由外部刺激(例如，问题、声音、图像等)引起的心理-生理状态的变化。In various implementations, cardiorespiratory information, activity, and other physiological movement data collected by the
如在此引入其全部内容作为参考的美国临时申请第63/141,213号中所公开的，非接触式生理传感器系统100的多种实施方式可用来获取呼吸速率、心跳速率、并进行分析以辅助评估测量主体的心理状态的生理波形。心理信息可用于许多应用，包括但不限于多种医疗应用、主体在机场、边境、体育赛事和其它公共场所的安全检查、测谎、心理或精神评价。在用于安全检查的系统100的多种实施方式中，从系统100输出的信息可辅助检测恶意(malintent)。As disclosed in U.S. Provisional Application Serial No. 63/141,213, which is hereby incorporated by reference in its entirety, various embodiments of a non-contact
如在此引入其全部内容作为参考的美国临时申请第61/154,176号中所公开的，生理运动传感器系统100的多种实施方式可用于提供生理运动波形，该波形可用于胸部或器官运动的同步医疗成像。As disclosed in U.S. Provisional Application Serial No. 61/154,176, which is hereby incorporated by reference in its entirety, various embodiments of a physiological
此处所述的系统的多种实施方式可用于提供生理运动波形，该波形可用于使包括非侵入性通气的机械通风和呼吸努力同步。Various embodiments of the systems described herein can be used to provide a physiological motion waveform that can be used to synchronize mechanical ventilation, including non-invasive ventilation, with respiratory effort.
系统100的多种实施方式可与脉搏血氧仪集成。此处所述的多种实施方式，生理运动传感器100可用于感知呼吸信息，并可与测量患者的血氧饱和度的脉搏血氧仪配合运行。在多种实施方式中，如在此引入其全部内容作为参考的美国临时申请第61/194,839号中所公开的，这两个传感器系统的结合可提供通风和氧化的信息，提供比单独使用任何一个传感器时更完整的呼吸功效测量。这些实施方式可用于监视术后患者、使用阿片类药物的患者、具有呼吸抑制风险的患者等。Various embodiments of
系统100的多种实施方式可与患者控制的镇痛系统集成或连接，并在呼吸速率下降到低于某一阈值(表示阿片类药物引起的呼吸抑制)时防止增加额外的镇痛剂。多种实施方式还可在计算时使用额外的呼吸变量以防止额外的镇痛剂，呼吸变量包括一次换气量、吸气时间与呼气时间的比率、呼吸深度、非心肺运动频率、非心肺运动持续时间、呼吸暂停的时长、频率、深度、喘息的时长、频率、深度、信号的时长、和/或呼吸波形的形状。在这些实施方式中的阈值至少可以是在工厂预设的，或由医疗专业人士基于患者的基准值计算而设定的。多种实施方式还可包括警报。Various embodiments of the
在多种实施方式中，系统100可用于确定主体是否在呼吸和/或主体的心脏是否在跳动。在多种实施方式中，系统100可探测和/或监视从距离主体几米之外到接触点之间的心肺信息(呼吸系统和/或心脏)的存在。在多种实施方式中，当与主体的身体接触时，系统100可探测和监视心肺信息(呼吸系统和心脏)。在多种实施方式中，系统100可测量与心肺活动有关的人体表面的运动。在多种实施方式中，系统100可测量与心肺活动有关的人体内部的运动。在多种实施方式中，系统100可测量与心肺活动有关的电磁可测的身体内部和/或外部的变化，包括但不限于阻抗变化。在多种实施方式中，系统100可通过自身或与其它监视装置结合以执行上述功能。In various implementations,
如在此引入其全部内容作为参考的美国临时申请第61/194,838号中所公开的，在多种实施方式中，此处所述的生理运动传感器可用于通过探测患者是否有心跳来确定主体是否需要心肺复苏或使用除颤器(自动体外除颤器或医用除颤器)。在多种实施方式中，系统100可向外部医疗装置发送信号，从而结合来自系统的信息和来自其它传感器的信息以确定是否需要急救。此决定可从视觉或听觉上指示用户。在多种实施方式中，系统100可为除颤器提供信号，从而使得如果探测到心跳，就不会对患者使用电击。在多种实施方式中，系统100可发送信号以触发外部医疗装置(例如，除颤器、呼吸机、氧气泵、外部呼吸器等)。对患者使用除颤器之后，非接触式生理运动传感器可用于确定自动心脏活动是否已经恢复。As disclosed in U.S. Provisional Application No. 61/194,838, which is hereby incorporated by reference in its entirety, in various embodiments, the physiological motion sensors described herein can be used to determine whether a subject is CPR or use of a defibrillator (automated external defibrillator or medical defibrillator) is required. In various implementations, the
在多种实施方式中，生理运动传感器系统100可用于以一定距离和/或通过雷达穿透障碍来探测人体运动。在多种实施方式中，该运动可包括诸如散步的大运动，和由于坐立不安或讲话、以及由心肺活动产生的片刻的表面位移的小运动。在多种实施方式中，如在此引入其全部内容作为参考的美国临时申请第61/125,164号中所公开的，通过精密的信号处理可将来自不同信号源的信号分离，并分类成每个人独特的生物特征签名。在多种实施方式中，如在此引入其全部内容作为参考的美国临时申请第61/125,023号中所公开的，经验模式分解可用于鉴别生理运动，包括心跳和呼吸运动波形的个人签名。在某些实施方式中，如在此引入其全部内容作为参考的美国临时申请第61/125,023号中所公开的，经验模式分解可用于鉴别生理运动的幅度变化模式。在多种实施方式中，如在此引入其全部内容作为参考的美国临时申请第61/125,023号中所公开的，经验模式分解可用于确定生理处理速率的变化模式，例如心跳速率变化性和呼吸速率变化性。在多种实施方式中，如在此引入其全部内容作为参考的美国临时申请第61/125,023号中所公开的，经验模式分解可用于分析相互作用。In various implementations, the physiological
在多种实施方式中，从心肺运动信号提取的许多变量可用于个人的生物特征鉴别。在多种实施方式中，这些变量包括呼吸速率、吸气时间、呼气时间、吸气时间与呼气时间的比率、喘息频率、喘息深度、喘息时长、信号频率，信号深度、信号时长、呼吸深度、反常呼吸的出现、反常呼吸的程度、一次换气量、腹腔偏移与胸腔偏移的比率、呼吸信号的和谐波含量、气流速率、心跳速率以及心跳间隔。在多种实施方式中，生物特征鉴别还将包括部分或全部上述变量在任意个频段的变化。在多种实施方式中，生物特征鉴别还将包括与心跳变量和呼吸变量之间的相互关系。在多种实施方式中，生物特征鉴别还将包括活动的频率、持续时间、活动量和/或烦躁的频率、持续时间和烦躁量。In various embodiments, a number of variables extracted from cardiopulmonary exercise signals can be used for biometric authentication of an individual. In various embodiments, these variables include respiration rate, inspiratory time, exhalation time, ratio of inspiratory time to exhalation time, wheeze frequency, wheeze depth, wheeze duration, signal frequency, signal depth, signal duration, respiration Depth, presence of abnormal breathing, degree of abnormal breathing, tidal volume, ratio of abdominal excursion to thoracic excursion, respiratory signal and harmonic content, airflow rate, heart rate, and heartbeat interval. In various embodiments, biometric authentication will also include changes in some or all of the above variables in any frequency band. In various embodiments, biometric authentication will also include correlations with heartbeat and respiration variables. In various embodiments, the biometrics will also include frequency, duration, amount of activity and/or frequency, duration and amount of restlessness.
如在此引入其全部内容作为参考的美国临时申请第61/125,021号中所公开的，系统100的多种实施方式可用于确定患者的一次换气量。系统100的多种实施方式可根据医疗记录信息确定位移和一次换气量之间的关系，使得准确测量的位移可转换为一次换气量估计。如在此引入其全部内容作为参考的美国临时申请第61/125,018号中所公开的，在多种实施方式中，系统100可基于患者动作和医疗记录信息来确定位移和一次换气量之间的关系，从而需要非接触式装置来进行校准。在系统的某些实施方式中，公布的规则和医疗记录可用于预测患者的肺活量，从而使如果患者通过尽可能深地吸气且尽可能充分地呼气进行肺活量动作，则可计算出胸腔位移和一次换气量之间的关系。在多种实施方式中，系统100可在测量前进行校准，从而可估算一次换气量。在多种实施方式中，如在此引入其全部内容作为参考的美国临时申请第61/125,021号中所公开的，系统100可用于通过直接测量确定位移和一次换气量之间的关系：利用肺活量计或其它准确测量一次换气量的装置来校准。Various embodiments of the
在多种实施方式中，通过在对患者的连续监视过程中提供关于一次换气量相对于基线值是增加还是减少的信息，可在不需要校准的情况下测量相对一次换气量。在测量相对一次换气量的多种实施方式中，相对一次换气量可在每次探测到非心肺运动时复位，从而避免相对一次换气量的误差，该误差是由处于不同位置的患者以及传感器与患者之间不同的空间关系引起的胸腔位移和一次换气量之间的关系变化引起的。这种实施方式有益于无通风或无创式通风的危重患者。In various embodiments, relative tidal volume can be measured without the need for calibration by providing information on whether tidal volume is increasing or decreasing relative to a baseline value during continuous monitoring of a patient. In various embodiments where the relative tidal volume is measured, the relative tidal volume can be reset each time a non-cardiopulmonary exercise is detected, thereby avoiding relative tidal volume errors that are caused by patients in different positions As well as the change in the relationship between thoracic displacement and one ventilation volume caused by the different spatial relationship between the sensor and the patient. This embodiment is beneficial for non-ventilated or non-invasively ventilated critically ill patients.
在多种实施方式中，来自系统100的数据可用于生成活动指数。在多种实施方式中，系统100可使用非心肺运动探测算法以确定主体活动的频率和持续时间或主体活动时间的百分比。该信息可用于提供活动指数。在某些实施方式中，活动指数的变化可用作健康状态的变化指示(例如，如果患者的一天的活动明显少于其基线，则可表明患者患有疾病)。在多种实施方式中，还可在睡眠主体的测量过程中利用活动指数来评估睡眠与清醒状态、失眠、下肢不宁综合征的对比。在多种实施方式中，活动指数可用于评估昼夜节律紊乱、警觉度、代谢活动、能量消耗和白天嗜睡。In various implementations, data from
系统100的多种实施方式可用于探测呼吸暂停或呼吸停止活动。例如，在某些实施方式中，如在此引入其全部内容作为参考的美国临时申请第61/072,982号中所公开的，如果生理运动传感器探测到没有高于规定阈值的本地极大值，系统100可探测到的呼吸停止。Various embodiments of the
如在此引入其全部内容作为参考的美国临时申请第61/072,983号和第61/123,135号中所公开的，在多种实施方式中，装置可使用算法来确定由于呼吸停止或主体不再存在而导致的没有超过规定阈值的本地极大值。在某些实施方式中，该算法可包括分析两个频段：高频段和低频段，其通过处理器可执行的软件过滤器进行分离。如果呼吸的主体存在，该装置可通过主要集中在低频段(约低于0.8赫兹)的呼吸信号告知主体存在。然而，如果主体没有呼吸，该装置仍可探测其它运动，包括含有较高频成分的心跳或其它无意识运动。因此，该装置可利用阈值功率水平比较不同频段的平均功率来确定非呼吸主体的存在或不存在。As disclosed in U.S. Provisional Application Nos. 61/072,983 and 61/123,135, the entire contents of which are hereby incorporated by reference, in various embodiments, the device may use an algorithm to determine resulting in local maxima that do not exceed the specified threshold. In certain embodiments, the algorithm may include analyzing two frequency bands: a high frequency band and a low frequency band, separated by a processor-executable software filter. If a breathing subject is present, the device can communicate the presence of the subject through a breathing signal mainly concentrated in the low frequency band (below about 0.8 Hz). However, if the subject is not breathing, the device can still detect other movements, including heartbeats or other involuntary movements with higher frequency components. Thus, the device can compare the average power of different frequency bands using the threshold power level to determine the presence or absence of a non-breathing subject.
装置的多种实施方式可基于频率分析和由运动传感器获取的心肺和非心肺信号来区分主体的存在和不存在。在多种实施方式中，如在此引入其全部内容作为参考的美国临时申请第61/123,135号、第61/001,996号和第No.61/154,732号中所公开的，非接触式生理运动传感器可用于确定主体是否存在。例如，在家庭监视情况下，系统100可用于跟踪患者在特定位置或特定房间待了多久。例如，在数据亭方式中，当主体存在于数据亭时，系统能够确定主体在数据亭中待多久。Various embodiments of the device can differentiate the presence and absence of a subject based on frequency analysis and cardiopulmonary and non-cardiopulmonary signals acquired by motion sensors. In various embodiments, a non-contact physiological motion sensor as disclosed in U.S. Provisional Application Nos. 61/123,135, 61/001,996, and 61/154,732, the entire contents of which are Can be used to determine if a principal exists. For example, in a home monitoring situation, the
在多种实施方式中，非接触式生理运动传感器在安全应用中还能以穿墙模式来确定容器或房间中是否有人存在。由于传感器可用于探测心跳速率，因此还可用于探测藏匿或屏住呼吸的人。In various embodiments, the non-contact biomotion sensor can also be used in a through-the-wall mode in a security application to determine the presence of a person in a container or room. Since the sensor can be used to detect heart rate, it can also be used to detect people who are hiding or holding their breath.
如在此引入其全部内容作为参考的美国临时申请第61/072,983号和第61/123,135号中所公开的，在多种实施方式中，装置可基于算法来探测主体的存在或不存在。在某些实施方式中，该算法可包括两个频段的分析：高频段和低频段，其通过处理器可执行的过滤软件进行分离。如果呼吸的主体存在，该装置可通过主要集中在低频段(约低于0.8赫兹)的呼吸信号告知主体存在。然而，如果主体没有呼吸，该装置仍可探测其它运动，包括含有较高频成分的心跳或其它无意识运动。因此，该装置可利用比较阈值功率水平来比较不同频段的平均功率从而确定非呼吸主体是否存在。在某些实施方式中，当装置朝向特定的床或椅子时，可通过生理运动活动是否大于阈值来探测主体的存在，其中阈值是基于基线测量来设定的。在某些实施方式中，如果主体不存在，则可关闭呼吸处理。In various embodiments, the device may detect the presence or absence of a subject based on an algorithm, as disclosed in US Provisional Application Nos. 61/072,983 and 61/123,135, the entire contents of which are hereby incorporated by reference. In certain embodiments, the algorithm may include the analysis of two frequency bands: a high frequency band and a low frequency band, which are separated by processor-executable filtering software. If a breathing subject is present, the device can communicate the presence of the subject through a breathing signal mainly concentrated in the low frequency band (below about 0.8 Hz). However, if the subject is not breathing, the device can still detect other movements, including heartbeats or other involuntary movements with higher frequency components. Accordingly, the device may compare the average power of different frequency bands using the comparison threshold power level to determine the presence of a non-breathing subject. In certain embodiments, the presence of a subject may be detected by whether the physio-motor activity is greater than a threshold when the device is oriented toward a particular bed or chair, where the threshold is set based on the baseline measurements. In some embodiments, respiratory processing may be turned off if the subject is not present.
此处所述的系统100的多种实施方式包括基于雷达的生理运动传感器。系统100的多种实施方式可包括辐射源、一个或多个接收主体散射的辐射的接收器、将所接收信号数字化的系统(例如模拟数字转换器)。系统100的多种实施方式还可包括处理器、计算机或微处理器以处理数字信号并提取生理运动的相关信息。在多种实施方式中，处理器可由控制器控制。生理运动的相关信息可以多种方式传达给用户(如视觉或图形显示、在有线或无线通信链路上或网络上电子传送、通过内部声音或警报的听觉通信等)。Various embodiments of the
此处所述的系统100的多种实施方式可不接触地运行，并在距离主体一定的距离处工作。系统100的多种实施方式可运行于任何姿态的主体，包括躺卧、斜倚、坐或站立。系统100的多种实施方式可工作在距离主体的不同距离处，例如0.1至4.0米。在某些实施方式中，系统100可置于相对于主体的不同位置，包括但不限于，在主体之前、主体之后、主体之上、主体之下以及主体的一侧或在主体的各个角度方向。在某些实施方式中，系统100在被置于主体(例如患者)胸部时也可运行。在这些实施方式中，系统100可以置于主体的胸部之上，由用户保持在主体胸部或用带、项链或吊带戴在主体胸部。Various embodiments of the
系统100的多种实施方式可使用与指定算法结合的多个接收信道来确定目标的方向、从空间分离的生理运动中隔离非生理运动、同时探测不同主体的生理运动、跟踪单个主体的角度，或者当一个或多个其它主体在观测范围内时隔离来自第一主体的生理运动。Various embodiments of the
在多种实施方式中，可添加多个接收天线和接收通道以提供多通道输出。这些附加的接收通道可用于确定目标方向、从空间分离的生理运动中隔离非生理运动、同时探测来自不同主体的生理运动，或者当第二主体在观测范围内时隔离来自第一主体的生理运动。如在此引入其全部内容作为参考的美国临时申请第61/141,213号、第61/204,881号和第61/137,519号中所公开的，用于提供来自多个天线的该信息的算法包括但不限于波达方向、独立成分分析以及盲源分离。In various embodiments, multiple receive antennas and receive channels can be added to provide a multi-channel output. These additional receive channels can be used to determine target orientation, isolate non-physiological motion from spatially separated physiological motion, simultaneously detect physiological motion from different subjects, or isolate physiological motion from a first subject when a second subject is within view . Algorithms for providing this information from multiple antennas include, but do not Limited to direction of arrival, independent component analysis, and blind source separation.
在多种实施方式中，生理运动传感器系统100可以是独立式的装置，具有显示器、用户界面、时钟、记录硬件和软件、信号处理硬件和软件、和/或通信硬件和软件，这些都可集成在一个单元中，或集成在可包括通过例如USB的导线连接的多个单元中。可选地，生理传感器也可集成作为包括其它监视装置(生理的和/或非生理的)的系统的一部分，并使用该系统的显示器、用户界面、时钟、记录硬件和软件、信号处理硬件和软件和/或通信硬件。在多种实施方式中，传感器可接收来自系统的模拟或数字同步信号，使得来自传感器的数据可与来自其它传感器和事件的信号同步，或者传感器可将模拟或数字同步信号传输到系统，或传感器可具有与系统时钟同步的内部时钟，并使用用于同步的数据上的时间戳。在某些实施方式中，传感器可以是具有自己的信号处理硬件和软件的、与系统双向通信的装置，其中所述系统包括显示、记录和/或在系统外的通信，还可能包括对来自装置的波形的附加信号处理，如果包括附加信号处理的话还可能包括对来自其它装置的波形的信号处理。在这种情况下，装置接收来自系统的用于启动测量、停止测量的命令和其它硬件控制信号。在某些实施方式中，装置可执行初始信号处理并提供由系统进行分析的波形。如在此引入其全部内容作为参考的美国临时申请第61/204,880号中所公开的，这些数据可被实时的或后置处理。In various embodiments, the physiological
在多种实施方式中，传感器系统100具有警报，如果探测到患者的呼吸不规则或出现异常时能发布警报。在某些实施方式中，系统100还可激活警报(例如，当主体停止呼吸超过30秒钟或呼吸速率快于约每分钟20次或更快超过每10秒20次)。In various embodiments, the
在多种实施方式中，如在此引入其全部内容作为参考的美国临时申请第61/141,213号中所公开的，可由生理运动传感器获取与呼吸努力、由于潜在的心跳运动而发生的胸壁运动、及周脉搏运动等相关的生理波形。来自这些波形的信息可包括但不限于如在此引入其全部内容作为参考的美国临时申请第61/141,213号中所公开的呼吸速率、吸气时间，如在此引入其全部内容作为参考的美国临时申请第61/141,213号中所公开的呼气时间，如在此引入其全部内容作为参考的美国临时申请第61/141,213号中所公开的吸气时间和呼气时间的比率，如在此引入其全部内容作为参考的美国临时申请第61/141,213号中所公开的喘息的频率、深度和时长，如在此引入其全部内容作为参考的美国临时申请第61/141,213号中所公开的信号的频率、深度和时长，如在此引入其全部内容作为参考的美国临时申请第61/072,983号中所公开的呼吸深度，如在此引入其全部内容作为参考的美国临时申请第61/194,836号、第61/194,848号和第61/200,761号中所公开的反常呼吸的存在和程度，如在此引入其全部内容作为参考的美国临时申请第61/125,021号、第61/125,018号中所公开的一次换气量，如在此引入其全部内容作为参考的美国临时申请第61/141,213号中所公开的腹腔活动和胸腔活动的比率，如在此引入其全部内容作为参考的美国临时申请第61/141,213号中所公开的呼吸信号的谐波含量，如在此引入其全部内容作为参考的美国临时申请第61/141,213号中所公开的呼吸波形的形状，如在此引入其全部内容作为参考的美国临时申请第61/072,983号和第61/125,021号中所公开的气流速度，如在此引入其全部内容作为参考的美国临时申请第61/072,983号中所公开的呼吸困难指示，如在此引入其全部内容作为参考的美国临时申请第61/125,021号中所公开的非受迫性肺活量，如在此引入其全部内容作为参考的美国临时申请第61/125,019号中所公开的心跳和脉搏速率、平均心跳速率、脉搏和呼吸速率、心跳间隔、心跳速率变化性、血压、脉搏传导时间、心脏输出量、其它呼吸信号，心跳速率和呼吸速率或波形之间的关系、活动的频率、持续时间和活动量，烦躁的频率、持续时间、烦躁量和肺液含量。In various embodiments, as disclosed in U.S. Provisional Application No. 61/141,213, which is hereby incorporated by reference in its entirety, physiological motion sensors can be used to obtain information related to respiratory effort, chest wall motion due to underlying heartbeat motion, Physiological waveforms related to weekly pulse movement and so on. Information from these waveforms may include, but is not limited to, respiration rate, inspiratory time as disclosed in U.S. Provisional Application No. 61/141,213, which is hereby incorporated by reference in its entirety, U.S. Exhalation time as disclosed in Provisional Application No. 61/141,213, ratio of inspiratory time to expiratory time as disclosed in U.S. Provisional Application No. 61/141,213, which is hereby incorporated by reference in its entirety, as herein The frequency, depth and duration of wheezing as disclosed in U.S. Provisional Application No. 61/141,213, which is hereby incorporated by reference in its entirety, as the signal disclosed in U.S. Provisional Application No. 61/141,213, which is hereby incorporated by reference in its entirety The frequency, depth and duration of breathing, as disclosed in U.S. Provisional Application No. 61/072,983, which is hereby incorporated by reference in its entirety, as disclosed in U.S. Provisional Application No. 61/194,836, which is hereby incorporated by reference in its entirety , 61/194,848 and 61/200,761, as disclosed in U.S. Provisional Application Nos. 61/125,021, 61/125,018, the entire contents of which are hereby incorporated by reference The ratio of abdominal and thoracic activity as disclosed in U.S. Provisional Application No. 61/141,213, which is hereby incorporated by reference in its entirety, as disclosed in U.S. Provisional Application No. Harmonic content of respiratory signals as disclosed in 61/141,213, as disclosed in U.S. Provisional Application Serial No. 61/141,213, the entire contents of which are incorporated herein by reference as Airflow velocity as disclosed in referenced U.S. Provisional Application Nos. 61/072,983 and 61/125,021, as disclosed in U.S. Provisional Application No. 61/072,983, which is hereby incorporated by reference in its entirety, as an indication of dyspnea, as Unforced vital capacity as disclosed in U.S. Provisional Application No. 61/125,021, which is hereby incorporated by reference in its entirety, heartbeat as disclosed in U.S. Provisional Application No. 61/125,019, which is hereby incorporated by reference in its entirety and pulse rate, average heart rate, pulse and respiration rate, heartbeat interval, heart rate variability, blood pressure, pulse transit time, cardiac output, other respiratory signals, relationship between heart rate and respiration rate or waveform, frequency of activity , duration and activity, frequency, duration, amount of restlessness and lung fluid content of irritability.
这些变量在不同频段的变化也是分析的主体，包括心跳速率变化性和呼吸速率变化性，还包括对心跳或呼吸波形的形状改变的变化性、呼吸深度的改变、以及反常呼吸程度的改变。这些可抽样检查测量，在患者休息时连续监视，在进行询问的特定时间监视，作出声明或执行指定任务时监视，或还可在主体进行正常活动时监视。Changes in these variables in different frequency bands are also the subject of analysis, including heart rate variability and respiration rate variability, as well as variability in shape changes to heartbeat or respiration waveforms, changes in respiration depth, and changes in the degree of abnormal respiration. These may be spot check measurements, continuous monitoring while the patient is at rest, monitoring at specific times when interrogations are made, statements are made or prescribed tasks are performed, or also while the subject is performing normal activities.
从这些波形提取的信息可显示在显示单元上。在多种实施方式中，可在屏幕上提供的信息包括但不限于呼吸速率、吸气时间、呼气时间、吸气时间和呼气时间的比率、呼吸深度、反常呼吸的存在和程度、一次换气量、腹腔偏移和胸腔偏移的比率、心跳或脉搏速率、平均心跳速率、平均脉搏速率和平均呼吸速率、心跳间隔。在多种实施方式中，波形提供的信息可包括但不限于呼吸波形、非接触式获取的心跳波形、接触胸部的装置获取的心跳波形和脉搏波形。在多种实施方式中，对屏幕上提供的信息的分析可包括如在此引入其全部内容作为参考的美国临时申请第61/125,019号中所公开的呼吸速率的历史、心跳速率的历史、活动指数(主体物理活动的时间百分比)，如在此引入其全部内容作为参考的美国临时申请第61/125,021号中所公开的一次换气量随时间的变化，如在此引入其全部内容作为参考的美国临时申请第61/125,021号中所公开的空气流动速率随肺容积的变化。Information extracted from these waveforms can be displayed on a display unit. In various embodiments, information that may be provided on the screen includes, but is not limited to, respiration rate, inspiratory time, exhalation time, ratio of inspiratory time to exhalation time, depth of respiration, presence and extent of abnormal respiration, Ventilation volume, ratio of abdominal excursion to thoracic excursion, heartbeat or pulse rate, average heart rate, average pulse rate and average respiration rate, heartbeat interval. In various embodiments, the information provided by the waveforms may include, but is not limited to, respiratory waveforms, non-contact acquired heartbeat waveforms, heartbeat waveforms acquired by devices touching the chest, and pulse waveforms. In various embodiments, the analysis of the information provided on the screen may include history of respiration rate, history of heart rate, activity Index (percentage of time of physical activity of the subject), as disclosed in U.S. Provisional Application No. 61/125,021, which is hereby incorporated by reference in its entirety Air flow rate as a function of lung volume as disclosed in U.S. Provisional Application No. 61/125,021 of .
如上所述，在多种实施方式，生理运动传感器700可作为连续波无线收发机。在多种实施方式中，如在此引入其全部内容作为参考的美国临时申请第61/072,983号中所公开的，该收发机可作为具有单正交接收通道的单独的发射器，如图7所示。在某些实施方式中，如在此引入其全部内容作为参考的美国临时申请第61/072,983号和第61/125,027号中所公开的，传感器700可包括具有多个接收通道或天线702、703、704(例如单输入多输出系统)的单独的发射器701。在某些实施方式中，如在此引入其全部内容作为参考的美国临时申请第61/125,027号和第61/137,519号中所公开的，传感器700可包括每个位于不同的频率的多个发射器，以及多个接收通道或天线，其中每个天线都可接收所有频率。As noted above, in various embodiments, the
在多种实施方式中，收发机包括发射器和接收器。在连续波的实现中，收发机可产生馈送到天线的单频信号。该收发机可在从100MHz到100GHz中的任何频率下运行，包括但不限于频率在902-928MHz的工业、科学和医用(ISM)频段、2.400-2.500GHz的ISM频段、5.725-5.875GHz的ISM频段、10.475-10.575GHz的运动探测频段和24.00-24.25GHz的ISM频段。该信号由电压控制振荡器(VCO)705在内部产生，电压控制振荡器(VCO)705可以是锁相晶振或非锁相的晶振或外部时钟。在某些实施方式中，如果装置集成于外部系统，可由外部系统提供信号。在多种实施方式中，信号源可在内部产生并与外部信号同步，也可在外部系统中产生。在多种实施方式中，主板可包括射频开关，射频开关可将已发射的射频功率量改变约10分贝或更多。In various implementations, a transceiver includes a transmitter and a receiver. In a continuous wave implementation, the transceiver generates a single frequency signal that is fed to the antenna. The transceiver can operate at any frequency from 100MHz to 100GHz, including but not limited to industrial, scientific and medical (ISM) bands at 902-928MHz, ISM bands at 2.400-2.500GHz, ISM at 5.725-5.875GHz frequency band, 10.475-10.575GHz motion detection frequency band and 24.00-24.25GHz ISM frequency band. The signal is generated internally by a voltage controlled oscillator (VCO) 705, which can be a phase-locked or non-phase-locked crystal or an external clock. In some embodiments, if the device is integrated with an external system, the signal may be provided by the external system. In various embodiments, the signal source can be generated internally and synchronized with the external signal, or can be generated in an external system. In various embodiments, the motherboard can include a radio frequency switch that can change the amount of radio frequency power that has been transmitted by about 10 decibels or more.
在多种实施方式中，如在此引入其全部内容作为参考的美国临时申请第61/072,983号、第61/128,743号和第61/137,519号中所公开的，接收器可与能产生正交输出(也称为正交解调)的复和混合器706、707、708零差(也称为直接转换)。在多种实施方式中，如在此引入其全部内容作为参考的美国临时申请第61/128,743号中所公开的，接收器也可是低中频接收器，其中包括可直接将中频(IF)数字化的外差接收器。在多种实施方式中，中频的范围可从几赫兹到约200千赫。在某些实施方式中，中频可大于200千赫。在多种实施方式中，如在此引入其全部内容作为参考的美国临时申请第61/128,743号中所公开的，收发机还可使用外差或超外差接收器。在多种实施方式中，发射器和接收器可包括单个天线或用作单个天线的天线阵列。可在由模拟向数字转换器710数字化之前由模拟信号处理器709处理来自接收器的正交输出。In various embodiments, the receivers may be orthogonal to the The complex and
在多种实施方式中，可通过交流耦合或其它直流消除的方法去除直流偏移量。在某些实施方式中，直流消除的方法可利用数字控制的信号源作为非时变(直流)参考与原始信号进行比较。在某些实施方式中，数字控制的信号源是具有数字控制电位计的分压器。当由差分功能执行比较时，该方法可消除直流偏移同时保留时变信号。在某些实施方式中，在直流消除周期的开端，由搜寻函数启动直流消除，该函数反复搜寻正确的直流偏移值。在某些实施方式中，通过使用额外的采集装置启动直流消除，从而在通过在放大和补偿之前获得充分的信号来即时地提供直流消除的粗略初值估计。一旦得出直流偏移的初始值并从信号中减去该初始值，数字控制参考可通过分析新补偿和放大的信号进行微调，然后优化以找到更好的直流偏移值。利用多种方法可得出新的直流偏移值，其中，方法包括但不限于：第一读值，在呼吸周期的中间、呼吸周期的平均值、或找出在复系数星座图中的呼吸弧的中心点(通过计算相位信号和正交信号的平均值得出，并分别为通道1和Q设置直流偏移值)。使用上述方法，直流偏移消除参考信号可进行动态调整以响应雷达视野中大的或细微的变化，从而确保最低信号损失或扭曲，同时保持采集装置的适当的分辨率。在多种实施方式中，直流消除可包括调制发送或接收的射频信号。利用相敏同步的解调器、放大器和低通滤波，可从高噪声、较大的直流偏移环境中提取信号。在某些实施方式中，这可以是类似于具有锁定放大器的信号斩波。可由多种方式实现调制，包括但不限于：物理方式如振动方式或电子方式，如调节发送信号或接收信号的相位、波幅或频率。In various embodiments, the DC offset can be removed by AC coupling or other DC cancellation methods. In some embodiments, a method of DC cancellation may utilize a digitally controlled signal source as a time-invariant (DC) reference to which the original signal is compared. In some embodiments, the digitally controlled signal source is a voltage divider with a digitally controlled potentiometer. This method removes the DC offset while preserving the time-varying signal when the comparison is performed by a differential function. In some embodiments, at the beginning of a DC cancellation period, DC cancellation is initiated by a search function that iteratively searches for the correct DC offset value. In some embodiments, DC cancellation is enabled by using an additional acquisition device, providing a rough initial estimate of DC cancellation instantaneously by obtaining sufficient signal prior to amplification and compensation. Once an initial value for the DC offset is found and subtracted from the signal, the digitally controlled reference can be fine-tuned by analyzing the newly compensated and amplified signal, and then optimized to find a better value for the DC offset. The new DC offset value can be derived using a variety of methods, including but not limited to: first reading, in the middle of the breath cycle, averaging the breath cycle, or finding the breath in the complex coefficient constellation The center point of the arc (obtained by averaging the phase and quadrature signals and setting DC offset values for
图8示出用于执行直流消除800的方法的实施方式的流程图。开始时，如框801所示，模拟数字转换器(ADC)获取通过多普勒频移接收信号转换获得的运动信号。在框802中，如果确定该信号被截取，则该方法转入框803。在框803中，如框803a和803b所示，基于以下几个因素中的至少一个调整直流偏移的估计值：系统增益、ADC的输入范围和多种其它因素。如框803c所示，直流偏移的估计值输出到数字模拟转换器(DAC)。如框804所示，清除信号缓存，该方法返回到框801重新获得信号，其中优质信号缓存用于存储连续获得的没有截取的信号。FIG. 8 shows a flowchart of an embodiment of a method for performing
在框802中如果确定信号没有被截取，则方法转入步骤805，其中针对阈值长度检查优质信号缓存的长度。在多种实施方式中，阈值长度可由用户或系统设计者设定。在多种实施方式中，阈值长度可以至少是一个完整的呼吸周期的样本数，约大于6秒。如果优质信号缓存的长度小于阈值长度，则方法转入框806，其中通过获得更多的信号建立优质信号。然而，如果优质信号缓存的长度大于阈值的长度，则该方法转入框807，如框807a和807b所示，优化直流偏移的估计值。在优化过程中，以多种方式分析优质信号缓存，例如，通过计算平均值、中位数或中端电压值。对于正交系统，可优化弧中心点。如框807c所示，优化后的直流偏移值输出到DAC，方法转入框808以继续采集信号。If in
在系统100的多种实施方式中，上述一个或多个发射器发射的信号被主体及其周围物件散射，随后由以上描述为基于雷达的心肺运动传感器的一个或多个接收器接收。在多种实施方式中，多普勒频移信号可转化为由零差接收器或外差接收器接收的模拟运动信号。可选地，多普勒频移信号可向下变换到可直接数字化的中频，且用数字地生成运动信号。在多种实施方式中，模拟运动信号在其数字化之前需要信号和低中频加工。在多种实施方式中，信号加工系统100可包括一个或多个基带放大器。在多种实施方式中，信号加工系统100可包括一个或多个模拟抗混叠过滤器。在多种实施方式中，信号加工系统100可包括消除直流偏移的方法，包括但不限于，高通滤波、交流耦合或如本文所述的直流偏移消除。在多种实施方式中，一个或多个基带放大器是固定的放大器。在多种实施方式中，一个或多个基带放大器是可变增益放大器(VGA)。在多种实施方式中，VGA可具有两个或多个阶段。在多种实施方式中，VGA可具有连续的可调增益。VGA由数字控制信号控制。在多种实施方式中，如在此引入其全部内容作为参考的美国临时申请第61/141,213号中所公开的，VGA的增益水平可由用户确定或由处理器动态地通过信号分析确定。In various embodiments of the
在某些实施方式中，接收器的每个天线或天线阵列具有一个正交输出。在某些实施方式中，接收器可具有随不同模拟滤波和/或放大的多个输出，以在数字化和数字信号处理之前隔离不同的信息。这对改善每个生理运动信号的动态范围是有益的。例如，如在此引入其全部内容作为参考的美国临时申请第61/141,213号中所公开的，每个基带信号将被分割为具有对于心跳信号和呼吸信号不同的增益和滤波。在多种实施方式中，系统100可包括数字信号装置或数字-模拟转换器(DAC)和硬件，硬件可由软件控制。在多种实施方式中，可以多种方式控制硬件，其中包括但不限于：可在多种实施方式中旋转收发机的零件或组件以及开启或关闭信号加工系统，以对可控的上电或自测节省电源；可在多种实施方式中开启或关闭接收和/或发送的射频信号，以减少对无线电信号或用于自测的泄漏，设置接收器增益，其可用于增加系统的动态范围；设置信号调解中直流偏移的补偿；在采集之前控制信号调解中的增益量；修改数据采集的范围，其可用于增加系统的动态范围；修改系统的天线类型，其可改变由天线束覆盖的范围；以及改变发射的信号的频率。在多种实施方式中，如在此引入其全部内容作为参考的美国临时申请第61/141,213号中所公开的，硬件设置可由软件自动选择、由用户手动设置、或对于不同的设置可结合自动与手动设置。In some embodiments, each antenna or antenna array of the receiver has a quadrature output. In some embodiments, a receiver may have multiple outputs with different analog filtering and/or amplification to isolate different information prior to digitization and digital signal processing. This is beneficial for improving the dynamic range of each physiological motion signal. For example, as disclosed in US Provisional Application No. 61/141,213, which is hereby incorporated by reference in its entirety, each baseband signal would be split to have different gains and filtering for heartbeat and respiration signals. In various implementations,
包括基于雷达的生理运动传感器的系统的多种实施方式，可包括有线或无线通信系统。多种实施方式可使用标准的或专有的通信协议，或两者的组合。该协议可包括来自TCP/IP网络模型的所有层的技术，包括但不限于，串口、USB、蓝牙、ZigBee、无线保真(Wi-Fi)、蜂窝(Cellular)、TCP/IP，以太网、简单对象访问(SOAP)等。例如，以太网可作为链路层协议，而TCP/IP用于路由，SOAP用作应用层协议。另一方面，只有使用以太网上的TCP/IP，不需要在应用层额外封装。此后，从雷达系统100收集的数据可进行格式化，并直接打包为TCP有效载荷。在某些实施方式中，这可包括用于数据收集时的时间戳、数据、以及数据质量指示器。该数据被附至TCP报头，然后成为IP的有效载荷。IP报头(地址)被附在该有效载荷，然后由链路层报头和报尾封装。最后，添加物理层报头和报尾并且通过以太网连接发送数据包。要从连接器访问数据，用户或客户端应具有程序以便在发送包的以太网连接上对指定端口进行监听。Various embodiments of systems including radar-based physiological motion sensors may include wired or wireless communication systems. Various implementations may use standard or proprietary communication protocols, or a combination of both. The protocol can include technologies from all layers of the TCP/IP network model, including, but not limited to, Serial, USB, Bluetooth, ZigBee, Wi-Fi, Cellular, TCP/IP, Ethernet, Simple Object Access (SOAP), etc. For example, Ethernet can be used as a link layer protocol, while TCP/IP is used for routing, and SOAP is used as an application layer protocol. On the other hand, only using TCP/IP over Ethernet, no additional encapsulation at the application layer is required. Thereafter, the data collected from the
在多种实施方式中，数字化的正交信号可通过使用多种算法进行处理，以提供呼吸和脉搏波形。In various embodiments, the digitized quadrature signals may be processed using various algorithms to provide respiration and pulse waveforms.
在多种实施方式中，正交信号可通过使用多种算法中的任何一种被解调，所述算法包括但不限于线性解调、基于弧的解调算法(例如利用中心跟踪的弧切线解调)或非线性解调算法。解调算法可包括但不限于以下方法，将复平面的信号映射在最佳拟合线上，将复平面中的信号映射在主特征向量上，或将信号弧校准为最佳拟合圆并使用圆的参数从信号弧中提取角距信息。线性解调可使用众多算法中的任何一种，包括将复平面上的信号映射在主特征向量上，或将复平面中的信号映射在最佳拟合线上。反正切解调可提取与此处所述的与心肺活动有关的胸部运动对应的相位信息。在正交系统中，通过位于圆上的两个正交通道(例如，同相(I)和正交相位(Q))收集数据，其中该圆以通道的直流向量为中心。跟踪相应圆的中心向量并将其从数据样本中减去之后，可通过反正切函数提取接收信号的相位信息。In various embodiments, quadrature signals may be demodulated using any of a variety of algorithms including, but not limited to, linear demodulation, arc-based demodulation algorithms (e.g., using center-tracked arc tangent demodulation) or nonlinear demodulation algorithm. Demodulation algorithms may include, but are not limited to, methods that map signals in the complex plane onto a best-fit line, map signals in the complex plane onto principal eigenvectors, or calibrate signal arcs to best-fit circles and Use the parameters of the circle to extract angular distance information from the signal arc. Linear demodulation can use any of a number of algorithms, including mapping a signal in the complex plane onto principal eigenvectors, or mapping a signal in the complex plane onto a line of best fit. Arctangent demodulation extracts phase information corresponding to chest motion as described herein in relation to cardiorespiratory activity. In a quadrature system, data is collected through two quadrature channels (eg, in-phase (I) and quadrature-phase (Q)) located on a circle centered on the channel's DC vector. After tracking the center vector of the corresponding circle and subtracting it from the data samples, the phase information of the received signal can be extracted by the arctangent function.
图9中示出的线性解调算法的实施方式将在下文中进一步说明。在一种实施方式中，如框901a所示，算法包括计算包括最近帧的输入帧的角标的协方差矩阵，以及如框902所示，将数据映射到主向量或所述协方差矩阵的特征向量上。如果确定当前的特征向量与先前确定的特征向量对比方向相反，则算法将当前向量旋转180度。The implementation of the linear demodulation algorithm shown in Fig. 9 will be further explained below. In one embodiment, the algorithm includes computing the covariance matrix of the subscripts of the input frames including the most recent frame, as indicated at
在多种实施方式中，线性解调算法包括下列步骤：In various embodiments, the linear demodulation algorithm includes the following steps:
1.计算当前输入帧x的协方差矩阵CM-1，如框901a所示。1. Compute the covariance matrix CM-1 of the current input frame x, as shown in
2.根据给定的公式2. According to the given formula
利用CM-1和先前帧的协方差矩阵C0至CM-2来计算A矩阵，如框901b所示。The A matrix is calculated using CM-1 and the covariance matrices C0 toCM-2 of the previous frame, as shown in block 901b.
其中α对应阻尼因子，并且可以是正实数。在多种实施方式中，α的值的范围大约从0.1到0.5。在一个实施方式中，α可以是0.2。M对应于缓存内的帧数并且范围从2到15。在一个实施方式中，M可以是10。where α corresponds to the damping factor and can be a positive real number. In various embodiments, the value of α ranges from approximately 0.1 to 0.5. In one embodiment, α may be 0.2. M corresponds to the number of frames in the cache and ranges from 2 to 15. In one embodiment, M may be 10.
3.找出对应于A的最大主值或特征向量的主向量或特征向量v0。3. Find the principal or eigenvectorv0 corresponding to the largest principal value or eigenvector of A.
4.计算v0和v1的内积，其中v1是当执行用于前一输入帧的算法时由步骤3得出的特征向量，如框901d所示。4. Compute the inner product of v0 and v1 , where v1 is the feature vector derived from
5.v0乘以步骤4得出的内积符号，如框901e所示。5. Multiply v0 by the sign of the inner product obtained in
6.估算在步骤5中计算出的特征向量v0上的当前输入帧x的样本以获取解调帧，如框902所示。6. Evaluate the samples of the current input frame x on the feature vector v0 calculated in
在多种实施方式中，许多不同的算法可单独使用或联合使用以从生理运动信号和周围噪音的混合中隔离不同的生理运动信号。这包括但不限于，如在此引入其全部内容作为参考的美国临时申请第61/141,213号中所公开的固定过滤器，如在此引入其全部内容作为参考的美国临时申请第61/141,213号中所公开的适合的过滤器，如在此引入其全部内容作为参考的美国临时申请第61/125，023号中所公开的匹配过滤器、微波、经验模式解调，如在此引入其全部内容作为参考的美国临时申请第61/141,213号中所公开的盲源分离，如在此引入其全部内容作为参考的美国临时申请第61/125,020号和第61/141,213号中所公开的波达方向(DOA)信息，如在此引入其全部内容作为参考的美国临时申请第61/141,213号中所公开的独立式成分分析，如在此引入其全部内容作为参考的美国临时申请第61/141,213号中所公开的智能天线，以及如在此引入其全部内容作为参考的美国临时申请第61/125,023号和第61/141,213号中所公开的经验模式解调。用来从混合信号中隔离心跳信号的一种实施方式首先提取呼吸信号，然后从混合信号中除去呼吸信号，然后过滤(固定或自适应过滤)其余的信号，以获取相对较小的心跳信号。用来隔离心跳信号的另一种实施方式是消除与最小均方误差估计混合的呼吸信号的谐波。In various embodiments, a number of different algorithms may be used alone or in combination to isolate different physiological motion signals from a mixture of physiological motion signals and ambient noise. This includes, but is not limited to, fixed filters as disclosed in U.S. Provisional Application No. 61/141,213, which is hereby incorporated by reference in its entirety, such as U.S. Provisional Application No. 61/141,213, which is hereby incorporated by reference in its entirety Suitable filters are disclosed in, as disclosed in U.S. Provisional Application No. 61/125,023, microwave, empirical mode demodulation, which is hereby incorporated by reference in its entirety Blind source separation as disclosed in U.S. Provisional Application No. 61/141,213, the contents of which are incorporated by reference, as disclosed in U.S. Provisional Application Nos. 61/125,020 and 61/141,213, the entire contents of which are incorporated herein by reference. Direction of Arrival (DOA) information, as disclosed in U.S. Provisional Application No. 61/141,213, which is hereby incorporated by reference in its entirety Freestanding component analysis, as in U.S. Provisional Application No. 61/141,213, which is hereby incorporated by reference in its entirety and empirical mode demodulation as disclosed in US Provisional Application Nos. 61/125,023 and 61/141,213, the entire contents of which are hereby incorporated by reference. One implementation to isolate the heartbeat signal from the mixed signal first extracts the respiration signal, then removes the respiration signal from the mixed signal, and then filters (fixed or adaptive filtering) the remaining signal to obtain a relatively small heartbeat signal. Another implementation to isolate the heartbeat signal is to eliminate the harmonics of the respiration signal mixed with the minimum mean square error estimate.
对于某些应用，重要的是确定呼吸或心跳的开始和结束，或确定每次呼吸或心跳的峰值，从而可计算呼吸到呼吸时间间隔或心跳到心跳的时间间隔。峰值探测包括寻找满足信号中多种限定特性的本地极大值和极小值。峰值探测存在的多种变化可用于该装置的多种实施方式中，包括但不限于，超过前阈值的极大值以及随后的低于阈值的极小值(在多种实施方式中，阈值可以是固定的或基于以往的峰值和谷值)；执行适合于峰值、谷值和/或过零之间的最小的二次方，并确定该函数的峰值(此方法提供插值)。在某些实施方式中，可在去除信号的基线变化之后进行上述算法。在某些实施方式中，峰值探测算法可包括寻找信号导数的过零。在某些实施方式中，也有可能通过选择正或负过零来使用过零估计每个呼吸周期的间隔。在某些实施方式中，谷值探测可取代峰值探测。For some applications, it is important to determine the start and end of a breath or heartbeat, or the peak value of each breath or heartbeat, so that breath-to-breath or heartbeat-to-beat intervals can be calculated. Peak detection involves finding local maxima and minima that satisfy various defined properties in a signal. There are many variations of peak detection that can be used in various embodiments of the device, including, but not limited to, a maximum above a threshold followed by a minimum below the threshold (in various embodiments, the threshold can be is fixed or based on past peaks and valleys); performs the least squares fit between peaks, valleys, and/or zero crossings, and determines the peak of the function (this method provides interpolation). In some embodiments, the algorithm described above can be performed after removing the baseline variation of the signal. In some embodiments, the peak detection algorithm may include looking for zero crossings of the derivative of the signal. In some embodiments, it is also possible to use zero crossings to estimate the interval of each respiratory cycle by selecting positive or negative zero crossings. In some embodiments, valley detection may replace peak detection.
对于某些应用程序，期望估计心肺信号的速率。在某些实施方式中，如在此引入其全部内容作为参考的美国临时申请第61/128,743号中所公开的，可在时域中使用峰值探测估计信号的速率，或如在此引入其全部内容作为参考的美国临时申请第61/141,213号中所公开的进行过零探测，并通过计算平均的峰值-峰值间隔或通过确定指定的时间周期内的峰值数来计算指定数量的峰值所需的时间。也可在频域内估计速率。这可作为短时傅里叶变换，通过使用可为预定时长或依赖信号的可变时长的窗口来计算。如在此引入其全部内容作为参考的美国临时申请第61/125,023号中所公开的，在应用经验模式解调后使用希尔伯特-黄变换计算的瞬时频率，呼吸速率也可在频域内计算。For some applications, it is desirable to estimate the rate of cardiopulmonary signals. In some embodiments, the rate of the signal may be estimated in the time domain using peak detection as disclosed in U.S. Provisional Application No. 61/128,743, which is incorporated herein by reference in its entirety, or as incorporated herein by reference in its entirety. The contents of which are disclosed in U.S. Provisional Application No. 61/141,213 by reference to perform zero-crossing detection and calculate the specified number of peaks by calculating the average peak-to-peak interval or by determining the number of peaks in a specified time period. time. Rates can also be estimated in the frequency domain. This can be computed as a Short Time Fourier Transform by using a window which can be of predetermined duration or variable duration depending on the signal. As disclosed in U.S. Provisional Application No. 61/125,023, which is hereby incorporated by reference in its entirety, the respiration rate can also be calculated in the frequency domain using the instantaneous frequency computed using the Hilbert-Huang transform after demodulation using an empirical mode calculate.
图10A中示出的频域速率估计算法的实施方式将在下文中说明。频域速率估计包括以下步骤：An implementation of the frequency domain rate estimation algorithm shown in Fig. 10A will be described below. Frequency domain rate estimation consists of the following steps:
1.收集解调数据x和非心肺运动或其它信号干扰探测事件的M个样本，如框1001a所示，其中M是用于速率估计的样本数，在多种实施方式中可以是1440，2880，4320或其它数。1. Collect M samples of demodulated data x and non-cardiopulmonary motion or other signal interference detection events, as shown in block 1001a, where M is the number of samples used for rate estimation, which can be 1440, 2880 in various embodiments , 4320 or other numbers.
2.将x中的非心肺运动或其它信号干扰的所有间隔设为0，如框1001b所示。2. Set all intervals in x that are not cardiopulmonary exercise or other signal interference to 0, as shown in block 1001b.
3.从x中减去x的平均值，如框1001c所示。3. Subtract the mean value of x from x, as shown in
4.按照如下确定使用频域信息的速率：4. Determine the rate at which frequency domain information is used as follows:
i.对x中的所有样本计算傅立叶变换(例如，离散傅立叶变换)以提供幅度谱(magnitude spectrum)，如框1001d所示。使用无窗(no windowing)、零填充或内插算法。在某些实施方式中，傅立叶变换可包括具有矩形窗口的短时快速傅里叶变换。i. Compute a Fourier transform (eg, a discrete Fourier transform) on all samples in x to provide a magnitude spectrum, as shown in
ii.速率的频域估计是x中的最大频率分量，如框1001e所示。在多种实施方式中，速率的频域估计可以是位于呼吸速率为6和呼吸速率为48之间的最大频率分量。ii. The frequency domain estimate of rate is the largest frequency component in x, as shown in
时域速率估计算法的实施方式将在下文中说明并在图10B中示出。时域速率估计包括以下步骤：An implementation of the time domain rate estimation algorithm will be described below and shown in Figure 10B. Time-domain rate estimation consists of the following steps:
1.收集解调数据x和非心肺运动或其它信号干扰探测事件的M个样本，如图10A的框1001a所示，其中M是用于速率估计的样本数，在多种实施方式中可以是1440，2880，4320或其它数。1. Collect M samples of demodulated data x and non-cardiopulmonary motion or other signal interference detection events, as shown in block 1001a of Figure 10A, where M is the number of samples used for rate estimation, which in various embodiments can be 1440, 2880, 4320 or other numbers.
2.将x中的非心肺运动或其它信号干扰的所有间隔设为0，如图10A的1001b所示。2. Set all intervals in x that are not cardiopulmonary exercise or other signal interference to 0, as shown in 1001b of Fig. 10A.
3.从x中减去x的平均值，如图10A的1001c所示。3. Subtract the mean value of x from x, as shown at 1001c in Figure 10A.
4.按照如下确定使用时域信息的速率：4. Determine the rate at which time domain information is used as follows:
(a)将zi设为样本的指数，以使x(zi)≤0并且x(zi+1)＞0，从而鉴别在输入帧中的正过零，如框1001f所示。在多种实施方式中，也可以鉴别出负过零。(a) Setzi to be the index of the sample such that x(zi ) ≤ 0 and x(zi+1 ) > 0 to identify positive zero crossings in the input frame, as shown in
(b)将ai设为间隔zi和zi+1的之间最大的幅度。(b) Set ai to be the largest amplitude between intervals zi and zi+1 .
(c)对于所有i设从而存在三个(在快速模式中是两个)不同数字i，j，k，其中(c) For all i set Thus there are three (two in fast mode) different numbers i, j, k, where
i)ai＞0.1Ai)ai >0.1A
ii)aj＞0.1Aii)aj > 0.1A
iii)ak＞0.1Aiii) ak > 0.1A
(d)如果在框1001g中确定不存在这样的A，则不能确定速率，如框1001h所示。(d) If it is determined in
(e)否则在间隔[zi，zi+1]内指示呼吸的一个时段gi＝1，并满足下列条件，如框1001j所示：(e) Otherwise indicate a period gi =1 of respiration within the interval [zi , zi+1 ], and satisfy the following conditions, as shown in
i)ai＞0.1Ai)ai >0.1A
ii)u(n)＝1其中zi＜n＜zi+1ii) u(n)=1 where zi <n< zi+1
iii)v(n)＝1其中zi＜n＜zi+1iii) v(n)=1 where zi <n< zi+1
其中u(n)和v(n)分别为运动窗和截取窗。where u(n) and v(n) are motion window and interception window respectively.
(f)否则gi＝0(f) otherwise gi =0
(g)将λ设为连续呼吸的最大数，其中gi＝1。也就是说，λ是使得gi，gi+1，gi+2，…gi+λ-1＝1的i中的最大数，如框1001j所示。(g) Let λ be the maximum number of consecutive breaths, where gi =1. That is, λ is the largest number in i such that gi , gi+1 , gi+2 , . . . gi+λ-1 =1, as shown in
(h)如果在框1001k中确定λ＜3(在快速模式中λ＜2)，则不能确定速率，否则给出每分钟的呼吸速率为(60×100×λ)/(zi+λ-zi)，如框1001m所示。(h) If it is determined in block 1001k that λ<3 (in fast mode λ<2), the rate cannot be determined, otherwise the breathing rate per minute is given as (60×100×λ)/(zi+λ − zi ), as shown in
在多种实施方式中，速率估计算法可同时使用频域估计和时域估计以确定呼吸速率，如图10C所示。同时使用两种方法的优点是双重的。首先，比较这两种方法的结果将有助于确定呼吸是否正常。其次，使用两种算法引入的冗余可有助于减小由于确定呼吸速率的不准确性带来的风险。例如，参考上述时域速率估计算法和频域速率估计算法的实施方式，如果算法确定所有的测量是由如框1001n所示的非心肺运动或其它信号的干扰组成，则报告错误信息。在某些实施方式中，如果两种算法估计的速率之间的差值大于4，如框1001p所示，则报告错误。在某些实施方式中，如果频域速率算法或时域速率算法估计的速率值小于6，则报告错误，如框1001q所示。在某些实施方式中，如果由频域速率算法或时域速率算法估计的速率值小于8或12，则报告错误，如框1001q所示。在某些实施方式中，如果由频域速率算法或时域速率算法估计的速率值大于48，则报告错误。在多种实施方式中，如果由频域速率算法或时域速率算法估计的速率值在范围12到48之间，则报告频域速率。在某些实施方式中，如果由频域速率算法或时域速率算法估计的速率值在范围8到48或6到48之间，则被视为正确。In various embodiments, a rate estimation algorithm may use both frequency domain estimates and time domain estimates to determine respiration rate, as shown in FIG. 10C . The advantages of using both approaches are twofold. First, comparing the results of the two methods will help determine if breathing is normal. Second, the redundancy introduced by using two algorithms may help reduce the risk due to inaccuracies in determining the respiration rate. For example, referring to the embodiments of the time domain rate estimation algorithm and the frequency domain rate estimation algorithm described above, if the algorithm determines that all measurements consist of non-cardiopulmonary motion or other signal interference as indicated by block 1001n, an error message is reported. In some embodiments, if the difference between the rates estimated by the two algorithms is greater than 4, as indicated by block 1001p, an error is reported. In some embodiments, if the rate value estimated by the frequency domain rate algorithm or the time domain rate algorithm is less than 6, an error is reported, as shown in block 1001q. In some embodiments, if the rate value estimated by the frequency domain rate algorithm or the time domain rate algorithm is less than 8 or 12, an error is reported, as indicated by block 1001q. In some embodiments, an error is reported if the rate value estimated by the frequency domain rate algorithm or the time domain rate algorithm is greater than 48. In various embodiments, the frequency domain rate is reported if the rate value estimated by the frequency domain rate algorithm or the time domain rate algorithm is in the
用于估计速率的峰值探测算法的实施方式将在下文进一步说明并由图10D示出。An embodiment of a peak detection algorithm for estimating velocity will be described further below and illustrated in Figure 10D.
1.收集解调数据x和非心肺运动或其它信号干扰探测事件的M个样本，如图10A的框1001a所示，其中M是用于速率估计的样本数，在多种实施方式中可以是1440，2880，4320或其它数。1. Collect M samples of demodulated data x and non-cardiopulmonary motion or other signal interference detection events, as shown in block 1001a of Figure 10A, where M is the number of samples used for rate estimation, which in various embodiments can be 1440, 2880, 4320 or other numbers.
2.将x中的非心肺运动或其它信号干扰的所有间隔设为0，如图10B的1001b所示。2. Set all intervals in x that are not cardiopulmonary exercise or other signal interference to 0, as shown in 1001b of Figure 10B.
3.从x中减去x的平均值，如图10C的1001c所示。3. Subtract the mean value of x from x, as shown at 1001c of Figure 10C.
4.按照如下得出时域估计速率：4. The time-domain estimated rate is derived as follows:
(a)将pv(n)设为“兴趣点”，如下：(a) Set pv(n) as "point of interest", as follows:
(I)|x(n)|＞|x(n-1)|且|x(n)|＞|x(n+1)|(I)|x(n)|＞|x(n-1)| and |x(n)|＞|x(n+1)|
(II)|x(n)|＝|x(n-1)|(II)|x(n)|=|x(n-1)|
(III)u(k)＝1其中n-τ≤k≤n+τ(III) u(k)=1 where n-τ≤k≤n+τ
(IV)v(k)＝1其中n-τ≤k≤n+τ(IV)v(k)=1 where n-τ≤k≤n+τ
其中u(k)和v(k)分别是运动窗和截取窗，如框1001s所示。where u(k) and v(k) are motion window and intercept window respectively, as shown in
(b)通过下列方法执行以长度为2W相邻的每个样本的非极大值抑制，如框1001t所示：(b) Perform non-maximum suppression of each sample adjacent to a length of 2W by the following method, as shown in block 1001t:
对于每个n，都有For each n, there are
其中γm＝pv(m)where γm =pv(m)
(c)通过使用下列公式将兴趣点分为峰值或谷值，如框1001u所示：(c) Classify the points of interest into peaks or valleys by using the following formula, as shown in
(d)由于呼吸信号具有交替的峰值和谷值，因此在框1001v中解析连续峰值和连续谷值。在多种实施方式中，可按如下方式进行：(d) Since the respiration signal has alternating peaks and valleys, consecutive peaks and consecutive valleys are resolved in
(i)当Δk使pvid(k)＜0且k1＜k＜k2时，pvid(k1)＞0，pvid(k2)＞0是连续的峰值。(i) When Δk satisfies pvid(k)<0 and k1 <k<k2 , pvid(k1 )>0 and pvid(k2 )>0 are continuous peaks.
(ii)对于具有相同极性的两个或多个连续的兴趣点，若兴趣点是峰值则仅保留最大值，若兴趣点是谷值则仅保留最小值。(ii) For two or more consecutive interest points with the same polarity, only the maximum value is retained if the interest point is a peak value, and only the minimum value is retained if the interest point is a valley value.
(iii)作为结果的兴趣点应具有交变的极性。(iii) The resulting points of interest should have alternating polarities.
(e)将λ设为序列中峰值的最大数。如果λ＜4(快速模式中λ＜3)则不能确定速率，否则由每分钟呼吸(60×100×λ)/L给定速率，其中L是第一和最后峰值之间的区间范围的长度。通过考虑谷值也可类似地确定速率。(e) Set λ to be the maximum number of peaks in the sequence. If λ < 4 (λ < 3 in fast mode) the rate cannot be determined, otherwise the rate is given by breaths per minute (60 x 100 x λ)/L, where L is the length of the interval range between the first and last peak . Rates can also be similarly determined by taking valleys into account.
在多种实施方式中，信号处理可确定吸气点和呼气点，并在时间上计算吸气点和呼气点。对于每一个数据块，可基于探测到的吸气或呼气事件计算和缓冲呼吸速率。在探测到连续吸气事件或呼气事件的指定数目(例如，3、5、10、15、20)之前，可存储速率。在某些实施方式中，默认值可以是3。在某些实施方式中，装置可返回或显示得到的吸气和呼气事件的中间值。在多种实施方式中，如在此引入其全部内容作为参考的美国临时申请第61/128,743号中所公开的，如果在读取的过程中探测到中断(例如，非生理运动或其它干扰信号)，将清除存储于缓存的任何呼吸速率值，且在中断停止之前不缓存任何值。In various embodiments, signal processing may determine inhalation and exhalation points and calculate inhalation and exhalation points in time. For each data block, a respiration rate may be calculated and buffered based on detected inspiratory or expiratory events. The rate may be stored until a specified number (eg, 3, 5, 10, 15, 20) of consecutive inspiratory or expiratory events are detected. In some implementations, the default value may be 3. In certain embodiments, the device may return or display the resulting median value for inspiratory and expiratory events. In various embodiments, as disclosed in U.S. Provisional Application Serial No. 61/128,743, which is hereby incorporated by reference in its entirety, if an interruption (e.g., non-physiological motion or other interfering signal) is detected during the reading ), will clear any breath rate values stored in the cache, and will not cache any values until the interrupt stops.
在多种实施方式中，如在此引入其全部内容作为参考的美国临时申请第61/128,743号中所公开的，还可计算基于每个吸气峰值-吸气峰值间隔的呼吸，而不是计算基于数据块的呼吸。在某些实施方式中，系统(例如，抽查监测器)可在显示呼吸速率之前测量指定数量的峰值，或测量指定的时间间隔。在多种实施方式中，如在此引入其全部内容作为参考的美国临时申请第61/204,880号中所公开的，如果测得的呼吸速率超过每分钟几次呼吸，则时间间隔或峰值数量可自动延长，以确保准确读取不规则速率。In various embodiments, instead of computing Block based breathing. In certain embodiments, the system (eg, a spot-check monitor) may measure a specified number of peaks, or measure a specified time interval, before displaying the respiration rate. In various embodiments, as disclosed in U.S. Provisional Application No. 61/204,880, which is hereby incorporated by reference in its entirety, if the measured respiration rate exceeds a few breaths per minute, the time interval or number of peaks may be Automatic extension to ensure accurate reading of irregular rates.
在某些实施方式中，如在此引入其全部内容作为参考的美国临时申请第61/128,743号和第61/137,532号中所公开的，包括基于雷达的生理运动传感器的抽样检查监测器可在显示速率之前测量指定数量的峰值。如在此引入其全部内容作为参考的美国临时申请第61/128,743号和第61/137,532号中所公开的，抽样检查监测器可测量对于某个时间间隔(如10秒、15秒、20秒、30秒、45秒、60秒或其它时间间隔)的用户可选择的峰值数(例如，3、5、10、15)。In certain embodiments, spot check monitors that include radar-based physiomotion sensors may be used at Measure the specified number of peaks before displaying the rate. As disclosed in U.S. Provisional Application Nos. 61/128,743 and 61/137,532, which are hereby incorporated by reference in their entirety, a spot check monitor may measure , 30 seconds, 45 seconds, 60 seconds, or other time interval) user-selectable number of peaks (eg, 3, 5, 10, 15).
在系统的多种实施方式中，如在此引入其全部内容作为参考的美国临时申请第61/128,743号中所公开的，如果呼吸是不规则的或超过每分钟几次，处理器可执行的软件可自动延长速率估计包括的时间间隔或峰值数。在某些实施方式中，如在此引入其全部内容作为参考的美国临时申请第61/128,743号中所公开的，如果在测量间隔中速率的变化性较低，则处理器可执行的软件只能提供呼吸速率。在某些实施方式中，如在此引入其全部内容作为参考的美国临时申请第61/128,743号中所公开的，处理器可执行的软件可提供变化性水平的指示。In various embodiments of the system, as disclosed in U.S. Provisional Application No. 61/128,743, which is hereby incorporated by reference in its entirety, if breathing is irregular or exceeds a few times per minute, the processor may execute The software can automatically extend the time interval or number of peaks included in the rate estimate. In some embodiments, the processor-executable software only Can provide breathing rate. In certain embodiments, processor executable software may provide an indication of the level of variability as disclosed in US Provisional Application No. 61/128,743, the entire contents of which are incorporated herein by reference.
在某些实施方式中，处理器可执行的软件可对信号质量进行评估，以防止显示不正确的速率。在多种实施方式中，评估可包括四个步骤。在多种实施方式中，第一步可采用非呼吸信号探测算法以抑制除呼吸外的运动信号的任何部分。第二步，处理器可执行的软件分别使用上述的时域法和频域法计算出呼吸速率，从而产生对于相同信号的两个呼吸速率。第三步包括比较由时域法和频域法得到的两个速率，并确定其是否接近呼吸数的某个量。在多种实施方式中，两个速率之间较小的差异可能意味着正常呼吸间隔和正常呼吸深度。在多种实施方式中，处理器可执行的软件可把正常呼吸间隔和正常呼吸深度视为两个信号的质量测量，这样能提供准确的速率。在多种实施方式中，第四步包括检查是否有位于呼吸速率预定的区间之外的速率，若有，在这种情况下，处理器可执行的软件不能提供速率。否则，在多种实施方式中，能以两个速率的平均值或简单选择其中任一速率来计算呼吸速率。In some embodiments, processor-executable software can evaluate signal quality to prevent incorrect rates from being displayed. In various embodiments, the assessment can include four steps. In various embodiments, the first step may employ a non-respiration signal detection algorithm to suppress any portion of the motion signal other than respiration. In the second step, the software executable by the processor calculates the respiration rate by using the above-mentioned time-domain method and frequency-domain method respectively, so as to generate two respiration rates for the same signal. The third step consists of comparing the two rates obtained by the time domain method and the frequency domain method and determining whether it is close to a certain amount of respiration rate. In various embodiments, a small difference between the two rates may imply a normal breathing interval and normal breathing depth. In various embodiments, processor-executable software can consider normal breath interval and normal breath depth as quality measures of the two signals, which can provide an accurate rate. In various embodiments, the fourth step includes checking whether there is a rate outside a predetermined interval of breathing rates, in which case the processor-executable software cannot provide the rate. Otherwise, in various embodiments, the respiration rate can be calculated as an average of the two rates, or a simple selection of either rate.
此处所述的多种实施方式，具有复合信号处理的多普勒雷达系统根据接收的基于目标运动的运动信号的复系数星座图可监视反常呼吸，其中目标运动包括胸部和腹部运动。复系数星座图是正交信号相对同相位信号的曲线图。在多种实施方式中，反常呼吸可成为阻碍呼吸、呼吸肌无力或呼吸衰竭的重要信号。反常呼吸也可能与某些类型的麻痹并发出现。反常呼吸中，腹部和肋骨向相反方向移动而不是向相同的方向移动，例如胸部扩张、腹部收缩，以及腹部扩张、肋骨收缩。In various embodiments described herein, a Doppler radar system with complex signal processing can monitor for abnormal respiration based on a constellation of complex coefficients of received motion signals based on the motion of a target, where the motion of the target includes chest and abdominal motion. A complex coefficient constellation is a plot of quadrature signals versus in-phase signals. In various embodiments, abnormal breathing can be an important sign of obstructed breathing, respiratory muscle weakness, or respiratory failure. Paradoxical breathing may also occur concurrently with certain types of paralysis. Paradoxical breathing in which the abdomen and ribs move in opposite directions instead of in the same direction, such as chest expanding, abdomen contracting, and abdomen expanding, ribs contracting.
阻塞性呼吸暂停通常定义为在持续的呼吸努力下出现至少10秒的气流信号幅度减少80-100％。当患者试图呼吸但气道阻塞时，肋骨和腹部可异相移动。多普勒雷达系统，例如上所述的正交多普勒雷达系统，可基于由于目标胸部和腹部运动产生的复系数星座图来监视这种反常呼吸。由于人的呼吸等生理信号与雷达载波信号相比是窄带信号(小于1KHz)，因此所有的反射信号将在相干载波信号上进行相位调整。因此，如果人体部位比如胸部和腹部同时扩张或收缩，从不同路径(从不同的身体部位反射)接收到反射信号只会使载波信号的相量改变而不是使相位调制窄带载波信号改变。在不同的身体部位以相同频率但不同的振幅或相位延迟移动时，如反常呼吸时，也可发生相位调制的窄带载波信号的相量改变。因此，在前者情况下，由于呼吸引起的基带的复合曲线图的形状不会改变，将形成一个圆(弧)，与来自单一源的情况类似，而在后一种情况下，基带信号的相量在周期运动的过程中(如呼吸)变化，导致复系数星座图变形。该事实可用来探测反常呼吸。在前一段中这两种情况的简化相量图在图11A和11B中示出，如在此引入其全部内容作为参考的美国临时申请第61/194,836号、第61/194,848号和第61/200,761号中所公开的。Obstructive apnea is usually defined as an 80-100% decrease in airflow signal amplitude for at least 10 seconds with sustained respiratory effort. When a patient tries to breathe but the airway is obstructed, the ribs and abdomen can move out of phase. A Doppler radar system, such as the quadrature Doppler radar system described above, can monitor for such abnormal breathing based on a constellation of complex coefficients due to the target's chest and abdomen motion. Since physiological signals such as human breathing are narrow-band signals (less than 1KHz) compared with the radar carrier signal, all reflected signals will be phase-adjusted on the coherent carrier signal. Therefore, if body parts such as the chest and abdomen expand or contract at the same time, receiving reflected signals from different paths (reflected from different body parts) will only change the phasor of the carrier signal and not the phase modulated narrowband carrier signal. Phasor changes of the phase-modulated narrow-band carrier signal can also occur when different body parts move at the same frequency but with different amplitudes or phase delays, such as in abnormal breathing. Therefore, in the former case, the shape of the composite graph of the baseband due to respiration will not change, and will form a circle (arc), similar to the case from a single source, while in the latter case, the phase of the baseband signal The quantity changes in the process of periodic motion (such as breathing), which causes the complex coefficient constellation to be deformed. This fact can be used to detect abnormal breathing. Simplified phasor diagrams for the two cases in the preceding paragraph are shown in FIGS. 200,761 disclosed.
图11A示出正常呼吸的相量图，图11B示出反常呼吸的相量图。在正常呼吸的过程中，当由虚线矢量代表的不同相位延迟载波信号叠加时只有载波信号的相量移位，而在反常呼吸的过程中，不仅是载波信号的相量发生改变而且基带信号的相量也改变了，因此导致图11A的不同形状的复系数星座图。FIG. 11A shows a phasor diagram for normal respiration, and FIG. 11B shows a phasor diagram for abnormal respiration. During normal respiration, only the phasor of the carrier signal shifts when different phase-delayed carrier signals represented by the dotted vectors are superimposed, while during abnormal respiration, not only the phasor of the carrier signal changes but also the phasor of the baseband signal The phasors are also changed, thus resulting in a differently shaped complex coefficient constellation of Fig. 11A.
在多种实施方式中，包括引起多普勒频移的运动的测量，其中多普勒移位与载波信号(＜＜1％)相比是窄带，来自同步源的多重反射不扭曲复合运动信号的形状，但由于具有不同时间延迟的反射的载波信号的破坏性或建设性的干扰，反射可改变信号的功率。在多种实施方式中，包括引起多普勒频移的运动的测量，其中多普勒频移与载波信号(＜＜1％)相比是窄带，来自同步源的多重反射不会导致复杂运动信号的扭曲，除非在与对应于心肺信号(＜1千赫兹)的频率的电气波长(＞300千米)可比(＞1％)的范围内发生多路径，其中心肺信号的频率是载波信号上的相位调制的频率。在多种实施方式中，由不同的身体部位反射的信号可作为引起多普勒频移的多路径信号，其中多普勒频移发生在具有非常窄的信号带和时间延迟远少于与相位调制频率的波长(＞300千米)对应的时间延迟的载波信号上，因此，只要所有的身体部位同时扩张和收缩，复系数的形状就不改变。然而，如果在身体不同部位的扩张和收缩运动之间存在时间延迟(或相位移)，例如反常呼吸，复系数星座图被扭曲，并成为椭圆或带状而不是小弧或线形。可通过比较两个主矢量的比率(例如特征向量)和在每个主向量上估算的信号的波幅来探测到反常呼吸。由公式给出的特定的价值函数可根据处理过的输出来鉴别反常呼吸事件，并提供反常呼吸的指示。In various embodiments, including the measurement of Doppler-induced motion, where the Doppler shift is narrowband compared to the carrier signal (<<1%), multiple reflections from synchronized sources do not distort the composite motion signal , but reflections can change the power of the signal due to destructive or constructive interference of the reflected carrier signal with different time delays. In various embodiments, including the measurement of Doppler-induced motion, where the Doppler shift is narrow-band compared to the carrier signal (<<1%), multiple reflections from synchronized sources do not cause complex motion Distortion of the signal, unless multipath occurs within a range comparable (>1%) to the electrical wavelength (>300 km) corresponding to the frequency of the cardiopulmonary signal (<1 kHz) on the carrier signal The frequency of the phase modulation. In various implementations, signals reflected by different body parts can act as multipath signals causing a Doppler shift, where the Doppler shift occurs in a The wavelength of the modulation frequency (>300 km) corresponds to a time-delayed carrier signal, so the shape of the complex coefficients does not change as long as all body parts expand and contract simultaneously. However, if there is a time delay (or phase shift) between the expansion and contraction movements of different parts of the body, such as abnormal respiration, the complex coefficient constellation is distorted and becomes an ellipse or ribbon instead of a small arc or line. Abnormal respiration can be detected by comparing the ratio of two principal vectors (eg, eigenvectors) with the amplitude of the signal estimated on each principal vector. A specific cost function given by the formula can identify abnormal breathing events from the processed output and provide an indication of abnormal breathing.
反常因子可按照如下计算：最大特征向量和第二大特征向量的比率乘以在主向量上估算的信号的最大波幅和在正交向量与主特征向量的比率上估算的信号的最大波幅的比率。价值函数可将反常因子转换为反常指标，其可用于指示反常呼吸。The anomaly factor can be calculated as follows: the ratio of the largest eigenvector to the second largest eigenvector multiplied by the ratio of the maximum amplitude of the signal estimated on the principal vector to the ratio of the maximum amplitude of the signal estimated on the ratio of the orthogonal vector to the principal eigenvector . A value function can convert an anomaly factor into an anomaly indicator, which can be used to indicate abnormal breathing.
价值函数的输入将作为反常因子，而价值函数将其转换为0和1之间的值。在某些实施方式中，价值函数可由以下给出的公式计算The input to the value function will be taken as an anomalous factor, and the value function will convert it to a value between 0 and 1. In some embodiments, the value function can be calculated by the formula given below
其中x1、x2是范围在0和1之间的反常因子，m和v是在反常和非反常之间的边界输入值，v是反常因子的强调因子。例如，如果m接近x1，则将反常指示阈值设为更低的反常因子。另一方面，如果反常因子变化，v将更显著地增加反常指示变化。如果反常指示接近1，则可能存在反常呼吸；如果反常指示接近0，则不可能存在反常呼吸。可在反常指示上设定阈值以提供是/否输出，或者，应用两个阈值以达到与可能反常呼吸、不确定输出以及不可能反常呼吸的绿-黄-红对应的输出。Among them, x1 and x2 are abnormal factors ranging between 0 and 1, m and v are boundary input values between abnormal and non-abnormal, and v is an emphasis factor of abnormal factors. For example, if m is close to x1, the abnormality indication threshold is set to a lower abnormality factor. On the other hand, if the anomalous factor changes, v will increase more significantly the anomalous indicator changes. If the abnormality indicator is close to 1, there may be abnormal breathing; if the abnormality indicator is close to 0, there is no possibility of abnormal breathing. Thresholds may be set on abnormal indications to provide a yes/no output, or two thresholds may be applied to achieve green-yellow-red outputs corresponding to possible abnormal breathing, indeterminate output, and unlikely abnormal breathing.
在本发明的一个实施方式中，m设为0.3而v设为0.04。图11C示出具有m和v的这些值的价值函数。In one embodiment of the present invention, m is set to 0.3 and v is set to 0.04. Figure 11C shows the cost function with these values of m and v.
图11D和11E示出了当身体部位存在同步身体扩张和收缩运动时的具有多路径延迟信号的基带输出，而图11F和11G示出了当身体部位以不同相位延迟扩张或收缩时的具有多路径延迟信号的基带输出。参照图11D和11E，图11D的参考数字1101示出运动信号(例如胸部位移信号)。由1102表示的曲线图示出基于复合信号的多路径。图11E的曲线图1103示出总的多路径信号。曲线图1104示出解调信号，其近似于线性，指示不存在异常呼吸(例如反常呼吸)。Figures 11D and 11E show the baseband output of a signal with multipath delays when there is simultaneous body expansion and contraction motion of the body part, while Figures 11F and 11G show the baseband output with multiple path delays when the body part expands or contracts with different phase delays. Baseband output for path-delayed signals. Referring to FIGS. 11D and 11E ,
参照图11F和图11G，图11F的参考数字1105示出运动信号(例如胸部位移信号)。由1106表示的曲线图示出基于复合信号的多路径。图11G的曲线图1107示出总的多路径信号。曲线图1108示出解调信号，其近似于线性，指示不存在异常呼吸(例如反常呼吸)。Referring to FIGS. 11F and 11G ,
在多种实施方式中，此处所述的基于雷达的生理运动传感器可探测非心肺信号或运动事件。在多种实施方式中，如在此引入其全部内容作为参考的美国临时申请第61/123,017号和第61/125,019号中所公开的，具有单一稳定源的信号可被看作心肺信号，而不稳定或有多个来源的信号可视为非心肺信号。在多种实施方式中，具有单一稳定的周期散射的信号可被认为是心肺信号，而不稳定或有多重散射的信号可被认为是包括非心肺运动或其它信号干扰。In various embodiments, the radar-based physiological motion sensors described herein can detect non-cardiopulmonary signals or motion events. In various embodiments, as disclosed in U.S. Provisional Application Nos. 61/123,017 and 61/125,019, which are hereby incorporated by reference in their entirety, a signal with a single stable source can be considered a cardiopulmonary signal, while Signals that are unstable or have multiple origins can be considered non-cardiopulmonary. In various embodiments, a signal with a single stable periodic scatter may be considered a cardiopulmonary signal, while a signal that is unstable or has multiple scatter may be considered to include non-cardiopulmonary motion or other signal interference.
在多种实施方式中，分析生理信号以确定信号的质量，其中信号分析包括但不限于，探测非心肺运动、探测高信号噪声比、探测低信号功率、探测射频干扰和探测信号截取。此外，可通过分析在复平面上的信号来测量信号质量，以确定有多少与弧或主向量相关的分散的的数据样本被污染。高质量的信号样本应非常接近弧或主向量，并显著偏离可表明低质量信号的弧或向量。在某些实施方式中，无论是在频域还是时域中，可基于阈值计算低信号切断。在某些实施方式中，根据由模拟数字转换器和基带电路的满量程电压提供的有效位数中可计算出低信号功率阈值。在某些实施方式中，如在此引入其全部内容作为参考的美国临时申请第61/141,213号中所公开的，当数字化电压超过最高值时，可触发截取指示。In various embodiments, the physiological signal is analyzed to determine the quality of the signal, wherein signal analysis includes, but is not limited to, detecting non-cardiopulmonary motion, detecting high signal-to-noise ratio, detecting low signal power, detecting radio frequency interference, and detecting signal interception. In addition, signal quality can be measured by analyzing the signal on the complex plane to determine how many scattered data samples associated with arcs or principal vectors are contaminated. High-quality signal samples should be very close to the arc or principal vector, and deviate significantly from the arc or vector that can indicate a low-quality signal. In some embodiments, the low signal cutoff may be calculated based on a threshold, whether in the frequency domain or the time domain. In some embodiments, the low signal power threshold can be calculated from the number of effective bits provided by the full-scale voltage of the analog-to-digital converter and the baseband circuit. In certain embodiments, a clipping indication may be triggered when the digitized voltage exceeds a maximum value, as disclosed in US Provisional Application No. 61/141,213, the entire contents of which are incorporated herein by reference.
在多种实施方式中，可以多种方式探测非心肺运动(例如，在主体周围的对象的运动或主体的物理移动)。例如，在某些实施方式中，比心肺运动(呼吸)引起的主体最大胸腔偏移大的偏移可以作为非心肺运动的指示。类似地，信号功率的显著增加可指示运动。In various implementations, non-cardiopulmonary motion (eg, motion of an object around the subject or physical movement of the subject) may be detected in a variety of ways. For example, in some embodiments, an excursion greater than the subject's maximum thoracic excursion induced by cardiopulmonary exercise (breathing) may be indicative of non-cardiopulmonary exercise. Similarly, a significant increase in signal power may indicate motion.
在线性解调适用的这些系统中，最佳向量、主向量或协方差矩阵的特征向量的显著改变可指示非心肺运动。最佳向量、主向量或特征向量是在其上的可估算信号的向量。最佳向量、主向量或特征向量的显著改变还可指示天线和主体之间的新关系，并进一步指示非心肺运动。可通过计算归一化的当前向量与归一化的先前向量的内积来探测最佳向量、主向量或特征向量的改变。如在此引入其全部内容作为参考的美国临时申请第61/141,213号中所公开的，如果内积低于阈值，则很可能存在非心肺运动。当使用线性解调时，特征值比率、或最佳向量的数据的均方根误差、或信号和最佳向量之间复系数星座图的均方根差值的显著改变，指示探测到的运动与指示非心肺运动或信号干扰存在的线不符合。In these systems where linear demodulation is applicable, significant changes in the optimal vector, principal vector, or eigenvectors of the covariance matrix may indicate non-cardiopulmonary exercise. An optimal vector, principal vector or eigenvector is a vector on which the signal can be estimated. Significant changes in the optimal, principal, or eigenvectors may also indicate a new relationship between the antenna and the subject, and further indicate non-cardiopulmonary motion. Changes in the best, principal, or eigenvectors can be detected by computing the inner product of the normalized current vector and the normalized previous vector. As disclosed in US Provisional Application No. 61/141,213, which is hereby incorporated by reference in its entirety, if the inner product is below a threshold, non-cardiopulmonary exercise is likely present. When linear demodulation is used, a significant change in the ratio of the eigenvalues, or the root mean square error of the data of the best vector, or the root mean square difference of the complex coefficient constellation between the signal and the best vector, indicates detected motion Not consistent with lines indicating non-cardiopulmonary exercise or the presence of signal interference.
当使用基于弧的解调时，原点位置、弧所在的圆的半径或弧在圆上位置的显著变化可指示天线和主体之间的关系，也可反过来指示非心肺运动的存在。如在此引入其全部内容作为参考的美国临时申请第61/141,213号中所公开的，在使用基于弧的解调的这些系统中，最佳弧的数据的均方根误差、或信号和最佳圆之间的复系数星座图的均方根差值的显著改变，指示非心肺运动信号或信号干扰。When arc-based demodulation is used, significant changes in the location of the origin, the radius of the circle in which the arc lies, or the position of the arc on the circle can indicate the relationship between the antenna and the subject, and vice versa, the presence of non-cardiopulmonary motion. As disclosed in U.S. Provisional Application No. 61/141,213, which is hereby incorporated by reference in its entirety, in these systems using arc-based demodulation, the root mean square error of the data for the best arc, or the signal sum A significant change in the root mean square difference of the complex coefficient constellation between the good circles indicates a non-cardiopulmonary signal or signal interference.
在多种实施方式中，可通过对来自复平面中的线或弧的信号偏移估计同样影响I和Q通道的噪声，包括热噪声和来自无线电干扰的某些类型的噪声，其中信号功率可由线或弧的长度计算。因此，如在此引入其全部内容作为参考的美国临时申请第61/141,213号中所公开的，信号与噪声的比率可进行估计，并可用作信号质量的指示。In various embodiments, noise that also affects the I and Q channels, including thermal noise and certain types of noise from radio interference, can be estimated by offsetting the signal from lines or arcs in the complex plane, where the signal power can be determined by Line or arc length calculations. Thus, the signal-to-noise ratio can be estimated and used as an indicator of signal quality as disclosed in US Provisional Application No. 61/141,213, the entire contents of which are incorporated herein by reference.
在多种实施方式中，如在此引入其全部内容作为参考的美国临时申请第61/123,017号中所公开的，当探测到运动或其它非呼吸信号时，装置无法显示呼吸速率。非心肺运动探测算法可用于某些实施方式，作为活动监测器运行。In various embodiments, the device fails to display respiration rate when motion or other non-respiration signals are detected, as disclosed in US Provisional Application Serial No. 61/123,017, the entire contents of which are incorporated herein by reference. Non-cardiopulmonary motion detection algorithms may be used in some embodiments to operate as an activity monitor.
非心肺运动探测算法的实施例将在下文中进一步说明并由图12A-12D示出。算法可由处理器执行，并用于通过监控特征向量方向的改变、特征向量的比率以及信号中能量的改变来探测非心肺运动或其它信号干扰，如框1201b所示。算法开始于模式1，如框1201a所示，通过假设不存在非心肺运动或其它信号干扰，一旦探测到任何非心肺运动和其它信号干扰时就转入到模式2，如框1201c所示。当运行在模式2下时，算法类似地检查特征向量的方向改变以及特征向量的比率，如框1201a所示，以确定非心肺运动或其它信号干扰是否停止。如果运动停止，则算法将得出没有运动的最早时间(回顾)，如框1201e所示。算法包括以下步骤：Embodiments of the non-cardiopulmonary motion detection algorithm are described further below and illustrated in FIGS. 12A-12D . Algorithms may be executed by a processor and used to detect non-cardiopulmonary motion or other signal disturbances by monitoring changes in direction of eigenvectors, ratios of eigenvectors, and changes in energy in the signal, as shown in block 1201b. The algorithm starts in
模式＝1mode=1
a.对当前输入帧xh2计算协方差矩阵CM-1，，其中当前输入帧xh2经过具有过滤函数h2的第一过滤器的过滤，如图12B的框1201f所示。在某些实施方式中，第一过滤器可以是低通过滤器。a. Calculate the covariance matrix CM-1 for the current input frame xh2 , where the current input frame xh2 is filtered by a first filter with a filter function h2 , as shown in block 1201f of FIG. 12B . In some embodiments, the first filter can be a low pass filter.
b.利用CM-1和前帧的协方差矩阵C0-CM-2计算A矩阵，b. Use CM-1 and the covariance matrix C0 -CM-2 of the previous frame to calculate the A matrix,
如图12B的框1201g所示，其中M是先前的帧数并在某些实施方式中可以是32。在多种实施方式中M可以大于或小于32。As shown in
c.找出对应于A的最大特征向量的特征向量v0，如图12B的框1201h所示。c. Find the eigenvectorv0 corresponding to the largest eigenvector of A, as shown in
d.计算v0和v1的内积绝对值chd，其中v1是当执行用于前一输入帧的算法时由前步骤c得出的特征向量，如图12B的1201i所示。d. Calculate the absolute value chd of the inner product of v0 and v1 , where v1 is the feature vector derived from the previous step c when performing the algorithm for the previous input frame, as shown in 1201i of Figure 12B.
e.计算最大特征向量和第二大特征向量的比率pc，如图12B的框1201j所示。e. Compute the ratio pc of the largest eigenvector and the second largest eigenvector, as shown in block 1201j of Figure 12B.
f.计算由具有过滤函数h3的第二过滤器过滤的输入帧x3的能量e1。在多种实施方式中，第二过滤器可以是高通过滤器，如图12B的框1201k所示。f. Compute the energy e1 of the input frame x3 filtered by the second filter with filter function h3. In various implementations, the second filter may be a high pass filter, as shown in
g.计算由h3过滤的所有M-1项前输入帧x3的平均能量e2，如图12B的框1201l所示。g. Compute the average energye2 of the input framex3 before all M-1 terms filtered by h3, as shown in block 1201l of Figure 12B.
h.计算比率detectp＝e1/e2，如图12B的框1201m所示。h. Calculate the ratio detectp = e1 /e2 , as shown in
i.如果(chd＜th1或pc＜thev1或detectp＞thp1)且detectp＞thp1d)，如框1201b和1201c所示，则探测到非心肺运动或其它信号干扰，转换到模式＝2。在多种实施方式中，th1值的范围可在大约0.6到大约1之间。在多种实施方式中，thev1的值的范围可在4到12之间。在多种实施方式中，thp1的值的范围可在4到20之间。在多种实施方式中，thp1d的值的范围可在大约0.1到大约0.8之间。i. If (chd<th1 or pc<thev1 or detectp>thp1) and detectp>thp1d), as shown in blocks 1201b and 1201c, then non-cardiopulmonary motion or other signal interference is detected, switch to mode=2. In various embodiments, the value of th1 can range from about 0.6 to about 1. In various embodiments, the value of thev1 can range between 4-12. In various embodiments, the value of thp1 can range between 4 and 20. In various embodiments, the value of thpld can range from about 0.1 to about 0.8.
模式＝2mode=2
a.由公式a. by the formula
计算矩阵A’，其中Ci是帧i的协方差矩阵(帧n是最近的帧)，如图12C的1201n所示。Calculate the matrix A', where Ci is the covariance matrix of frame i (frame n is the nearest frame), as shown in 1201n of Fig. 12C.
b.计算如下的特征向量的矩阵ρ，如图12C的框1201p所示：b. Compute the matrix p of eigenvectors as follows, as shown in
Forj＝0 To SeqMForj＝0 To SeqM
{{
For i＝0 To SeqMFor i＝0 To SeqM
{{
m＝M-(minM+i-1)m=M-(minM+i-1)
n＝M-jn=M-j
ρi，j＝vm，nρi,j =vm,n
}}
}}
其中，在某些实施方式中SeqM约为5，对应于前帧数，其中，在某些实施方式中，minM是当前帧的之前的帧数并大约为8，其中vm，n是与Am，n的最大特征向量对应的特征向量。Wherein, in some embodiments, SeqM is about 5, corresponding to the number of previous frames, wherein, in some embodiments, minM is the number of frames before the current frame and is about 8, where vm,n is the same as Am, the eigenvector corresponding to the largest eigenvector of n .
c.计算矩阵Ai，M-1的最大特征向量和第二大特征向量的比率pci，M-1，如图12C的框1201q所示。c. Compute the ratio pc i,M-1 of the largest eigenvector and the second largest eigenvector ofmatrix A i,M-1 , as shown in block 1201q of Figure 12C.
d.找出ρ中所有对vm，n的内积的绝对值的最小值chd，如图12C中的框1201r所示。d. Find the minimum value chd of the absolute value of all inner products with respect to vm,n in p, as shown in
e.计算能量比率
f.如果(chd＞th2且pcM-(minM-1)，M-1＞thev2)则非心肺运动或其它信号干扰停止，转换到模式＝1，如图12A的框1203d和1201e所示。在多种实施方式中，th2的值的范围可在大约0.6到1之间。在多种实施方式中，thev2的值的范围可在大约4到12之间。f. If (chd > th2 and pcM-(minM-1), M-1 > thev2) then non-cardiopulmonary exercise or other signal interference ceases, transition to mode = 1, as shown in blocks 1203d and 1201e of Figure 12A. In various embodiments, the value of th2 may range between approximately 0.6 and 1. In various embodiments, the value of thev2 can range between about 4-12.
g.计算如下四个指数idx1、idx2、idx3、idx4，如框1201t所示：g. Calculate the following four indices idx1, idx2, idx3, idx4, as shown in block 1201t:
i.idx1：i最大，使得
ii.idx2：i最大，使得
iii.idx3：i最大，使得pci，M-1＜thev2iii.idx3: i is the largest, making pci, M-1 <thev2
iv.idx4：i最大，使得σi＜thp2iv.idx4: i is the largest, making σi <thp2
在多种实施方式中，th3的值的范围可在约0.6到1之间。在多种实施方式中，thp2的值的范围可在大约4到12之间。在多种实施方式中，thp2的值可以是5。在某种实施方式中，th3可以约为0.97。In various embodiments, the value of th3 can range from about 0.6 to 1. In various embodiments, the value of thp2 can range between about 4-12. In various embodiments, the value of thp2 can be 5. In certain embodiments, th3 may be approximately 0.97.
h.当(idx1、idx2、idx3、idx4)之中出现最大帧指数时非心肺运动或其它信号干扰停止，如框1201u所示。h. Non-cardiopulmonary exercise or other signal interference stops when the maximum frame index occurs among (idx1, idx2, idx3, idx4), as shown in block 1201u.
在多种实施方式中，在对已解调信号应用速率估算算法之前，需要对三个信号的质量测量进行计算。首先，采用算法使具有非呼吸信号或干扰的已解调信号的样本的子集突显。其次，采用算法使功率低于阈值的已解调信号的样本的子集突显。再次，采用算法使经过削波的样本的子集突显。在多种实施方式中，速率估算算法还考虑由三个算法所确定的低质量样本并对它们进行标记，从而这些样本不会影响速率结果的精度。在多种实施方式中，速率估算算法只采用通过这些质量校验的样本，并且试图基于这些样本来产生速率。在多种实施方式中，速率估算算法可以将被标记的样本置零。如果标记过多样本，系统在这一期间将无法探测到足够数量的呼吸以用于时域速率估算，并将报错。在多种实施方式中，速率估算还利用其自身的质量校验测量。在多种实施方式中，速率估算算法是速率估算的时域方法和频域方法的速率结果的交叉校验。在多种实施方式中，如果由时域方法所确定的速率与频域方法所确定的速率的区别超过阈值，则该交叉校验失败。在多种实施方式中，如果交叉校验质量检查失败，速率估算将传达该失败的可能的原因。它将以如下顺序把失败的原因归结为所遇到的下列情况之一：低信号功率、信号削波、非呼吸信号或干扰。如果没有遇到这些情况，则速率估算的失败为一般错误。In various implementations, three signal quality measures need to be computed before applying the rate estimation algorithm to the demodulated signal. First, an algorithm is used to highlight a subset of samples with demodulated signals that are not breathing signals or interferers. Second, an algorithm is used to highlight a subset of samples of the demodulated signal whose power is below a threshold. Again, an algorithm is used to highlight a subset of the clipped samples. In various embodiments, the rate estimation algorithm also considers and flags low-quality samples determined by the three algorithms so that these samples do not affect the accuracy of the rate results. In various embodiments, the rate estimation algorithm only takes samples that pass these quality checks, and attempts to generate a rate based on these samples. In various implementations, the rate estimation algorithm may zero the marked samples. If too many samples are marked, the system will not be able to detect a sufficient number of breaths during this period for temporal rate estimation and will report an error. In various implementations, the rate estimate also utilizes its own quality check measure. In various embodiments, the rate estimation algorithm is a cross-check of the rate results of the time-domain and frequency-domain methods of rate estimation. In various embodiments, the cross-check fails if the rate determined by the time domain method differs from the rate determined by the frequency domain method by more than a threshold. In various implementations, if the cross-validation quality check fails, the rate estimate will communicate the probable cause of the failure. It will attribute the failure to one of the following conditions encountered, in the following order: low signal power, signal clipping, non-breathing signal, or interference. If these conditions are not encountered, the failure of the rate estimation is a general error.
在本系统的这些实施方式中，当根据圆弧估算圆的中心时，通过复平面中所观察的信号的相位是顺时针运动还是逆时针运动(相位是减少还是增加)能够区分吸气与呼气。对触发应用的某些实施方式、同步应用的某些实施方式以及需要计算吸气时间、呼气时间、或吸气与呼气的时间比的实施方式来说，吸气和呼气的区别是很重要的。某些应用的示例将得益于吸气和呼气之间的区别，其原因在于吸气/呼气的时间比包括但不限于监视慢性病、用于管理慢性病的生物反馈、以及紧张的生物反馈。In these embodiments of the system, when estimating the center of a circle from the arc, it is possible to distinguish between inhalation and exhalation by whether the phase of the observed signal in the complex plane moves clockwise or counterclockwise (decreases or increases in phase). gas. For some implementations of triggering applications, some implementations of simultaneous applications, and implementations requiring the calculation of inspiratory time, expiratory time, or the ratio of inhalation to exhalation time, the difference between inhalation and exhalation is very important. Examples of certain applications that would benefit from the distinction between inhalation and exhalation due to the inhalation/exhalation time ratio include but are not limited to monitoring chronic disease, biofeedback for management of chronic disease, and stress biofeedback .
在多种实施方式中，系统100能够进行自检，从而检查不正确的操作和/或环境干扰。在某些实施方式中，能够自动执行自检。在某些实施方式中，能够周期性地进行自测，从而确定硬件部分是否存在故障。在本系统的多种实施方式中，通过数字化控制系统多个部件的启动并分析特征，例如但不限于通道噪声等级、通道失衡和直流偏置，系统能够进行自测。虽然能够将自测结合为系统启动程序的一部分，但是在多种实施方式中，系统100需要来自中央控制器的指令来启动多种自测检查。除了硬件状态之外，通过将正常传输的射频(RF，radio frequency)功率与衰减传输的射频功率进行对比，系统能够进行射频干扰测试。这样可以确保所接收到的信号不是由产生类似心肺信号的其它传感器装置所产生的。In various implementations, the
图13示出了自测电路1300的框图。在多种实施方式中，自测电路包括吸收型单刀双掷开关(SPDT)1301和电压控制移相器1302。单刀双掷开关1301可用于选择传输通道1303，或者用于选择自测通道1304。设置在自测通道上的电压控制移相器产生人工信号，该信号通过0°功率分配器1306被输入至IQ解调器1305的射频输入端。在复系数星座图上，信号根据控制电压或者形成整个圆，或者形成部分圆弧。该曲线图可用于测试信号源、IQ失衡、外部干扰、基频信号调整以及数据采集。FIG. 13 shows a block diagram of a self-test circuit 1300 . In various embodiments, the self-test circuit includes an absorptive single pole double throw switch (SPDT) 1301 and a voltage controlled phase shifter 1302 . The SPDT switch 1301 can be used to select the transmission channel 1303 or to select the self-test channel 1304 . A voltage-controlled phase shifter set on the self-test channel generates an artificial signal, which is input to the RF input terminal of the IQ demodulator 1305 through the 0° power divider 1306 . On a complex coefficient constellation, the signals form either a full circle or a partial arc, depending on the control voltage. This graph can be used for testing signal sources, IQ imbalance, external interference, baseband signal adjustment, and data acquisition.
在多种实施方式中，用于执行波达方向算法的处理器可用于从空间上分离的非心肺运动中分离心肺运动，基于它们来自于天线的不同角度，如美国临时申请第61/125027号与第61/125020号所公开的，并且该申请的全部内容结合于此作为参考。在多种实施方式中，用于执行波达方向算法的处理器可以基于来自于天线的不同角度分离两个空间上分离的心肺运动。在多种实施方式中，用于执行波达方向算法的处理器可用于对相对于主体的角度进行跟踪。为了利用波达方向，基于雷达的生理运动传感器在各平面上包括至少两根天线，在该平面中，期望估算信号源的方向和/或分离空间上分离的运动，从而分离主体和消除非心肺运动。In various embodiments, a processor for performing a direction-of-arrival algorithm can be used to separate cardiopulmonary motion from spatially separated non-cardiopulmonary motion based on their different angles from the antenna, as described in U.S. Provisional Application No. 61/125027 61/125020, and the entire contents of that application are hereby incorporated by reference. In various implementations, the processor for performing the direction-of-arrival algorithm may separate two spatially separated cardiorespiratory movements based on different angles from the antennas. In various implementations, a processor for executing a direction-of-arrival algorithm may be used to track the angle relative to the subject. To take advantage of direction of arrival, radar-based physiological motion sensors include at least two antennas in each plane in which it is desired to estimate the direction of the signal source and/or separate spatially separated motion, thereby separating subjects and eliminating non-cardiopulmonary sports.
在多种实施方式中，为了确保波束能够覆盖所有可能位置上的主体，通常期望天线具有一个宽的波束宽度。然而，宽的波束宽度意味着远离主体的运动仍然可能位于天线的范围内，因此仍然可能对测量造成影响。在多种实施方式中，对来自多根接收天线的波达方向(DOA，direction of arrival)进行处理，可以提供一个宽的扫描角度来探测主体，并随后提供一个较窄的角度来测量主体的生理运动，从而避免了来自远离主体的运动的干扰。在某些实施方式中，可以把来自天线的信号当作天线阵列进行处理，其中天线阵列较之任何单独的天线具有更窄的波束宽度。通过处理，该阵列的波束可以被有效地导向期望的信号源，因此天线波束集中于信号源上，并且波束范围之外的任何运动将会根据天线方向图在该方向上衰减。此外，在多种实施方式中，可以探测并在界面上以角度或作更常见的方向指示(即向前、向左、或向右)呈现相对于目标主体的角度，从而有效地提供主体的跟踪。In various embodiments, it is generally desirable for the antenna to have a wide beamwidth in order to ensure that the beam can cover subjects at all possible locations. However, the wide beamwidth means that motion away from the subject can still be within range of the antenna and therefore still potentially affect the measurement. In various implementations, the direction of arrival (DOA) from multiple receive antennas is processed to provide a wide scan angle to detect the subject and subsequently provide a narrower angle to measure the subject's Physiological movement, thereby avoiding interference from movements far from the subject. In some embodiments, the signals from the antennas may be processed as an antenna array, where the antenna array has a narrower beamwidth than any individual antenna. Through processing, the beam of the array can be effectively directed toward the desired signal source, so that the antenna beam is focused on the signal source, and any motion outside the beam range will be attenuated in that direction according to the antenna pattern. Furthermore, in various embodiments, the angle relative to the target subject can be detected and presented on the interface either as an angle or, more generally, as a direction indication (i.e., forward, left, or right), effectively providing the subject's track.
在多种实施方式中，可以采用来自不同天线的信号对干扰源的角度进行探测和跟踪，并对来自该天线的信号进行组合，从而天线方向图在干扰运动的方向上存在零位，使得在存在空间上分离的运动的情况下，也能够对呼吸波形进行连续探测。多个DOA算法中的任何一个均可用于这项技术。这些方法可用于包括一个发射器和多个接收器天线的单输入多输出(SIMO)系统。DOA算法可用于包括多个位于不同频率的发射器和多个接收器的多输入多输出(MIMO)系统。其它改进型DOA算法包括但不限于多信号分级(MUSIC)或基于旋转不变性原理的信号参数估算技术(ESPRIT)，还可用于分离位于来自于天线的不同角度的信号源。In various embodiments, signals from different antennas can be used to detect and track the angle of the interference source, and the signals from the antennas can be combined, so that the antenna pattern has a null position in the direction of the interference movement, so that in Continuous detection of respiratory waveforms is also possible in the presence of spatially separated movements. Any of several DOA algorithms can be used for this technique. These methods can be used in single-input multiple-output (SIMO) systems that include one transmitter and multiple receiver antennas. DOA algorithms can be used in multiple-input multiple-output (MIMO) systems that include multiple transmitters at different frequencies and multiple receivers. Other improved DOA algorithms include but are not limited to Multiple Signal Classification (MUSIC) or Signal Parameter Estimation Technique Based on the Rotation Invariance Principle (ESPRIT), which can also be used to separate signal sources located at different angles from the antenna.
在多种实施方式中，可以采用DOA处理分离胸部和腹式呼吸，如美国临时申请第61/125020号所公开的，并且该申请的全部内容结合于此作为参考。在多种实施方式中，可以采用DOA处理将腿部运动从心肺运动中分离，使得在睡眠期间探测下肢不宁综合症成为可能。在多种实施方式中，可以通过一个使用DOA处理的装置对多个主体进行监视，如美国临时申请第61/194880号所公开的，并且该申请的全部内容结合于此作为参考。在多种实施方式中，多普勒雷达系统100能够对人体生理信号例如呼吸或心跳的波形进行监视，从而能够提取呼吸和心跳的速率。通过在系统中使用多根天线，可以实现对波达方向(DOA)的处理，从而能够探测目标的角方向。在多种实施方式中，可以基于DOA处理对多个目标的生理信号进行分离，其中DOA处理通过排列好的多普勒雷达获得。在多种实施方式中，对这些生理信号进行分离使得能够对各目标的波形进行分离，以用于演示或传输波形或提取速率。如果天线的视野内有多个人，则只要他们的角距离大于阵列的分辨率，同时天线的视野内的人数不多于平面中的天线和接收器，同时人和天线的共享少于天线和接收器的数量，就可以通过这个信号处理方案获得各人的呼吸速率。在某些实施方式中，多个天线能够以λ/2距离分离。在多种实施方式中，使用三根天线，可以同时对以约15°至20°分离的两个主体进行跟踪和监视。通过增加天线的数量，可以进一步缩短两个主体之间的角间距。In various embodiments, DOA treatment can be used to separate thoracic and abdominal respiration, as disclosed in US Provisional Application No. 61/125020, which is hereby incorporated by reference in its entirety. In various embodiments, DOA processing can be used to separate leg motion from cardiorespiratory motion, making it possible to detect restless leg syndrome during sleep. In various embodiments, multiple subjects may be monitored by one device using DOA processing, as disclosed in US Provisional Application No. 61/194880, the entire contents of which are hereby incorporated by reference. In various implementations, the
图14中示出了对多个心肺信号进行分离的方法的一种实施方式，该方法包括：An embodiment of a method for separating a plurality of cardiopulmonary signals is shown in FIG. 14 , the method comprising:
1.如框图1401a至1401d所示，该方法包括：确定最有可能包含心肺信号的缓存数据的频率分量f＝f1，f2，...，fn。在某些实施方式中，可以通过测量通道组合的功率频谱密度并对输出应用价值函数来确定这些频率分量。在某些实施方式中，可以通过从各接收器获得功率频谱密度并将它们叠加，从而得到组合频谱来确定通道组合的功率频谱密度。在某些实施方式中，在从各接收器获得功率频谱密度之前应用低通滤波器。在某些实施方式中，所述低通滤波器的截止频率为1Hz。1. As shown in block diagrams 1401a to 1401d, the method includes: determining frequency components f=f1, f2 , . In some embodiments, these frequency components may be determined by measuring the power spectral density of the channel combination and applying a cost function to the output. In some embodiments, the power spectral density of the channel combination can be determined by obtaining the power spectral densities from the receivers and adding them to obtain the combined spectrum. In some embodiments, a low pass filter is applied before obtaining the power spectral density from each receiver. In some embodiments, the cut-off frequency of the low-pass filter is 1 Hz.
2.如框图1402所示，该方法还包括：识别各频率分量的角方向。在某些实施方式中，通过构造通道矩阵H来识别角频率分量，并使用该通道矩阵和数组向量来计算各角度上的最大平均功率，其中矩阵H的入口对应于步骤1中所发现的最有可能包含心肺信号的频率分量，数组向量对应于来自目标的各角度。在某些实施方式中，通道矩阵入口的第m行和第n列可以为hmn＝smn(fn)，对应于接收器天线m和移动散射体，其中smn代表该通道的频谱。在某些实施方式中，构成对应于来自目标的各角度的数组向量。在某些实施方式中，数组向量由等式(1)给出：2. As shown in block diagram 1402, the method further includes: identifying the angular direction of each frequency component. In some embodiments, the angular frequency components are identified by constructing a channel matrix H, and the maximum average power at each angle is calculated using the channel matrix and the array vector, where the entry of the matrix H corresponds to the maximum value found in
g(θ)＝[1 exp[jkd sin(θ)]...exp[jkd(M-1)sin(θ)]]T (1)g(θ)=[1 exp[jkd sin(θ)]...exp[jkd(M-1)sin(θ)]]T (1)
其中k为波数，d＝λ/2为各接收器天线之间的分离距离，θ为从天线法向量到目标的角度，而M为已接收天线的数量。在某些实施方式中，在散射体的各角度上所获得的最大平均功率由等式(2)给出：where k is the wavenumber, d = λ/2 is the separation distance between each receiver antenna, θ is the angle from the antenna normal vector to the target, and M is the number of received antennas. In some embodiments, the maximum average power achieved at each angle of the scatterer is given by equation (2):
Pav(θ)＝|HHg(θ)|2 (2)Pav (θ)＝|HH g(θ)|2 (2)
3.如框图1403a和1403b所示，该方法还包括：将彼此间距小于某一个角距离的角度消除，其中该角距离小于多个接收器天线阵列的角分辨率；以及识别至少第一角方向和第二角方向，从而使各角方向通过某一个角距离相互间隔，其中该角距离大于或等于所述天线阵列的角分辨率。4.为所述角方向上的各目标产生具有单位模值的DOA向量。在多种实施方式中，构造M×N阵列的矩阵A，其第i列由等式(3)给出：3. As shown in
g(θi)＝[1exp[jkd sin(θi)]...exp[jkd(M-1)sin(θi)]]T (3)g(θi )=[1exp[jkd sin(θi )]...exp[jkd(M-1)sin(θi )]]T (3)
其中d＝λ/2和θ分别为接收天线的间距和角度，而M为已接收天线的数量。在这些实施方式中，在主体附近存在着其它移动物体，该物体能够散射雷达信号并以某一个角距离分离，其中该角距离大于多个接收器天线阵列的角分辨率，N代表移动散射体的数量。where d=λ/2 and θ are the spacing and angle of the receiving antennas, respectively, and M is the number of received antennas. In these embodiments, there are other moving objects in the vicinity of the subject that can scatter the radar signal and are separated by an angular distance greater than the angular resolution of the multiple receiver antenna arrays, where N is the moving scatterer quantity.
4.在多种实施方式中，如框图1405所示，采用当前DOA向量的加权平均数和缓存中的先前的DOA向量对DOA向量进行平滑处理。4. In various implementations, as shown in block diagram 1405, the DOA vector is smoothed using the weighted average of the current DOA vector and previous DOA vectors in the cache.
5.通过将空间上的零位导向其它角方向来分离来自各角方向的信号，如框图1404所示。在多种实施方式中，通过对已调整过的通道数据应用矩阵A的逆矩阵来进行估算，将空间上的零位导向不需要的信号源，从而实现信号的分离。5. Separate the signals from each angular direction by directing spatial nulls to other angular directions, as shown in
S＝A-1Rx (4)S = A-1 Rx (4)
6.在多种实施方式中，对各个已分离的输出采用非心肺运动探测器，如果探测到非心肺运动，则清空DOA向量的缓存。6. In various embodiments, use non-cardiopulmonary motion detectors for each separated output, and clear the buffer of DOA vectors if non-cardiopulmonary motion is detected.
7.在多种实施方式中，对各个已分离信号进行单独解调，并对各信号进行处理，从而获得对应于心肺运动的信息。7. In various embodiments, each separated signal is individually demodulated and each signal is processed to obtain information corresponding to cardiopulmonary exercise.
8.输出位于相对于各目标的角度上的至少一个与该目标有关的心肺运动信息。8. Outputting at least one cardiorespiratory information related to each target at an angle relative to the target.
图15示出了来自两个目标的呼吸信号的分离。曲线图1501示出了采用DOA处理分离的混合基频信号。曲线图1502示出了来自第一主体或信号源的呼吸信号，曲线图1503示出了来自第二信号源或主体的呼吸信号。在多种实施方式中，包括执行DOA处理系统的体佩式识别标签可以用于帮助识别和增强目标主体的测量效果，如美国临时申请第61/200876号所公开的，并且该申请的全部内容结合于此作为参考。Figure 15 shows the separation of respiration signals from two targets.
可以选择性地对两种不同信号进行分离和分析，在多种实施方式中，系统100可以采用DOA算法来跟踪单个期望的心肺信号，而无效掉一个或多个不期望的心肺信号或非心肺信号。在某些实施方式中，可以通过射频识别(RFID)标签对期望主体进行跟踪。在某些实施方式中，可以通过生物识别技术对期望主体进行跟踪。在某些实施方式中，可以基于已知的初始位置对期望主体进行跟踪。在这种情况下，只对期望的信号进行解调，并且只输出与期望目标相关的角度信息和/或心肺信息。系统100的多种实施方式可以包括DOA处理算法，以便对主体或患者进行跟踪，如美国临时申请第61/125020号和第61/194836号所公开的，所述申请的全部内容结合于此作为参考。例如，在某些实施方式中，虽然主体在睡眠中辗转反侧，但可以采用DOA处理整晚对睡眠中的主体进行跟踪。The two different signals can optionally be separated and analyzed. In various embodiments, the
如图16所示，下文描述了跟踪一个或多个心肺信号的方向的算法的一种实施方式，该算法包括：As shown in Figure 16, one embodiment of an algorithm for tracking the direction of one or more cardiopulmonary signals is described below, the algorithm comprising:
1.如框图1601a至1601c所示，该方法包括：确定最有可能包含心肺信号的缓存数据的频率分量f＝f1，f2，...，fn。在某些实施方式中，可以通过测量通道组合的功率频谱密度并对输出应用价值函数来确定这些频率分量。在某些实施方式中，可以通过从各接收器获得功率频谱密度并将它们叠加，从而得到组合频谱来确定通道组合的功率频谱密度。在某些实施方式中，在从各接收器获得功率频谱密度之前应用低通滤波器。在某些实施方式中，所述低通滤波器的截止频率为1Hz。1. As shown in block diagrams 1601a to 1601c, the method includes: determining frequency components f=f1, f2 , . In some embodiments, these frequency components may be determined by measuring the power spectral density of the channel combination and applying a cost function to the output. In some embodiments, the power spectral density of the channel combination can be determined by obtaining the power spectral densities from the receivers and adding them to obtain the combined spectrum. In some embodiments, a low pass filter is applied before obtaining the power spectral density from each receiver. In some embodiments, the cut-off frequency of the low-pass filter is 1 Hz.
2.如步骤1601d所示，该方法还包括：识别各频率分量的角方向。在某些实施方式中，通过构造通道矩阵H来识别角频率分量，并使用该通道矩阵和数组向量来计算各角度上的最大平均功率，其中矩阵H的入口对应于步骤1中所发现的最有可能包含心肺信号的频率分量，数组向量对应于来自目标的各角度。在某些实施方式中，通道矩阵入口的第m行和第n列可以为hmn＝smn(fn)，对应于接收器天线m和移动散射体，其中smn代表该通道的频谱。在某些实施方式中，构成对应于来自目标的各角度的数组向量。在某些实施方式中，数组向量由等式(1)给出：2. As shown in step 1601d, the method further includes: identifying the angular direction of each frequency component. In some embodiments, the angular frequency components are identified by constructing a channel matrix H, and the maximum average power at each angle is calculated using the channel matrix and the array vector, where the entry of the matrix H corresponds to the maximum value found in
g(θ)＝[1 exp[jkd sin(θ)]...exp[jkd(M-1)sin(θ)]]T (1)g(θ)=[1 exp[jkd sin(θ)]...exp[jkd(M-1)sin(θ)]]T (1)
其中k为波数，d＝λ/2为各接收器天线之间的分离距离，θ为从天线法向量到目标的角度，而M为已接收天线的数量。在某些实施方式中，在散射体的各角度上所获得的最大平均功率由等式(2)给出：where k is the wavenumber, d = λ/2 is the separation distance between each receiver antenna, θ is the angle from the antenna normal vector to the target, and M is the number of received antennas. In some embodiments, the maximum average power achieved at each angle of the scatterer is given by equation (2):
Pav(θ)＝|HHg(θ)|2 (2)Pav (θ)＝|HH g(θ)|2 (2)
3.如框图1604e所示，该方法还包括：将彼此间距小于某一个角距离的角度消除，其中该角距离小于多个接收器天线阵列的角分辨率；以及识别至少第一角方向和第二角方向，从而使各角方向以某一个角距离相互间隔，其中该角距离大于或等于所述天线阵列的角分辨率。3. As shown in block 1604e, the method further comprises: eliminating angles that are less than an angular distance from each other, wherein the angular distance is less than an angular resolution of the plurality of receiver antenna arrays; and identifying at least a first angular direction and a second angular direction two angular directions such that the angular directions are spaced apart from each other by an angular distance greater than or equal to the angular resolution of the antenna array.
4.为所述角方向上的各目标产生具有单位模值的DOA向量。在多种实施方式中，如框图1601f所示，构造M×N阵列的矩阵A，其第i列由等式(3)给出：4. Generate a DOA vector with unit modulus for each target in the angular direction. In various embodiments, as shown in block diagram 1601f, a matrix A of M×N arrays is constructed, the ith column of which is given by equation (3):
g(θi)＝[1 exp[jkd sin(θi)]...exp[jkd(M-1)sin(θi)]]T (3)g(θi )=[1 exp[jkd sin(θi )]...exp[jkd(M-1)sin(θi )]]T (3)
其中d＝λ/2和θ分别为接收天线的间距和角度，而M为已接收天线的数量。在这些实施方式中，在主体附近存在着其它移动物体，该物体能够散射雷达信号并以某一个角距离分离，其中该角距离大于多个接收器天线阵列的角分辨率，N代表移动散射体的数量。where d=λ/2 and θ are the spacing and angle of the receiving antennas, respectively, and M is the number of received antennas. In these embodiments, there are other moving objects in the vicinity of the subject that can scatter the radar signal and are separated by an angular distance greater than the angular resolution of the multiple receiver antenna arrays, where N is the moving scatterer quantity.
5.在多种实施方式中，如框图1601g所示，采用当前DOA向量的加权平均数和缓存中的先前的DOA向量对DOA向量进行平滑处理。5. In various implementations, as shown in block diagram 1601g, the DOA vector is smoothed using the weighted average of the current DOA vector and previous DOA vectors in the cache.
6.通过将空间上的零位导向其它角方向来分离来自各角方向的信号。在多种实施方式中，通过对已调整过的通道数据应用矩阵A的逆矩阵来进行估算，将空间上的零位导向不需要的信号源，从而实现信号的分离。6. Separation of signals from angular directions by directing spatial nulls to other angular directions. In various embodiments, signal separation is achieved by applying the inverse of matrix A to the adjusted channel data for estimation, directing spatial nulls to unwanted signal sources.
S＝A-1Rx (4)S = A-1 Rx (4)
7.在多种实施方式中，对各个已分离的输出采用非心肺运动探测器，如果探测到非心肺运动，则清空DOA向量的缓存。7. In various embodiments, use a non-cardiopulmonary motion detector for each separated output, and clear the buffer of DOA vectors if non-cardiopulmonary motion is detected.
8.在多种实施方式中，对各个已分离信号进行单独解调，并对各信号进行处理，从而获得对应于心肺运动的信息。8. In various embodiments, each separated signal is individually demodulated and each signal is processed to obtain information corresponding to cardiopulmonary exercise.
9.输出位于相对于各目标的角度上的至少一个与该目标有关的心肺运动信息，如框图1601j所示。9. Output at least one cardiorespiratory information related to each target at an angle relative to the target, as shown in
在多种实施方式中，如全部内容结合于此作为参考的美国临时申请第61/125023号所公开的，可以采用经验模态分解(EMD)算法从运动中分离信号其包括但不限于：由主体的非心肺运动、预期主体以外的一个或多个人的心肺运动、周围环境中的其它物体的运动、雷达系统的运动所引起的运动。In various embodiments, as disclosed in U.S. Provisional Application No. 61/125023, which is hereby incorporated by reference in its entirety, an Empirical Mode Decomposition (EMD) algorithm may be employed to separate signals from motion, including but not limited to: Non-cardiopulmonary motion of the subject, cardiopulmonary motion of one or more persons other than the intended subject, motion of other objects in the surrounding environment, motion induced by motion of a radar system.
系统100的多种实施方式可以包括经验模态分解和波达方向处理的组合，如美国临时申请第61/125027号所公开的，并且该申请的全部内容结合于此作为参考。在某些实施方式中，可以采用DOA处理来分离出现在不同角度的运动信号。随后可以采用EMD处理从非心肺运动和DOA处理后所遗留的其它信号的干扰中提取期望的生理运动信号。多种实施方式可以包括用于执行运动补偿算法的处理器。运动补偿可以通过心肺信号来抑制由身体其它部分或天线视野内的其它人的运动所引起的干扰。心肺信号可以在低频率范围内，例如从几Hz至几kHz甚至包括谐波，而其它非心肺运动由于运动的更快因而具有宽频带；例如，一个脉冲响应可以包括全部频率分量。在某些实施方式中，运动补偿算法可以将低通滤波和高通滤波型式的数据或信号分离，并找出高通滤波后的数据或信号的至少两个主向量(例如，主特征向量)。可以将包含心肺信号的低通滤波后的数据或信号投射在由高通滤波后的信号的这些主向量所产生的正交子空间上。这个子空间可以包含减少的或最低的运动干扰。当使用多个空间上分离的天线时，这种方法能够提供更高精度的呼吸信号的相关信息。Various implementations of the
降低噪声可以通过滤波来实现，其中滤波器使处于生理频带之上的信号通过，并且使处于该频带之外的信号衰减。Noise reduction can be achieved by filtering, where the filter passes signals above the physiological frequency band and attenuates signals outside this frequency band.
由于心肺信号具有低频率分量，故可以采用过采样和求平均值的方法配合廉价的数据采集装置来降低噪声。通过过采样，可以通过对N个样本求平均值来使基频信号上的无关噪声功率(例如加性高斯白噪声(AWGN))降低1/N，在保持同样的信号功率的同时，过采样和求平均值法所产生的信噪比(SNR)是奈奎斯特(Nyquist)采样的N倍。Since cardiopulmonary signals have low-frequency components, oversampling and averaging methods can be used with cheap data acquisition devices to reduce noise. Through oversampling, the irrelevant noise power (such as additive white Gaussian noise (AWGN)) on the fundamental frequency signal can be reduced by 1/N by averaging N samples, while maintaining the same signal power, oversampling The signal-to-noise ratio (SNR) produced by the sum averaging method is N times that of Nyquist sampling.
可以通过执行经验模态分解，并选择包含生理信号的一个或多个模态，并且仅使用这些模态来对信号进行重组，从而实现噪声的降低。经验模态分解算法适应性地将信号分离至本质模态函数(IMF)中，从而捕获信号中的最重要信息，其中本质模态函数是基于数据中的最高能本征时间范围适应性地产生的。本质模态函数具有定义明确的希尔伯特变换。该经验模态分解算法可用于对被设计为测量主体的心肺运动的雷达的数字化输出进行处理。可以通过EMD算法对雷达信号的正交输出进行处理，其中EMD算法包括至少一个二变量EMD、复数EMD、或旋转变体EMD。可以将I通道和Q通道的本质模态函数与线性或非线性解调算法相结合。随后可以从包含运动信号的本质模态函数而非包含噪声的本质模态函数中构造运动信号，从而有效地降低了噪声，如美国临时申请第61/125023号所公开的，并且该申请的全部内容结合于此作为参考。Noise reduction can be achieved by performing empirical mode decomposition, and selecting one or more modes that contain the physiological signal, and using only these modes to recombine the signal. The Empirical Mode Decomposition algorithm captures the most important information in the signal by adaptively separating the signal into intrinsic mode functions (IMFs), where the IMF is adaptively generated based on the highest energy intrinsic time range in the data of. The intrinsic mode functions have well-defined Hilbert transforms. The empirical mode decomposition algorithm may be used to process the digitized output of a radar designed to measure cardiorespiratory motion of a subject. The quadrature output of the radar signal may be processed by an EMD algorithm comprising at least one of bivariate EMD, complex EMD, or rotational variant EMD. The intrinsic mode functions of the I and Q channels can be combined with linear or nonlinear demodulation algorithms. The motion signal can then be constructed from the EMFs containing the motion signal instead of the EMFs containing the noise, effectively reducing the noise, as disclosed in U.S. Provisional Application No. 61/125023, and the entirety of which The contents are hereby incorporated by reference.
可以采用下述的多种方式对包括基于雷达的生理传感器的系统100进行配置。The
一个示例性系统配置可以包括抽查监测器，该抽查监测器可以被配置为单片或双片系统并适于工作在2.4GHz。系统100还包括单根天线、直接变频器或同差接收器、以及高通滤波器。系统100还可以包括处理器，该处理器被配置为采用上述线性解调算法对信号进行处理。在多种实施方式中，处理器还可以被配置为采用一个或多个速率查找算法估算速率(例如呼吸速率、心跳速率等)。An exemplary system configuration may include a spot-check monitor, which may be configured as a single-chip or two-chip system and adapted to operate at 2.4 GHz.
如上所述，在多种实施方式中，监测器可以包括同差接收器。在多种实施方式中，采用同差接收器是由于其具有简单和消除相位噪声的特性。在多种实施方式中，为了在下变频RF信号之后清除位于基频的镜像，系统包括提供正交模拟输出的复解调。在多种实施方式中，采用2×2阵列的微带天线以便获得聚焦束。在多种其它实施方式中，可以采用更小或更大阵列的微带天线或单根(非阵列)微带天线。例如，可以在阵列中使用更多天线以便获得更多聚焦束。在多种其它实施方式中，可以采用其它(非阵列)天线配置。在多种实施方式中，可以对正交输出进行抗混叠滤波，并且采用高通滤波器移除直流信号。可以通过模数转换器(ADC)对滤波后的信号进行采样，随后在处理器中对数字化后的数据进行处理。在某些实施方式中，对生理运动信号进行分析，从而确定该信号是否由于噪声、干扰、和/或非生理运动的原因而具有低质量。在某些实施方式中，将生理运动信号从噪声、干扰、和/或非生理运动中分离。随后对生理运动信号进行处理，从而确定呼吸波形和呼吸速率。在某些实施方式中，从呼吸速率波形中提取呼吸速率。As noted above, in various embodiments, a monitor may include a homodyne receiver. In various embodiments, a homodyne receiver is used due to its simplicity and phase noise cancellation properties. In various embodiments, to clean up images at the fundamental frequency after downconverting the RF signal, the system includes complex demodulation that provides a quadrature analog output. In various embodiments, a 2x2 array of microstrip antennas is employed in order to obtain a focused beam. In various other embodiments, smaller or larger arrays of microstrip antennas or single (non-array) microstrip antennas may be used. For example, more antennas can be used in the array in order to obtain more focused beams. In various other embodiments, other (non-array) antenna configurations may be employed. In various implementations, the quadrature output can be anti-aliased filtered, and a high-pass filter can be used to remove the DC signal. The filtered signal can be sampled by an analog-to-digital converter (ADC), and the digitized data subsequently processed in a processor. In some embodiments, the physiological motion signal is analyzed to determine whether the signal is of low quality due to noise, interference, and/or non-physiological motion. In some embodiments, physiological motion signals are separated from noise, interference, and/or non-physiological motion. The physiological motion signal is then processed to determine the respiratory waveform and respiratory rate. In some embodiments, the respiration rate is extracted from the respiration rate waveform.
图17示出了被配置为呼吸速率抽查测量装置的系统100的一种实施方式。图17中所示的装置包括电磁辐射信号源1701(例如压控振荡器)以及收发器1702。在某些实施方式中，收发器1702可以包括单根天线以用于发射和接收信号。接收自散射辐射并运动的所述一个或多个物体的信号穿过功率分配器1703被导向至少一个混合器1704。在某些实施方式中，功率分配器可以是2通道0°功率分配器。在多种实施方式中，可以在混合器中将来自信号源1701的信号与接收到的信号进行混合。在多种实施方式中，系统100可以包括两个混合器(例如1704和1705)，该混合器能够输出同相和正交相分量。通过数据采集系统(DAQ或DAS)1706对来自混合器的输出信号进行调整和采样。在多种实施方式中，可以对信号进行调整以移除混叠信号，例如采用低通滤波器。在多种实施方式中，可以对信号进行调整，例如通过高通滤波器、低通滤波器、直流消除器、放大器等。数字采集系统1706可以包括多路复用器、模数转换器(ADC)、数模转换器(DAC)、计时器、缓存器等。可以将数字采集系统1706的输出传输至计算机或处理器，用于进一步的信号处理。在某些实施方式中，计算机或处理器可以与输出单元进行电子通信，该输出单元被配置为基于信号处理之后获得的信息来执行输出动作。例如，在某些实施方式中，输出单元可以包括显示用的显示单元。在某些实施方式中，输出单元可以包括打印用的打印机，或用于发出报警声的音响系统，或用于报出呼吸信息的音响系统，或利用该信息的医疗设备(例如除颤器)，或从多种医疗设备收集信息并将该信息传送至中央数据库的家用保健设备，或将该信息传送至远处的医疗保健工作者的健康数据亭计算机。在某些实施方式中，计算机或处理器可以与输入单元进行电子通信，该输入单元可以是开始按钮或健康数据亭计算机，允许远处的保健工作者启动测量，或者是用于启动测量的家用保健设备。FIG. 17 illustrates one embodiment of a
在多种实施方式中，或者以保持一定间隔的方式对身体表面与心肺相关的运动进行测量，或者以接触身体表面的方式对上述运动进行测量。在这些实施方式中，当天线接触身体时，可以将表面反射从内部反射中分离，从而能够对身体内部的运动进行测量。还可以对身体表面和内部的部分和组织进行多种内部心肺相关变化的电磁测量，其中包括与心跳相关的阻抗变化。In various embodiments, cardiorespiratory related movement of the body surface is measured either at intervals or in contact with the body surface. In these embodiments, when the antenna is in contact with the body, surface reflections can be separated from internal reflections, enabling measurements of motion inside the body. Electromagnetic measurements of a variety of internal cardiorespiratory-related changes, including changes in impedance associated with the heartbeat, can also be performed on parts and tissues on the surface and inside of the body.
图18示出了呼吸速率抽查器的一种实施方式。该系统包括类似于上述多种实施方式的基于雷达的生理传感器1801、计算单元、以及显示单元。在多种实施方式中，可以将计算单元和显示单元集成在单个壳体1802中(例如笔记本电脑、手持式电脑、PDA等)。传感器1801可以采用上述多种通信协议与计算单元和/或显示单元进行无线或有线通信。在多种实施方式中，可以将传感器1801、计算单元和显示单元集成在单个壳体中。在特定实施方式中，可以将传感器1801和计算单元集成在单个壳体中，而显示单元则是分离式的。Figure 18 shows one embodiment of a breath rate spotter. The system includes a radar-based
在多种实施方式中，当开始按钮被触发时，抽查监测器开始工作。在多种实施方式中，在工作模式下，监测器可以开始测量生理运动信号。在多种实施方式中，用户可以选择三个模式中的一个模式：快速模式、加强模式、或连续模式。三个模式中的每个模式在提供速率之前均需要不同数量的无运动连续呼吸。例如，在快速模式下，大致需要2次无运动连续呼吸来计算速率，在加强模式下，大致需要6次无运动连续呼吸来计算速率，而在普通模式下，大致需要3次连续呼吸来计算速率。In various implementations, the spot check monitor is activated when the start button is activated. In various embodiments, in the working mode, the monitor can begin to measure physiological motion signals. In various implementations, the user can select one of three modes: fast mode, intensive mode, or continuous mode. Each of the three modes requires a different number of consecutive breaths without motion before providing a rate. For example, in fast mode, it takes approximately 2 consecutive breaths without exercise to calculate the rate, in boost mode, it takes approximately 6 continuous breaths without exercise to calculate the rate, and in normal mode, it takes approximately 3 consecutive breaths to calculate the rate rate.
图19示出了界面(例如显示屏)的一种实施方式，该界面用于输出心肺或心血管的相关信息(例如呼吸速率、呼吸波形、心跳速率、脉搏等)。图19所示的实施方式是显示器的屏幕截图，上面显示有测量到的呼吸速率。在多种实施方式中，信号处理单元(例如图18的计算单元)能够确定主体的吸气峰值点并采用一种或多种算法在时间上对该点进行计数。在多种实施方式中，系统100可以缓存每个呼吸速率数据块。在多种实施方式中，如果在读数期间探测到中断(例如由于非心肺运动或其它信号干扰的原因而产生的中断)，缓存中的任何呼吸速率值将被清空，并且直至中断结束为止，都不再对任何值进行缓存。一旦连续读取到大致所需的呼吸数量后，该装置返回所记录的中间值，从而确保读数尽可能的精确。在某些实施方式中，所需的呼吸数量可以是3。在多种实施方式中，所需的呼吸数量可以是5、10、15、20或3-30范围内的其它值。在多种实施方式中，界面可以具有用于显示状态的状态指示器1901。例如，状态指示器1901可以为条形，该条形可以随着每次连续呼吸的读取而升高。一旦读取到所需数量的呼吸后，状态指示器就能停止升高。可以在显示器的区域1902中对测量到的呼吸速率进行显示。在多种实施方式中，可以在用于控制系统的界面上设置控制器。例如，可以在图19所示的显示界面上设置开始按钮1903和停止按钮1904。在多种实施方式中，如果停止按钮被触发，则可以中断测量，在这种情况下不返回任何值。Fig. 19 shows an embodiment of an interface (such as a display screen), which is used to output cardiopulmonary or cardiovascular related information (such as breathing rate, breathing waveform, heart rate, pulse, etc.). The embodiment shown in Figure 19 is a screenshot of a display showing the measured respiration rate. In various embodiments, a signal processing unit (eg, the computing unit of FIG. 18 ) is capable of determining a subject's peak inspiratory point and counting that point in time using one or more algorithms. In various implementations,
在系统的多种实施方式中，可以采用速率估算算法来确定呼吸速率，其中该算法运用了两种处理，例如运用时域方法和/或频域方法来确定呼吸速率：频域估算和时域估算。应用两种方法的第一个优点是：将两种方法所产生的结果进行对比，可以有助于确定呼吸是否有规律。第二个优点是：通过应用两种算法而引入的冗余可以有助于降低出现错误呼吸速率的风险。在多种实施方式中，时域速率估算采用信号中的具有正或负斜率的零交叉来识别呼吸。将两个连续的正的零交叉或两个连续的零交叉之间的信号峰值与阈值进行对比，从而确定两个连续的零交叉是否确实包括呼吸。在某些实施方式中，采用正的零交叉，如果用于计算速率的呼吸不够时，负的零交叉也会被采用。此外，对所有样本进行傅里叶变换的计算以提供信号频谱。在多种实施方式中，速率的频域估算可以是信号中的最大值频率分量。可以将时域和频域速率估算进行对比。在多种实施方式中，两种结果之间的区别可以表示信号不符合时域方法或频域方法的假设的程度。例如，区别程度为0可以表示在时域和频域方法之间的完美匹配。在多种实施方式中，频域计算可以作为从时域方法获得的测量结果的交叉校验，反之亦然。在多种实施方式中，两种速率可以作为精度的交叉校验。频域和时域计算之间的不匹配还可以表示可能的不规则呼吸。本装置的多种实施方式可以期望呼吸速率具有低变化率以提供测量或读数，从而确保测量或读数的精确性。在某些实施方式中，系统可以显示或通过其它方式传送被测速率的变化率等级，即，速率在测量间隔期间发生了多少变化。保健专业人员可以将被测速率的变化用于医学分析。In various embodiments of the system, a rate estimation algorithm may be used to determine the respiration rate, wherein the algorithm employs two processes, for example, a time domain method and/or a frequency domain method to determine the respiration rate: frequency domain estimation and time domain estimate. The first advantage of using both methods is that comparing the results produced by the two methods can help determine whether breathing is regular. A second advantage is that the redundancy introduced by applying both algorithms can help reduce the risk of erroneous breathing rates. In various embodiments, the temporal rate estimation uses zero crossings in the signal with positive or negative slopes to identify respiration. Two consecutive positive zero crossings or the signal peak between two consecutive zero crossings are compared to a threshold to determine whether the two consecutive zero crossings indeed include a breath. In some embodiments, positive zero crossings are used, and negative zero crossings are also used if insufficient breaths are used to calculate the rate. In addition, a calculation of Fourier transform is performed on all samples to provide the signal spectrum. In various implementations, the frequency domain estimate of velocity may be the maximum frequency component in the signal. Time domain and frequency domain rate estimates can be compared. In various implementations, the difference between the two results may indicate the extent to which the signal does not meet the assumptions of either the time domain method or the frequency domain method. For example, a degree of discrimination of 0 may indicate a perfect match between time domain and frequency domain methods. In various embodiments, frequency domain calculations may serve as a cross-check for measurements obtained from time domain methods, and vice versa. In various implementations, the two rates may serve as a cross-check for accuracy. A mismatch between frequency domain and time domain calculations can also indicate possible irregular breathing. Various embodiments of the device may desire a low rate of change in respiration rate to provide measurements or readings to ensure the accuracy of the measurements or readings. In some embodiments, the system may display or otherwise communicate the rate-of-change grade of the measured velocity, ie, how much the velocity has changed during the measurement interval. Changes in the measured rate can be used for medical analysis by healthcare professionals.
图20示出了显示装置的屏幕截图。显示装置与系统100进行通信，如上所述，该系统同时采用时域方法和频域方法对呼吸速率进行计算。系统100能够以固定周期执行测量，固定周期的范围在约15秒至1分钟之间。例如，在某些实施方式中，在快速模式下，系统100能够以15秒的时间间隔执行测量，在普通模式下，系统100能够以30秒的时间间隔执行测量，在加强模式下，系统100能够以60秒的时间间隔执行测量。当对呼吸全程进行计数以估算呼吸速率时，这些时间间隔对应于保健工作者的常用间隔。在其它实施方式中，三个模式的时间间隔可以不同。状态指示器2001可以指示测量已经用去的时间以及测量的剩余时间。在某些实施方式中，显示器还可以具有控制按钮2002，该按钮允许用户选择工作模式(例如快速、普通或加强)。可以在显示器上设置其它控制器，例如开始按钮2003和停止按钮2004，从而对系统进行控制。在某些实施方式中，显示器还能够提供系统的状态指示。例如，在图20中，显示器指示计算单元的电源和电池电量的状态。在某些实施方式中，还可以显示先前测量到的速率。在某些实施方式中，还可以包括清空按钮2005，用于从屏幕上清除已显示过的呼吸速率。在多种实施方式中，还可以在显示装置上显示呼吸速率估算中的误差，例如由于存在非心肺运动或其它信号干扰而引起的误差。Fig. 20 shows a screenshot of the display device. The display device is in communication with the
图21示出了系统100的另一种实施方式，该系统包括集成在单个壳体2102中的传感器2101、计算单元和显示单元。FIG. 21 shows another embodiment of a
在多种实施方式中，对在测量间隔期间所获得的全部数据应用上述速率估算算法。在多种实施方式中，速率估算算法可以探测非呼吸信号(例如非心肺信号或其它信号干扰)，并利用该信息鉴定信号质量。可以丢弃信号质量差的数据样本。例如，非心肺运动或其它信号干扰可能导致样本的偏移大于主体的最大呼吸，因此可以丢弃该样本。在某些实施方式中，非心肺运动也可能导致样本的信号功率表现出显著的增加，因此，可以丢弃该样本。在某些实施方式中，可以采用上述非心肺运动探测算法来探测非呼吸信号或其它信号干扰。在多种实施方式中，信号质量指示的附加输入可以包括低信号功率、高信号功率所导致的信号削波、以及估算出的低信噪比。在多种实施方式中，在通过速率估算进行处理之前，可以将由于信号质量差而被丢弃的值置零。In various embodiments, the rate estimation algorithm described above is applied to all data acquired during the measurement interval. In various embodiments, a rate estimation algorithm may detect non-respiratory signals (eg, non-cardiopulmonary signals or other signal interference) and use this information to identify signal quality. Data samples with poor signal quality can be discarded. For example, non-cardiopulmonary exercise or other signal disturbances may cause a sample to be shifted greater than the subject's maximum respiration, so the sample may be discarded. In some embodiments, non-cardiopulmonary exercise may also cause a sample to exhibit a significant increase in signal power, and therefore, the sample may be discarded. In some embodiments, the non-cardiopulmonary motion detection algorithm described above may be used to detect non-breathing signals or other signal interference. In various embodiments, additional inputs for signal quality indicators may include low signal power, signal clipping due to high signal power, and estimated low signal-to-noise ratio. In various implementations, values discarded due to poor signal quality may be zeroed prior to processing by rate estimation.
如上所述，在多种实施方式中，时域速率估算采用信号中的具有正或负斜率的零交叉来识别呼吸。将两个连续的正的零交叉或两个连续的零交叉之间的信号峰值与阈值进行对比，从而确定两个连续的零交叉是否确实包括呼吸。在某些实施方式中，采用正的零交叉，如果用于计算速率的呼吸不够时，负的零交叉也会被采用。此外，对所有样本进行傅里叶变换的计算以提供信号频谱。在多种实施方式中，速率的频域估算可以是信号中的最大值频率分量。可以将时域和频域速率估算进行对比并确定所估算速率的精度。As noted above, in various embodiments, temporal rate estimation uses zero crossings in the signal with positive or negative slopes to identify respiration. Two consecutive positive zero crossings or the signal peak between two consecutive zero crossings are compared to a threshold to determine whether the two consecutive zero crossings indeed include a breath. In some embodiments, positive zero crossings are used, and negative zero crossings are also used if insufficient breaths are used to calculate the rate. In addition, a calculation of Fourier transform is performed on all samples to provide the signal spectrum. In various implementations, the frequency domain estimate of velocity may be the maximum frequency component in the signal. Time domain and frequency domain rate estimates can be compared and the accuracy of the estimated rate can be determined.
在采用线性解调算法以解调样本的系统(例如采用2.4GHz ISM频段的系统)的多种实施方式中，在其上投射有信号的最佳拟合向量或主特征向量的显著变化可以指示天线和主体之间的新的关系，还可以指示非心肺运动或信号干扰的存在。当采用线性解调时，特征值或拟合最佳拟合线的均方根误差的比例的变化还可以指示：所探测到的运动不能很好地符合该线，因此表明非心肺运动或其它信号干扰的存在。In various embodiments of a system employing a linear demodulation algorithm to demodulate samples, such as a system employing the 2.4GHz ISM band, a significant change in the best-fit vector or principal eigenvector on which the signal is projected may indicate The new relationship between the antenna and the subject can also indicate the presence of non-cardiopulmonary exercise or signal interference. When linear demodulation is used, changes in the eigenvalues or in the ratio of the root mean square error to the line of best fit can also indicate that the detected motion does not fit the line well, thus indicating non-cardiopulmonary motion or other The presence of signal interference.
上述呼吸速率抽查测量装置的多种实施方式可以适合于用在健康亭中。参考图17至21所描述的抽查测量装置可以与一个或多个主控系统进行通信，从而使一个或多个主控系统可以对抽查监测器进行控制。系统的多种实施方式可以通过以下方式启动测量：通过至少一个本地操作人员按压装置上的按钮，通过医疗保健系统工作者的远程动作，通过在亭中探测到患者存在时的自动启动。该装置的多种实施方式可以在亭中探测患者的存在，并且将该信息传送至亭中的计算机。该装置的多种实施方式可以从其它传感器获取通过亭中的计算机所传递的输入，该计算机表明亭中有患者存在。系统100的多种实施方式可以采用任何标准或专有通信协议或二者的组合来与一个或多个主控系统进行通信。这些协议可以包括任何通讯技术并且可以属于TCP/IP或OSI网络层，也可以不属于TCP/IP或OSI网络层，其中所述通讯技术包括但不限于串行通信、USB、蓝牙、Zigbee、Wi-Fi、蜂窝技术、WiMax、以太网和SOAP。例如，可以将以太网用作链路层协议，而将TCP/IP用于路由，并且将SOAP用作应用层协议。在另一方面，仅采用以太网上的TCP/IP，而不需要在应用层额外封装。在后一种情况下，可以对从雷达系统100收集到的数据进行格式编排并直接打包为TCP有效载荷。该载荷可以包括何时收集数据的时间戳、数据、数据质量指示器。该数据被附至TCP报头并随后成为IP有效载荷。IP报头(地址)被附在该有效载荷，并随后通过链路层报头和报尾封装。最后，附加上物理层报头和报尾并且经由以太网连接发送该包。为了从该连接获取数据，客户端应具有程序，以便在发送包的以太网连接上对指定端口进行监听。系统100的多种实施方式可以遵照康体佳健康联盟的医疗设备通信指南，该指南包括经由USB或蓝牙的控制和通信。Various embodiments of the breath rate spot-check measurement device described above may be suitable for use in a health kiosk. The spot-check measurement devices described with reference to FIGS. 17 to 21 can communicate with one or more host control systems so that the one or more host control systems can control the spot-check monitors. Various embodiments of the system may initiate measurements by at least one local operator pressing a button on the device, by remote action by a healthcare system worker, by automatic initiation upon detection of the patient's presence in the kiosk. Various embodiments of the device may detect the presence of a patient in a kiosk and transmit this information to a computer in the kiosk. Various embodiments of the device may take input from other sensors communicated by a computer in the kiosk indicating the presence of a patient in the kiosk. Various implementations of
系统100的一种示例性配置可以包括抽查监测器，该抽查监测器在多种实施方式中被配置为单片或双片系统，并适于工作在约5.8GHz的无线电频率。系统100的多种实施方式可以包括直流消除电路，用于减少运动信号与该运动的电子指示之间的延迟。在多种实施方式中，直流消除电路可以加快运动传感器与输出装置(例如显示器或成像系统)之间的同步。直流消除器或5.8GHz的低中频可以使圆弧解调相对更加精确。直流消除器通常能够改善同步时间，所以在成像系统或通气装置上集成直流消除器是很重要的。An exemplary configuration of
与采用2.4GHz范围的无线电频率的实施方式相比，在采用5.8GHz范围的无线电频率的实施方式中，由与心肺活动相关的胸部运动导致的相位偏差可能增加两倍以上。在多种实施方式中，这种现象可以导致非线性基频输出，从而复系数星座图更加近似于圆弧而非直线。在这些实施方式中，相对于其它解调算法，可以优选基于圆弧的解调算法。在多种实施方式中，基于圆弧的解调算法可以通过合理地解决这种非线性影响以提供更加精确的结果。在多种实施方式中，由于直流消除器可以减少信号失真，所以相对于交流耦合放大器，可以优选直流消除器。在不具有直流消除器的实施方式中，不能足够精确地确定散布有信号样本的圆的原点。Phase deviations caused by chest motions associated with cardiorespiratory activity may be more than doubled in embodiments employing radio frequencies in the 5.8 GHz range compared to embodiments employing radio frequencies in the 2.4 GHz range. In various implementations, this phenomenon can result in a non-linear fundamental frequency output such that the complex coefficient constellation more closely resembles a circular arc than a straight line. In these embodiments, arc-based demodulation algorithms may be preferred over other demodulation algorithms. In various embodiments, arc-based demodulation algorithms can provide more accurate results by properly addressing this non-linear effect. In various embodiments, a DC canceller may be preferred over an AC-coupled amplifier because the DC canceller can reduce signal distortion. In implementations without a DC canceller, the origin of the circle interspersed with signal samples cannot be determined with sufficient accuracy.
当采用反正切解调时，原点的位置的显著变化，或开放圆弧所在的圆的半径的变化，或圆上的圆弧位置的变化可以指示天线和主体之间的关系变化，还可以指示非心肺运动或信号干扰的存在。在某些实施方式中，如果计算出的归一化的当前向量与归一化的先前向量的内积的在阈值之下，则可以探测主体和天线之间的关系变化。在采用反正切解调的系统中，拟合最佳拟合圆弧的均方根误差的变化还可以指示非心肺运动或其它信号干扰。When arctangent demodulation is employed, a significant change in the position of the origin, or a change in the radius of the circle on which the open arc lies, or a change in the position of an arc on a circle can indicate a change in the relationship between the antenna and the subject, and can also indicate Presence of non-cardiopulmonary exercise or signal interference. In some embodiments, a change in the relationship between the subject and the antenna may be detected if the calculated inner product of the normalized current vector and the normalized previous vector is below a threshold. In systems employing arctangent demodulation, changes in the root mean square error of the best fit arc may also indicate non-cardiopulmonary motion or other signal disturbances.
系统100的一种示例性配置可以包括连续生理监测器，该监测器工作在约2.4GHz范围的频率，并且被配置为双片系统。连续生理监测器用于持续提供生命体征信息和/或生理波形，而不仅仅提供周期性快照。连续生命体征监测器的多种实施方式是可配置的，以便能够以抽查或连续模式工作。监测器的多种实施方式可以用来监视至少一个心跳波形和变量以及呼吸波形和变量。监测器的多种实施方式可以包括单根天线或通过结合而作为单根天线工作的天线阵列、直接变频器或同差接收器、以及高通滤波器。在多种实施方式中，可以采用多根天线。监测器可以包括其它电子元件，例如滤波器、放大器、多路复用器等。在多种实施方式中，系统100可以包括处理器，用于执行基于特征向量的线性解调算法、或基于圆弧的解调算法、或其它上述算法。在某些实施方式中，系统100可以用来确定心跳速率和/或呼吸速率。An exemplary configuration of
图17中所示的系统适合作为连续生命体征监测器而工作。图17中所示的系统是具有同差接收器的连续波雷达收发器。这种配置的一个优点在于系统的简易性，另一个优点在于其消除或降低相位噪声的能力。在多种实施方式中，收发器1702可以工作在2.4GHz-2.5GHz或5.8GHz ISM频段。在多种实施方式中，收发器可以工作在上述频段之外的频率范围内。在多种实施方式中，信号源1701可以用来产生接收器的传输信号和本机振荡信号。该配置可称为内部压控振荡器。在多种实施方式中，振荡器可以是自由运转的，并且被锁相至晶振，或被锁相至外部参考点。在其它实施方式中，可以在剩余电路外部产生本机振荡器。在多种实施方式中，可以采用复解调来产生正交输出。该技术的一个优点是在下变频RF信号之后消除位于基频的镜像。在多个实施方式中，该技术的另一个优点是采用线性或非线性复解调算法来避免相位解调零位的能力，该零位可以损害用于该应用的单个混合器接收器。在某些实施方式中，可以在模数转换之前对正交输出进行放大和抗混叠滤波。在多种实施方式中，为了提高动态范围，可以采用高通滤波器移除直流偏置，并且可以使用变量增益放大器(VGA)来确保利用模数转换器的完整输入范围。在多种实施方式中，可以通过数字控制信号对变量增益放大器进行控制。在多种实施方式中，或者由用户控制动态变量增益放大器的增益级，或者由处理器通过信号分析控制动态变量增益放大器的增益级。在多种实施方式中，可以采用直流消除器来替代高通滤波器。在多种实施方式中，在通过模数转换器(ADC)对信号进行采样之后，可以通过有线或无线通信连接(例如蓝牙、USB等)将信号传输至进行信号处理的处理器。在多种实施方式中，该处理器可以包括数字信号处理器、微处理器或计算机。在多种实施方式中，处理器可以与模数转换器位于同一块电路板上，或者位于单独的电路板上，或者位于单独的单元中。在多种实施方式中，处理器可以采用线性解调算法来产生组合的生理运动波形。在多种实施方式中，处理器可以采用数字滤波从组合的生理运动信号中进一步分离呼吸和心脏信号。在多种实施方式中，可以采用固定数字滤波器来分离呼吸和心脏信号。信号处理算法还可以确定信号质量参数，该参数包括信号是否具有极低功率(小于0.0001-0.0004W)或极高功率(大于5至10W)。在多种实施方式中，该算法还可以确定是否存在非生理运动。在多种实施方式中，处理器可以在以太网上基于帧对帧通过TCP/IP对数据进行流式传输。在其它实施方式中，处理器可以采用遵照康体佳健康联盟指南的协议对数据进行流式传输。在多种实施方式中，各个包包含何时获取数据的时间戳、以及至少一个组合的生理波形(被分离之前的心跳和呼吸)，呼吸波形、以及心跳波形、呼吸速率、心跳速率、以及信号质量参数。图22示出了与处理器2202进行通信的上述连续波监测器2201的实施方式。如图所示，在这种实施方式中，连续监测器2201通过有线USB连接2203与处理器2202进行通信。The system shown in Figure 17 is suitable for operation as a continuous vital signs monitor. The system shown in Figure 17 is a continuous wave radar transceiver with a synchronous receiver. One advantage of this configuration is the simplicity of the system, and another is its ability to eliminate or reduce phase noise. In various embodiments, the
图23示出了显示装置的一种实施方式的屏幕截图，除了呼吸信号和心脏信号之外，该显示装置还为本地或远程用户显示其它信息。曲线图2301示出了监测器2301所获得的呼吸轨迹，曲线图2302示出了监测器2301所获得的心跳轨迹。Figure 23 shows a screenshot of one embodiment of a display device that displays other information for a local or remote user in addition to respiration and heart signals. Graph 2301 shows the respiration trace obtained by monitor 2301 , and graph 2302 shows the heartbeat trace obtained by monitor 2301 .
系统100的示例性配置可以包括连续生理监测器，该连续生理监测器包括工作在2.4-2.5GHz的无线电频率范围内的一根或多根天线、直接变频器或同差接收器、以及抗混叠滤波器。多种实施方式或者包括高通滤波器，或者包括直流消除电路。在多种实施方式中，系统100可以包括用于执行线性解调算法的处理器。在某些实施方式中，该处理器还可以用于执行非心肺运动探测算法和/或速率估计算法。在某些实施方式中，出于分离和/或跟踪的目的，将采用多根接收天线和多个接收器，以便处理器可以执行所述DOA算法。在多种实施方式中，此处可以采用上述速率估算算法来估算呼吸或心脏活动的速率。例如，在多种实施方式中，可以采用频域速率估算算法、时域速率估算算法、峰值探测算法或者这些算法的组合。在多种实施方式中，通过使用上面列举的方法，可以提高所确定的呼吸或心脏活动的精度，这些方法被美国临时申请第63/204881号所公开，并且该申请的全部内容结合于此作为参考。在某些实施方式中，可以周期性地执行速率估算算法，例如每10秒一次、每20秒一次、每30秒一次等。Exemplary configurations of the
在多种实施方式中，连续生理监测器可以包括活动监测器，该活动监测器用于提供目标主体何时进行非呼吸运动以及进行了多久的指示。在某些实施方式中，活动监测器可用于提供活动指数，该活动指数提供在测量时间段内的运动的频率和持续时间的指示。在多种实施方式中，通过设置多根天线，DOA算法能够确定主体的位置和主体改变位置的频率。例如，能够确定主体是否转向左边、转向右边、或进行原地活动。图24是显示装置或单元的屏幕截图，示出了呼吸速率、活动指示以及睡眠主体的位置。曲线图2401示出了作为时间的函数的主体每分钟的呼吸次数，曲线图2402示出了睡眠主体的活动。柱状图2403示出了主体在睡眠时的位置。In various embodiments, the continuous physiological monitor may include an activity monitor for providing an indication of when and for how long a subject subject is engaging in non-respiratory movements. In certain embodiments, an activity monitor may be used to provide an activity index that provides an indication of the frequency and duration of movement during the measurement period. In various embodiments, by providing multiple antennas, the DOA algorithm can determine the location of the subject and the frequency at which the subject changes location. For example, it can be determined whether the subject is turning to the left, turning to the right, or moving in place. Figure 24 is a screen shot of a display device or unit showing breathing rate, activity indication, and position of a sleeping subject.
在多种实施方式中，可以对生命体征信息(例如呼吸速率和心跳速率)进行缓存和绘图，以便提供主体的历史数据。图25A示出了系统在医院环境中用于测量主体的呼吸和/或心脏活动的应用。图25B是图25A中所示的显示装置的屏幕截图。在某些实施方式中，显示装置可以显示呼吸或呼吸速率2501以及指示呼吸活动2502(例如胸部随时间发生的移位)的波形。显示装置可以提供主体的相关附加信息2503和2504(例如年龄、性别等)。显示装置还可以包括开始按钮2505和停止按钮2506。在多种实施方式中，显示装置可以是保健专业人员所操作的装置的一部分。图26A和26B示出了显示装置的屏幕截图，该显示装置可以用来观察由其所提供的生命体征。图26A示出了显示装置的一种实施方式，该显示装置显示呼吸速率2601、随着时间的平均呼吸速率2602以及呼吸活动(例如胸部移位)的相关波形2603。图26B示出了显示装置的一种实施方式，该显示装置显示呼吸速率2604、指示呼吸活动的波形2605、指示心脏活动的波形2606以及心跳速率2607。In various implementations, vital sign information such as respiration rate and heart rate can be cached and mapped to provide historical data for the subject. Figure 25A shows the application of the system in a hospital setting for measuring the breathing and/or cardiac activity of a subject. FIG. 25B is a screenshot of the display device shown in FIG. 25A. In some embodiments, a display device may display respiration or
一种示例性系统配置包括用于探测反常呼吸的系统。该系统包括工作在约2.4GHz的无线电频率范围内的单根天线、直接变频器或同差接收器、以及直流消除电路。在多种实施方式中，该系统可以用于探测反常呼吸。在某些实施方式中，系统100还可以包括用于对呼吸活动或心脏活动的速率进行估算的算法。An exemplary system configuration includes a system for detecting abnormal breathing. The system consists of a single antenna operating in the radio frequency range of about 2.4 GHz, a direct converter or homodyne receiver, and a DC cancellation circuit. In various embodiments, the system can be used to detect abnormal breathing. In some embodiments, the
在多种实施方式中，如上述参考图17、18、19和20所描述的系统100可包括具有直接变频器或同差接收器的连续波雷达收发器。如上所述，该方法的优点在于系统的简易性与其消除或降低相位噪声的能力。在多种实施方式中，收发器工作的频率范围包括但不限于2.4GHz-2.5GHz ISM频段。如上所述，在多种实施方式中，信号源(例如图17中的信号源1701)可以用来产生接收器的传输信号和本机振荡信号。在多种实施方式中，同差接收器可以采用复解调来产生正交输出。在多种实施方式中，在正交输出被输入用于将模拟信号转变为数字信号的系统之前，可以对正交输出进行放大和抗混叠滤波。In various embodiments, the
在多种实施方式中，为了提高动态范围，可以移除或降低直流偏置。在多种实施方式中，采用交流耦合滤波器的传统方法可以被用来降低或移除直流偏置。然而，采用交流耦合滤波器或高通滤波器虽然可以移除直流偏置本身，但却可以抑制信号的低频分量并使这些低频分量的相位失真。因此，这将使不属于直流偏置的静态信号发生指数衰减，或者使该信号的相位出现失真。此外，具有交流耦合滤波器的系统可以产生或增加被滤波信号的群延迟，这可导致信号长时间的稳定时间或延迟。这些影响可以导致信号样本在复系数星座图上以带状分布而不是圆弧分布。这种失真可以对反常呼吸的探测算法的精度产生不利影响。采用图27中所示的直流消除电路2700可以消除这些缺点中的某些或全部，该直流消除电路只减去信号中的直流值，而不对余下的信号分量造成失真或不利影响。直流消除电路2700包括具有增益的差分放大器2701、模数转换器2702、数模转换器2703和数字信号处理/数字控制器2704。在多种实施方式中，直流消除电路可以采用模数转换器和数模转换器之间的反馈回路或具有数字电位器的分压器来移除或降低直流偏置。由于只带来非常微小的相位失真、稳定时间和群延迟，包含有直流消除器的系统可用于使心肺运动或其它运动与成像(例如CT扫描或核磁共振成像)保持同步，以及使自然呼吸运作与无创或有创辅助通气装置保持同步。相位失真和稳定时间的改善使得心肺运动与被问的问题和测谎器中的其它传感器的同步、心肺运动与刺激物和用于安全筛查和生物反馈的其它传感器的同步更加容易，如美国临时申请第61/204881号所公开的，并且该申请的全部内容结合于此作为参考。In various implementations, the DC bias can be removed or reduced for improved dynamic range. In various implementations, conventional methods using AC-coupled filters can be used to reduce or remove the DC bias. However, using an AC-coupled filter or a high-pass filter, while removing the DC bias itself, suppresses and distorts the phase of the low-frequency components of the signal. Therefore, this will exponentially decay a static signal that is not a DC bias, or distort the phase of that signal. Additionally, systems with AC-coupled filters can create or increase the group delay of the filtered signal, which can result in long settling times or delays in the signal. These effects can cause signal samples to be distributed in bands rather than arcs on the complex coefficient constellation. Such distortions can adversely affect the accuracy of abnormal breathing detection algorithms. Some or all of these disadvantages can be eliminated using the
在多种实施方式中，系统100可以包括用于发射和接收雷达信号的天线阵列。在某些实施方式中，采用单根天线发射雷达信号，并采用天线阵列接收雷达信号。可以将该接收器配置为采用复解调算法来产生正交输出的同差接收器。上述技术的一个优点是在下变频RF信号之后消除位于基频的镜像。在多种实施方式中，对正交输出进行抗混叠滤波，并且采用与上述类似的直流消除系统来移除或减小直流信号。可以通过模数转换器(ADC)对滤波后的信号进行采样，随后处理该数字化数据，以便将生理运动从噪声、干扰、和/或非生理运动中分离。可以对生理运动信号进行处理，从而提取所关心的波形和参数。In various implementations,
如上所述，在多种实施方式中，系统100可以用于探测反常呼吸的存在或者程度，其中反常呼吸是呼吸受阻、呼吸肌肉无力或呼吸衰竭的信号。该系统(例如连续监测器、正交连续波多普勒雷达系统)可以基于来自正交雷达接收器的同相(I)信号对比正交(Q)信号的曲线图的轨迹和/或复系数星座图对反常呼吸的程度进行监视。图28示出了一种确定反常呼吸指示器的方法的实施方式，其包括：As noted above, in various embodiments, the
1.可以通过最大特征值与第二大特征值的比值与投射在主特征向量上的最大峰间值信号与投射在与主向量正交向量上的最大峰间值信号的比值相乘来估算反常系数，如框图2801所示。1. It can be estimated by multiplying the ratio of the largest eigenvalue to the second largest eigenvalue by the ratio of the largest peak-to-peak signal projected on the main eigenvector to the largest peak-to-peak signal projected on a vector orthogonal to the main vector Abnormal coefficients, as shown in block diagram 2801.
2.可以根据以反常系数所实现的价值函数来计算反常指数。2. An anomaly index can be calculated from a value function implemented with anomalous coefficients.
3.如果将反常指数与一个或多个阈值进行比较，则该反常指数可以用来判断反常呼吸是否存在，或者被视为呼吸不同步的程度。3. If the abnormality index is compared to one or more thresholds, the abnormality index can be used to determine whether abnormal breathing is present, or the degree to which breathing is considered asynchronous.
图29和30为显示装置的屏幕截图，该显示装置用来显示来自探测反常呼吸的系统的输出。可以生动地(例如通过长条)2901和3001显示反常呼吸的相关信息。例如，如图29和30所示，当探测到反常呼吸时，指示平均呼吸速率的长条能够改变颜色(例如由黄变红、由绿变红、由红变绿等)。还可以显示其它信息，例如呼吸波形2902和3002或呼吸速率2903和3003。图30的显示还生动地(例如通过条形图)示出了一次换气量3004(每次呼吸流过鼻腔通道的空气量)。当探测到反常呼吸时，代表一次换气量的长条也能够改变颜色(例如由黄变红、由绿变红)。还可以采用指示反常呼吸的其它方法。29 and 30 are screen shots of a display device used to display output from a system for detecting abnormal breathing. Relevant information of abnormal respiration may be displayed vividly (eg, through bars) 2901 and 3001 . For example, as shown in FIGS. 29 and 30 , the bar indicating the average breathing rate can change color (eg, yellow to red, green to red, red to green, etc.) when abnormal breathing is detected. Other information such as
一种示例性配置包括工作在约2.4GHz频率的系统100。在某些实施方式中，系统包括作为发射器的单根天线和作为接收器的三根或更多天线。在多种实施方式中，可以采用不同数量的发射和接收天线。在一些实施方式中，系统还包括正交直接变频器或同差接收器、高通滤波器或直流消除电路、或同时包括前面两者。系统100还包括用于执行线性解调算法的处理器，如美国临时申请第61/204881号所公开的，并且该申请的全部内容结合于此作为参考。An exemplary configuration includes
如上所述，在多种实施方式中，同差接收器由于其简易性与其消除或降低相位噪声的能力而被采用。为了在下变频RF信号之后消除位于基频的镜像，该系统包括提供正交输出的复解调。在多种实施方式中，可以采用天线阵列来发射和接收雷达信号。在某些实施方式中，可以采用单根天线进行发射，并采用天线阵列进行接收。在多种实施方式中，系统100可用于执行波达方向(DOA)算法，或通过在每个所关心平面上的至少两个接收器天线提供处理。在多种实施方式中，可以采用一个或多个接收器天线阵列来执行DOA算法。如图31所示，通过共享不同阵列群的天线，可以将天线阵列设计得更加紧凑。图31中所示的系统3100包括中央天线3101、与天线3104通信的左侧天线3102、与天线3105通信的右侧天线3103。参考图31，中央天线3101同时属于左侧阵列群和右侧阵列群并且同时与接收器3104和3105通信，其中接收器3104和3105产生由两个单独元件所组成的两个独立阵列群。在另一实施方式中，与每个群被设计为具有两个元件的传统天线阵列设计相比，上述方法可以降低所需天线的数量，从而减少该数量的天线所需的总面积。如上所述，可以对正交输出进行抗混叠滤波，并且在多种实施方式中，采用高通滤波器移除直流信号，或者采用直流消除系统移除直流信号。可以通过模数转换器(ADC)对滤波后的信号进行采样，随后在处理器中对数字化后的数据进行处理，从而将生理运动信号从噪声、干扰、和/或非生理运动中分离。可以对生理运动信号进行处理，从而确定所关心的心肺参数。图32示出了包括两根接收天线3201和3202的系统的一种实施方式。可以将图32中所示的系统的接收天线扩展至任意数量，或者将该系统修改为只包括一根接收天线。在某些实施方式中，各接收器可以具有自己的天线。As noted above, in various embodiments, a homodyne receiver is employed due to its simplicity and its ability to cancel or reduce phase noise. To remove images at the fundamental frequency after downconverting the RF signal, the system includes complex demodulation that provides quadrature outputs. In various implementations, an antenna array may be employed to transmit and receive radar signals. In some embodiments, a single antenna may be used for transmission and an array of antennas may be used for reception. In various implementations, the
在包括多根天线和多个接收器的多种实施方式中，采用DOA算法或处理能够为生命体征的探测带来多种益处。当采用雷达探测生理信息时，期望具有宽的天线波束宽度，以便覆盖所有可能位置上的主体。然而，宽的波束宽度可能导致探测到远离主体的运动，并且这些运动可能对测量造成影响。对来自多根接收天线的DOA进行处理，不但可以提供探测和跟踪主体所需的宽的波束宽度，还可以提供一种方法：对较窄波束进行引导从而使雷达信号集中在生理运动上并且避免来自环境的干扰运动。在多种实施方式中，DOA处理还可以消除具有高振幅干扰信号的角度。In various embodiments including multiple antennas and multiple receivers, employing DOA algorithms or processing can provide various benefits for the detection of vital signs. When using radar to detect physiological information, it is desirable to have a wide antenna beamwidth in order to cover the subject at all possible locations. However, a wide beamwidth may cause motions far from the subject to be detected, and these motions may affect the measurement. Processing the DOA from multiple receive antennas not only provides the wide beamwidths needed to detect and track subjects, but also provides a means of steering the narrower beams so that the radar signal is focused on physiological motion and avoids Interfering motion from the environment. In various embodiments, DOA processing can also eliminate angles with high amplitude interfering signals.
雷达系统100基于信号源来自天线的不同角度可采用DOA来对该雷达系统所探测到的信号源进行分离。多种DOA算法中的任意一种都可以用于这项技术。可以将来自天线的信号作为天线阵列进行处理，其中该天线阵列较之任何单独天线具有更窄的波束宽度。通过处理，可以有效地将该阵列的波束导向期望的信号源，从而天线波束集中于信号源，并且波束范围之外的任何运动将会根据天线方向图在该方向上衰减。此外，可以探测相对于目标主体的角度并在界面上按照角度、或者更常见的方向指示(即，向前、向左、或向右)呈现相对于目标主体的角度。The
还可以采用多根天线对干扰运动源进行探测和跟踪。随后可以对来自这些天线的信号进行组合，从而使天线波束方向图在干扰运动的方向上出现零位。通过在测量一个信号源的同时在干扰运动的方向上设置零位，上述方法可以对信号源进行分离。Multiple antennas can also be used to detect and track interference sources. The signals from these antennas can then be combined such that the antenna beam pattern is nulled in the direction of the interfering motion. The above method isolates the signal source by setting a null in the direction of the interfering motion while measuring one source.
下面描绘了用于分离多个生理信号的算法的一种实施方式，其包括：One embodiment of an algorithm for separating multiple physiological signals is depicted below, comprising:
1.确定所关心的频率分量f＝f1，f2，...，fn。在某些实施方式中，可以通过测量多重通道的频谱功率的组合来确定上述分量。对输出应用指定的价值函数，从而区别来自目标的胸部运动的频率分量。1. Determine the frequency components of interest f = f1 , f2 , . . . , fn . In some embodiments, the aforementioned components may be determined by measuring a combination of spectral powers of multiple channels. Applies the specified cost function to the output, distinguishing the frequency components of the chest motion from the target.
2.构造通道矩阵H，其入口对应于f1，f2，...，fn。例如，通道矩阵入口的第m行和第n列可以为hmn＝smn(fn)，对应于接收器天线m和信号源n，其中smn代表该通道的频谱。2. Construct a channel matrix H whose entries correspond to f1 , f2 , . . . , fn . For example, the mth row and nth column of the channel matrix entry may be hmn = smn (fn ), corresponding to receiver antenna m and signal source n, where smn represents the frequency spectrum of the channel.
3.根据等式(1)构造数组向量，：3. Construct the array vector according to equation (1):
g(θ)＝[1 exp[jkd sin(θ)]...exp[jkd(M-1)sin(θ)]]T (1)g(θ)=[1 exp[jkd sin(θ)]...exp[jkd(M-1)sin(θ)]]T (1)
其中k为波数，d＝λ/2为各接收器天线之间的分隔距离，θ为从天线法向量到目标的角度，而M为已接收天线的数量。where k is the wavenumber, d = λ/2 is the separation distance between the receiver antennas, θ is the angle from the antenna normal vector to the target, and M is the number of received antennas.
4.根据等式(2)计算在信号源的角度上获得的最大平均功率：4. Calculate the maximum average power obtained at the angle of the signal source according to equation (2):
Pav(θ)＝|HHg(θ)|2 (2)Pav (θ)＝|HH g(θ)|2 (2)
5.将彼此间距小于某一个角距离的角度消除，其中该角距离小于多个接收器天线阵列的角分辨率；以及识别至少第一角方向和第二角方向，从而使各角方向通过某一个角距离相互间隔，其中该角距离大于或等于所述多个接收器天线阵列的角分辨率。5. Eliminating angles that are less than an angular distance from each other, wherein the angular distance is less than the angular resolution of the plurality of receiver antenna arrays; and identifying at least a first angular direction and a second angular direction such that each angular direction passes through a certain An angular distance is separated from each other, wherein the angular distance is greater than or equal to the angular resolution of the plurality of receiver antenna arrays.
6.构造M×N阵列的矩阵A，其第i列由等式(3)给出：6. Construct a matrix A of M×N arrays whose ith column is given by equation (3):
g(θi)＝[1 exp[jkd sin(θi)]...exp[jkd(M-1)sin(θi)]]T (3)g(θi )=[1 exp[jkd sin(θi )]...exp[jkd(M-1)sin(θi )]]T (3)
其中d＝λ/2和θ分别为接收天线的间距和角度，而M为已接收天线的数量。在这些实施方式中，在主体附近存在着其它移动物体，该物体能够散射雷达信号，N代表移动物体的数量。where d=λ/2 and θ are the spacing and angle of the receiving antennas, respectively, and M is the number of received antennas. In these embodiments, there are other moving objects near the subject that can scatter the radar signal, and N represents the number of moving objects.
7.如步骤4所示，通过使通道数据与矩阵A的逆矩阵相乘，将空间上的零位导向不需要的信号源，从而实现信号的分离。7. As shown in
S＝A-1Rx (4)S = A-1 Rx (4)
在多种实施方式中，这些方法可以用于包括一个发射器和多个接收器天线的单输入多输出(SIMO)系统。或者可以用于包括多个位于不同频率的发射器和多个接收器的多输入多输出(MIMO)系统。在多种实施方式中，其它DOA算法还可以用来分离位于天线不同角度的信号源。In various implementations, these methods can be used in single-input multiple-output (SIMO) systems that include a transmitter and multiple receiver antennas. Or it can be used in multiple-input multiple-output (MIMO) systems that include multiple transmitters at different frequencies and multiple receivers. In various embodiments, other DOA algorithms can also be used to separate signal sources located at different angles of the antenna.
在多种实施方式中，采用DOA算法之后，可以从生理运动波形提取主体的生命体征，例如呼吸速率、胸部移位、一次换气量、和/或心跳速率，并将其输出至输出单元。In various embodiments, after employing the DOA algorithm, the subject's vital signs, such as respiration rate, chest displacement, ventilation volume, and/or heart rate, may be extracted from the physiological motion waveform and output to an output unit.
在多种实施方式中，可以对生命体征和/或方向信息进行缓存和绘图，从而提供主体的历史数据。图33示出了一种显示装置的屏幕截图，该显示装置用于在采用DOA处理对两个主体的呼吸信号进行分离之后，输出他们的心肺信息。曲线图3301示出了从两个主体获得的基频信号。曲线图3302示出了对应于第一个主体的呼吸活动的波形，曲线图3303示出了对应于第二个主体的呼吸活动的波形。在多种实施方式中，显示装置可以用于显示呼吸活动的相关信息(例如呼吸的相关波形、平均呼吸速率等)。在多种实施方式中，还可以显示其它信息，例如一次换气量、心脏和/或主体的角度或位置。图34示出了一种显示装置的屏幕截图，该显示装置用于显示呼吸波形3401和一次换气量和呼吸速率的历史记录。在某些实施方式中，还可以在显示框3402上显示目标相对于传感器的位置。在多种实施方式中，为了在患者之间的进行切换，显示器可以包括控制区3403。图35示出了一种显示装置的屏幕截图，该显示装置用于显示两个人的呼吸运动波形。曲线图3501示出了系统从两个主体所获得混合基频信号。采用DOA算法对该基频信号进行处理，从而提取两个主体的呼吸活动的相关信息。曲线图3502示出了位于右侧约24°的第一个主体的呼吸活动。曲线图3503示出了位于左侧约13°的第二个主体的呼吸活动。曲线图3504示出了两个主体的呼吸速率的历史记录。In various implementations, vital signs and/or orientation information may be cached and mapped to provide historical data for the subject. Figure 33 shows a screenshot of a display device for outputting cardiorespiratory information of two subjects after their respiratory signals have been separated using DOA processing.
一种示例性配置包括具有低中频接收器的工作在约5.8GHz的系统100。在多种实施方式中，系统还包括用于发射雷达信号的单根天线，以及用于接收雷达信号的单根天线。在多种实施方式中，系统包括低中频接收器，用于将接收到的信号转换为从几Hz到几kHz频率范围内的信号。例如，在某些实施方式中，低中频接收器可以用于将接收到的信号转换为具有约1Hz至200kHz频率范围内的信号。在多种实施方式中，系统的处理器可用于执行圆弧解调算法。在多种实施方式中，可以将系统100配置为抽查监测器或连续监测器。An exemplary configuration includes
在多种实施方式中，系统包括工作在约5.8GHz的振荡器(例如压控振荡器)，以及用于产生在kHz至MHz范围内的辐射的稳定的晶体振荡器。在功率分配器中对来自振荡器的信号进行分配。将来自功率分配器的第一输出端的信号提供至发射天线，并且将来自晶体振荡器的信号功率与分配器的第二输出端的信号进行叠加以产生接收器的参考信号。由于参考信号仍将得益于范围相关效应，故参考信号的相位噪声将不会对剩余相位噪声产生不利影响；通过通常具有极低相位噪声的晶体振荡器对剩余相位噪声进行限制。在多种实施方式中，低中频接收器结构可以通过低相位噪声来缓解由1/f噪声、通道失衡和直流偏置所引起的问题。在多种实施方式中，可以通过模数转换器对低中频信号直接采样，并且在数字领域中将低中频信号下变频至正交基频信号。因此，当采用反正切解调时，原点位置的显著变化、开放圆弧所在的圆的半径的变化或圆上的圆弧的位置变化可以指示天线和主体之间的关系变化，还可以指示非心肺运动。如上所述，可以通过计算归一化的当前向量与归一化的先前向量的内积来探测非心肺运。如果该内积的值在阈值之下，则指示主体和天线之间的关系的显著变化。在这些实施方式中，在采用反正切解调的系统中，拟合最佳拟合圆弧的均方根误差的变化还可以指示非心肺运动或其它信号干扰。In various embodiments, the system includes an oscillator (eg, a voltage controlled oscillator) operating at about 5.8 GHz, and a stable crystal oscillator for generating radiation in the kHz to MHz range. The signal from the oscillator is divided in the power divider. The signal from the first output of the power splitter is provided to the transmit antenna, and the signal power from the crystal oscillator is superimposed with the signal at the second output of the splitter to produce a reference signal for the receiver. Since the reference signal will still benefit from range-dependent effects, the phase noise of the reference signal will not adversely affect the residual phase noise; the residual phase noise is limited by crystal oscillators which typically have very low phase noise. In various implementations, the low-IF receiver architecture can mitigate problems caused by 1/f noise, channel imbalance, and DC offset through low phase noise. In various embodiments, the low intermediate frequency signal may be directly sampled by an analog-to-digital converter and down-converted to a quadrature base frequency signal in the digital domain. Thus, when arctangent demodulation is employed, a significant change in the position of the origin, a change in the radius of the circle on which the open arc lies, or a change in the position of an arc on a circle can indicate a change in the relationship between the antenna and the subject, and can also indicate a non- Cardio. As mentioned above, non-cardiopulmonary movement can be detected by computing the inner product of the normalized current vector and the normalized previous vector. If the value of the inner product is below a threshold, it indicates a significant change in the relationship between the subject and the antenna. In these embodiments, in systems employing arctangent demodulation, changes in the root mean square error of the best fit arc may also be indicative of non-cardiopulmonary motion or other signal disturbances.
一种示例性配置包括具有直接变频接收器和直流偏置消除器的工作在约5.8GHz的无线电频率的系统100。在多种实施方式中，系统还包括用于发射无线信号的单根天线，以及用于接收无线辐射的单根天线。在多种实施方式中，可以采用一根或多根天线来发射和/或接收信号。在多种实施方式中，系统100可以包括用于执行圆弧解调算法的处理器。One exemplary configuration includes
在采用约5.8GHz的无线电频率的实施方式中，相位偏差可以导致非线性正交基频输出，或者在复系数星座图上产生圆弧轨迹而不是直线，如图36A所示。在具有5.8GHz的载体的系统中，相对于其它解调算法，可以优选圆弧解调来获得精确的信号。此外，可以优选直流消除器而不是交流耦合滤波器来减少信号失真，并能足够精确地确定散布有信号样本的圆的原点。由于圆弧解调可以从与实际胸部运动呈线性比例的基频信号中提取相位信息，因此能够从圆弧解调中估算呼吸的深度。还可以将从圆弧解调所获得的呼吸深度信息应用于一次换气量的估算；线性胸部移位与一次换气量之间存在线性关系。图38B示出了呼吸深度随时间变化的曲线图3601。呼吸深度示出了吸气峰值3602和呼气波谷3603。可以从该曲线图估算一次换气量(每个呼吸周期中所吸入的空气量Ti和所呼出的空气量Te)。曲线图3604示出了通过传统传感器所获得的相应测量结果。图36C示出了显示装置的屏幕截图，该显示装置示出了一次换气量3605、对应于呼吸活动的波形3606和呼吸速率3607。在多种实施方式中，随着圆弧长度的增加，信号极性的模糊性降低，从而能够对呼气和吸气的持续时间进行估算，并且能够对吸气时间和呼气时间的比值进行估算。或者以保持一定间隔的方式对身体表面与心肺相关的运动进行测量，或者以接触身体表面的方式对上述运动进行测量。在这些实施方式中，当天线接触身体时，可以将身体表面反射从内部反射中分离，从而对身体内部的运动的进行测量。在多种实施方式中，还可以对身体表面和内部的部分和组织进行其它内部心肺相关变化的电磁测量，其中包括与心跳相关的阻抗变化。In embodiments employing radio frequencies around 5.8 GHz, the phase deviation can result in a non-linear quadrature fundamental frequency output, or a circular trajectory rather than a straight line on the complex coefficient constellation, as shown in Figure 36A. In a system with a 5.8GHz carrier, arc demodulation may be preferred over other demodulation algorithms to obtain an accurate signal. Furthermore, a DC canceller may be preferred over an AC coupling filter to reduce signal distortion and to determine the origin of the circle interspersed with signal samples with sufficient accuracy. Since arc demodulation can extract phase information from the fundamental frequency signal which is linearly proportional to actual chest motion, the depth of respiration can be estimated from arc demodulation. The respiratory depth information obtained from arc demodulation can also be applied to the estimation of tidal volume; there is a linear relationship between linear chest displacement and tidal volume. Figure 38B shows a
一种示例性配置包括工作在5.8GHz频段的无线电频率的多重接收器系统。系统包括用于发射雷达信号的单根天线，以及用于接收雷达信号的四根或多根天线。在多种实施方式中，接收器天线可以间隔半个波长。在某些实施方式中，系统100的发射天线可以多于一根，并且接收天线可以少于四根。系统还为每个接收天线配置直接变频器或同差接收器。在多种实施方式中，系统100可以包括用于移除或降低直流偏置的直流消除电路。系统100还可以包括用于执行圆弧解调算法的处理器。An exemplary configuration includes a multiple receiver system operating at a radio frequency in the 5.8 GHz band. The system consists of a single antenna for transmitting radar signals and four or more antennas for receiving radar signals. In various implementations, receiver antennas may be spaced a half wavelength apart. In some implementations,
在工作在约5.8GHz的频率范围内的系统的实施方式中，可以将天线阵列设计或制造得更为紧凑。因此，在工作在约5.8GHz的频率范围内的系统中，能够在与工作在约2.4GHz的系统基本相同的面积内，获得更多的阵列单元数量。换言之，在天线的覆盖面积相同的情况下，相比于工作在约2.4GHz的系统，工作在约5.8GHz的系统能够达到更高的空间分辨率。图37示出了阵列元件的原理性布局，该阵列元件包括发射天线3701和至少四根接收天线3702a-3702d。由于能够在给定面积上比工作在约2.4GHz的系统包括更多天线，故当用于DOA处理时，工作在约5.8GHz的系统的实施方式更为有效。天线数量的增加使得能够对彼此间距很小的主体(例如，具有4根天线的情况下，两个主体之间的角间距小于15°)进行探测和跟踪。In an embodiment of the system operating in the frequency range of about 5.8 GHz, the antenna array may be designed or manufactured to be more compact. Therefore, in a system operating in the frequency range of about 5.8 GHz, a greater number of array elements can be obtained in substantially the same area as a system operating in the frequency range of about 2.4 GHz. In other words, in the case of the same coverage area of the antenna, the system operating at about 5.8 GHz can achieve higher spatial resolution than the system operating at about 2.4 GHz. Figure 37 shows a schematic layout of an array element comprising a transmit
在系统的多种实施方式中，可以使用上述DOA算法或处理技术来跟踪主体。在某些实施方式中，在采用DOA算法对主体进行跟踪或对来自非心肺运动或第二个人的心肺运动的干扰进行抑制之后，可以使用圆弧解调。在将来自多个主体的信号分离之后，可以使用非心肺运动探测算法。在多种实施方式中，可以通过基于圆弧的解调算法对来自各方向的信号进行解调，该算法采用最佳拟合圆的参数从复系数星座图中获得角度信息。最佳拟合圆的原点位置的显著变化、最佳拟合圆的半径的变化或圆上的圆弧的角度位置的变化可以指示非心肺运动或其它信号干扰。随后处理器可以提供一个或多个主体的心肺信息。In various embodiments of the system, subjects may be tracked using the DOA algorithms or processing techniques described above. In some embodiments, arc demodulation may be used after tracking the subject using a DOA algorithm or suppressing interference from non-cardiopulmonary or cardiopulmonary exercise of a second person. After separating the signals from multiple subjects, non-cardiopulmonary motion detection algorithms can be used. In various embodiments, signals from various directions may be demodulated by an arc-based demodulation algorithm that uses parameters of a best-fit circle to obtain angle information from a constellation of complex coefficients. A significant change in the location of the origin of the best-fit circle, a change in the radius of the best-fit circle, or a change in the angular position of an arc on the circle may indicate non-cardiopulmonary exercise or other signal interference. The processor may then provide cardiorespiratory information for one or more subjects.
在多种实施方式中，描述了一种包括传感器的系统100，该传感器放置在身体上，用于测量是否存在呼吸和/或心脏运动。还可以将系统100配置为能够放置在接触主体的位置(例如接触主体胸部)的穿戴式微波多普勒雷达。可以采用穿戴式微波多普勒雷达通过探测身体表面的运动、内脏的运动、或这些运动的组合来对主体的呼吸速率和心跳速率、和/或其它生命体征进行估算。该系统100的多种实施方式可以工作在约2.4GHz、约5.8GHz或某些其它频段。在多种实施方式中，可以将系统100配置为独立装置，或可以将系统与无线通信系统进行结合，从而与其它本地装置和/或远程数据中心或界面进行通信，如美国临时申请第61/194838号所公开的，并且该申请的全部内容结合于此作为参考。In various implementations, a
在多种实施方式中，描述了一种包括传感器的系统100，该传感器放置在身体上，用于测量呼吸活动和/或心脏运动。还可以将系统100配置为能够放置在接触主体的位置(例如接触主体胸部)的穿戴式微波多普勒雷达。可以采用穿戴式微波多普勒雷达通过探测身体表面的运动、内脏的运动、或这些运动的组合来对主体的呼吸速率和心跳速率、和/或其它生命体征进行估算。该系统的多种实施方式可以工作在约2.4GHz、约5.8GHz或某些其它频段。在多种实施方式中，可以将系统100配置为独立装置，或可以将系统与无线通信系统进行结合，从而与其它本地装置和/或远程数据中心或界面进行通信，如美国临时申请第61/194838号所公开的，并且该申请的全部内容结合于此作为参考。In various implementations, a
图38A示出了当类似于系统100的穿戴式雷达系统被放置在与屏住呼吸的主体相接触的位置时的心肺活动的相关信息。曲线图3801示出了还未被处理的原始心肺信号，曲线图3802示出了已处理的心脏信号。图38B示出了当穿戴式雷达系统被放置在与屏住呼吸的主体相接触的位置时的心肺活动的相关信息与参考信号的对比。曲线图3804示出了接收到的雷达信号，曲线图3803示出了参考信号。曲线图3804示出了雷达信号与参考信号之间的对比。FIG. 38A shows information related to cardiorespiratory activity when a wearable radar system similar to
图38C示出了当穿戴式雷达系统被放置在与正常呼吸的主体相接触的位置时的心肺活动的相关信息。曲线图3805示出了未处理的信号，曲线图3806示出了对原始信号进行处理后所获得的呼吸信号。曲线图3807示出了对原始信号进行处理后所获得心脏信号。由于伴随有呼吸和/或该呼吸信号的谐波，心脏信号呈现出不规则。然而，采用本申请中所描述的实施方式，可以测量到基本精确的心跳速率。38C shows information related to cardiorespiratory activity when the wearable radar system is placed in contact with a normally breathing subject.
图38D示出了通过对正常呼吸的主体使用上述基于雷达的非接触式生理传感器所获得的心肺活动的相关信息与参考信号的对比。曲线图3808示出了未处理的信号，曲线图3809示出了对原始信号进行处理后所获得呼吸信号。曲线图3809示出了通过传统传感器例如胸带测量到的呼吸信号。曲线图3810示出了对原始信号进行处理后所获得心脏信号与采用手指传感器所获得的心脏信号的对比。FIG. 38D shows the comparison of information on cardiorespiratory activity obtained using the above-described radar-based non-contact physiological sensor with a reference signal on a normally breathing subject.
图38E和38F是用于显示呼吸波形3811、心跳波形3812、呼吸速率3813和活动指示3814的显示装置的实施方式。在多种实施方式中，该用户界面可以用来探测主体的存在或用来探测是否主体正在呼吸或主体的心脏正在跳动。在多种实施方式中，显示界面不但可以用来探测主体的存在，还可以用来进行伤员鉴别分类和复苏。在多种实施方式中，如果探测到活动或呼吸或心跳，则主体存在；如果任何一项都不存在，则探测不到主体。在多种实施方式中，显示界面可以用来探测是否主体的心脏正在跳动和/或主体正在呼吸以用于伤员鉴别分类，显示界面还可以用来确定是否需要心肺复苏术和/或心脏除颤和/或其它复苏。在多种实施方式中，如果例如由于主体的心肺活动而探测到主体的存在，则随后提供指示。例如，如果主体存在，指示器3815可以变绿。然而，如果随后探测不到主体的存在，指示器3815可以变红，也不再显示呼吸波形或呼吸速率，如图38F所示。38E and 38F are embodiments of a display device for displaying
图38G-38J为图38E和38F中所示的显示装置的可选实施方式，其中显示装置用于显示呼吸波形、呼吸速率、心跳速率、心跳波形、活动指示、主体存在指示等。在图38G中，主体的存在可以通过心脏信号3812和呼吸信号3814来进行探测，并通过指示器3815的变黄和/或活动指示器3814的升高来进行指示。在图38H中，通过呼吸波形3811示出所探测到的主体的呼吸信号，并且当活动指示器变绿时可以对呼吸信号进行指示。可以将开始或停止控制器设置在显示器上，分别如3816和3815所示。38G-38J are alternative embodiments of the display device shown in FIGS. 38E and 38F, wherein the display device is used to display respiration waveforms, respiration rate, heart rate, heartbeat waveforms, activity indications, subject presence indications, and the like. In FIG. 38G , the presence of a subject may be detected by
在图38I中，由于没有探测到呼吸信号，指示器3815为红色。在图38J中，观察到呼吸信号3812，并通过活动指示器变红来指示主体的存在。In FIG. 381 ,
在某些实施方式中，传感器还可以通过直接接触主体胸部来探测包括心肺活动的机械生理运动。当传感器处于未接触状态时，一些从天线发出的信号在胸部表面被反射，而一些射出的信号可以完全绕过主体，从而周围环境中的运动可以对生理运动信号造成干扰。在传感器处于接触状态时，几乎所有信号都与身体结合，而几乎没有信号绕过主体。在传感器不接触身体的实施方式中，因为采用天线阵列，所以天线辐射方向图具有窄的波束宽度，从而能够将发射出的信号集中在期望方向，以避免探测到周围环境中的运动。在传感器接触身体的实施方式中，几乎所有信号都与身体结合，从而天线的波束宽度不再是问题，并且能够通过一根天线(而不是阵列)来探测心肺信号而不会受到任何来自周围环境的较大干扰。In some embodiments, the sensors can also detect mechanical physiological motion, including cardiorespiratory activity, through direct contact with the subject's chest. When the sensor is not in contact, some of the signal emitted from the antenna is reflected off the surface of the chest, and some emitted signal can completely bypass the subject, so that motion in the surrounding environment can interfere with the physiological motion signal. When the sensor is in contact, almost all of the signal is combined with the body, and almost none of the signal bypasses the body. In embodiments where the sensor is not in contact with the body, the antenna radiation pattern has a narrow beamwidth due to the use of an antenna array, thereby focusing the emitted signal in a desired direction to avoid detecting motion in the surrounding environment. In an embodiment where the sensor is in contact with the body, almost all of the signal is combined with the body, so that the beamwidth of the antenna is no longer an issue, and the cardiopulmonary signal can be detected by a single antenna (rather than an array) without any interference from the surrounding environment. greater interference.
当传感器与主体胸部相接处时，可以在反射信号上对由心肺活动所引起的胸部运动进行振幅调制。在某些实施方式中，可以通过低中频单通道接收器结构提取该振幅调制信号，其中该振幅调制信号与对应于主体心肺活动的主体胸部运动成比例。在多种实施方式中，一旦将反射信号下变频至低中频，将以高于奈奎斯特速率的速率对该信号进行取样以便获得不失真数字信号。在多种实施方式中，对数字化输入信号进行希尔伯特变换以获得复数信号，其中同相部分是输入信号，而正交部分是希尔伯特变换的输出。Chest motion induced by cardiopulmonary activity can be amplitude modulated on the reflected signal when the sensor is in contact with the subject's chest. In some embodiments, the amplitude modulated signal may be extracted by a low intermediate frequency single channel receiver configuration, wherein the amplitude modulated signal is proportional to the subject's chest motion corresponding to the subject's cardiorespiratory activity. In various embodiments, once the reflected signal is down-converted to a low intermediate frequency, the signal is sampled at a rate higher than the Nyquist rate in order to obtain an undistorted digital signal. In various embodiments, a digitized input signal is Hilbert transformed to obtain a complex signal, wherein the in-phase portion is the input signal and the quadrature portion is the output of the Hilbert transform.
在多种实施方式中，通过取前述步骤中获得的复数值的绝对值，可以获得与心肺活动成比例的反射信号的包络。这种方法可以通过采用单通道接收器来实现紧凑装置而不用考虑失衡因素。该解调电路比正交结构的电路更加简单。In various embodiments, by taking the absolute value of the complex values obtained in the preceding steps, an envelope of the reflected signal proportional to cardiorespiratory activity can be obtained. This approach allows the realization of compact devices by using a single-channel receiver regardless of imbalance. The demodulation circuit is simpler than a quadrature-structured circuit.
在多种实施方式中，包括众多“瘦”心肺传感器的网络与集中处理平台协同工作。图39A描述了一种集中拓扑结构，其中众多“瘦”非接触式心肺传感器构成群3901a和3901b。可以通过网络平台3902对传感器群进行控制，并且全部处理都交给网络平台进行。当在密集区域(每个病床一个传感器)部署传感器时，这种拓扑结构的实施方式是有效的。与每个传感器都配备有心肺监测器的情况相比，这种配置下的每个传感器只占用最少的硬件，在某些实施方式中，仅仅满足采集数据和转发数据流的需要即可。在多种实施方式中，每个传感器将包括数据采集模块和网络模块。在多种实施方式中，原始数据将以流的形式被发送至网络平台3902并在其中完成进一步的处理。在上述多种实施方式中，系统可以在内部对原始数据进行处理。在多种实施方式中，处理将包括IQ通道的解调、用于跟踪呼吸速率的任何DOA处理等。在多种实施方式中，计算出的统计信息和处理过的数据将随后驻留在网络平台3902上，或者可以将它们转发到电子健康记录服务器。远程客户端随后可以通过电脑、手机、PDA等访问该数据。在多种实施方式中，还可以通过终端本地或远程查看该数据。图39B示出了图39A的可选实施方式，示出了信息在传感器群3903a、网络平台3902以及该网络的多种其它部件之间的传播方向。In various embodiments, a network comprising numerous "thin" cardiorespiratory sensors works in conjunction with a centralized processing platform. Figure 39A depicts a centralized topology in which numerous "thin" non-contact cardiorespiratory sensors form
上述配置还可以用于需要在集中位置处理信息的安全应用中。例如，在家庭安保中，可以对网络平台3902进行设置，如果家庭所探测到的主体数量大于所设定的数量时，该平台将发出警报声。“瘦传感器网络”的多种实施方式的另一种应用是国家安保，其中需要对多人进行快速筛查，例如在机场。可以建立并访问实时数据库，其中可以基于安保的目的对特定主体的生物统计学信息进行采集、比对以及分析。The configuration described above can also be used in security applications where information needs to be processed at a centralized location. For example, in home security, the
上文虽然公开了特定的优选实施方式和实施例，但创造性主题可超越具体公开的实施方式和/或用途，并且延伸至其变形和等同替换。因此，此处所附的权利要求的范围不受任何上述的具体实施方式限制。例如，在此处所公开的任何方法或处理中，可以通过任何合适的顺序执行本方法或处理的动作和操作，而不必受任何具体公开的顺序限制。可以通过能够有助于理解特定实施方式的方式将多种操作描述为多个依次进行的独立操作。然而，不应将说明的顺序解释为暗示这些操作是依赖于顺序的。此外，可以将此处所述的结构、系统和/或装置实施为组合部件或为分离部件。基于与多种实施方式比较的目的，对这些实施方式的特定方面和优点进行了描述。不必通过任何具体实施方式来获得所有这些方面或优点。因此，例如，多种实施方式可以通过获得或优化如此处所教导的一个优点或一组优点进行实施，而不必获得此处可能被教导或提及的其它方面或优点。因此，本发明仅由所附权利要求所限定。While specific preferred embodiments and examples are disclosed above, the inventive subject matter extends beyond the specific disclosed embodiments and/or uses and extends to variations and equivalents thereof. Therefore, it is not intended that the scope of the claims appended hereto be limited by any of the specific embodiments described above. For example, in any method or process disclosed herein, the acts and operations of the method or process may be performed in any suitable order and are not necessarily limited by any specific disclosed order. Various operations may be described as multiple separate operations performed in sequence in a manner that may be helpful in understanding particular embodiments. However, the order of description should not be construed as to imply that these operations are order dependent. Furthermore, the structures, systems and/or devices described herein may be implemented as combined components or as separate components. Certain aspects and advantages of the various embodiments are described for purposes of comparison with the various embodiments. Not all such aspects or advantages need be achieved by any particular implementation. Thus, for example, various embodiments may be practiced by obtaining or optimizing one advantage or group of advantages as taught herein without necessarily obtaining other aspects or advantages that may be taught or mentioned herein. Accordingly, the invention is limited only by the appended claims.
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| US61/154,728 | 2009-02-23 | ||
| PCT/US2009/039560WO2009124297A1 (en) | 2008-04-03 | 2009-04-03 | Non-contact physiologic motion sensors and methods for use |
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| CN115040092A (en)* | 2022-06-13 | 2022-09-13 | 天津大学 | Heart rate monitoring method and respiratory event detection method based on channel state information |
| CN115089162A (en)* | 2022-05-30 | 2022-09-23 | 合肥工业大学 | A method and device for detecting respiratory rate based on UAV video |
| CN115153426A (en)* | 2021-04-06 | 2022-10-11 | 光禾感知科技股份有限公司 | Monitoring system and monitoring method for sleep apnea |
| CN115515481A (en)* | 2020-05-29 | 2022-12-23 | 深圳迈瑞生物医疗电子股份有限公司 | Monitoring equipment with non-contact physiological sign monitoring function |
| CN115736915A (en)* | 2022-11-21 | 2023-03-07 | 大连理工大学 | Patient physical ability assessment method based on multi-source information fusion |
| US11786678B2 (en) | 2012-06-26 | 2023-10-17 | Resmed Sensor Technologies Limited | Methods and apparatus for monitoring and treating respiratory insufficiency |
| EP4424237A1 (en)* | 2023-02-23 | 2024-09-04 | Draeger Medical Systems, Inc. | Electrocardiogram ("ecg") signal analysis |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US4438771A (en)* | 1982-04-26 | 1984-03-27 | University Of Virginia Alumni Patents Foundation | Passive contactless monitor for detecting cessation of cardiopulmonary |
| US4757825A (en)* | 1985-10-31 | 1988-07-19 | Diamond Research Group, Inc. | Cardio-pulmonary activity monitor |
| US5230094A (en)* | 1992-05-07 | 1993-07-20 | Motorola, Inc. | Method and apparatus for intermediate frequency selection in a heterodyne receiver |
| US5683424A (en)* | 1994-08-30 | 1997-11-04 | The Ohio State University Research Foundation | Non-invasive monitoring and treatment of subjects in cardiac arrest using ECG parameters predictive of outcome |
| US20080074307A1 (en)* | 2006-05-17 | 2008-03-27 | Olga Boric-Lubecke | Determining presence and/or physiological motion of one or more subjects within a doppler radar system |
- 2009
- 2009-04-03CNCN2009801203871Apatent/CN102046076A/enactivePending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US4438771A (en)* | 1982-04-26 | 1984-03-27 | University Of Virginia Alumni Patents Foundation | Passive contactless monitor for detecting cessation of cardiopulmonary |
| US4757825A (en)* | 1985-10-31 | 1988-07-19 | Diamond Research Group, Inc. | Cardio-pulmonary activity monitor |
| US5230094A (en)* | 1992-05-07 | 1993-07-20 | Motorola, Inc. | Method and apparatus for intermediate frequency selection in a heterodyne receiver |
| US5683424A (en)* | 1994-08-30 | 1997-11-04 | The Ohio State University Research Foundation | Non-invasive monitoring and treatment of subjects in cardiac arrest using ECG parameters predictive of outcome |
| US20080074307A1 (en)* | 2006-05-17 | 2008-03-27 | Olga Boric-Lubecke | Determining presence and/or physiological motion of one or more subjects within a doppler radar system |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
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| CN102512141A (en)* | 2011-12-07 | 2012-06-27 | 中国科学院深圳先进技术研究院 | Vital sign monitor |
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| CN104169926A (en)* | 2012-01-19 | 2014-11-26 | 耐克创新有限合伙公司 | Energy expenditure |
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| CN104662584A (en)* | 2012-09-21 | 2015-05-27 | 皇家飞利浦有限公司 | Motion robust vital signal monitoring |
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| CN103054708A (en)* | 2013-01-11 | 2013-04-24 | 湖南纳雷科技有限公司 | Obstructive sleep apnea therapy apparatus |
| CN103006225A (en)* | 2013-01-11 | 2013-04-03 | 湖南纳雷科技有限公司 | Sleep monitoring instrument capable of monitoring breathing state in sleep |
| CN103070687A (en)* | 2013-02-06 | 2013-05-01 | 南京理工大学 | Signal processing algorithm of non-contact type vital sign monitoring system |
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| CN103308893B (en)* | 2013-05-23 | 2016-01-20 | 中国人民解放军第四军医大学 | A kind of UWB bioradar human body echoed signal Enhancement Method based on over-sampling and double sampling |
| CN103308893A (en)* | 2013-05-23 | 2013-09-18 | 中国人民解放军第四军医大学 | UWB (ultra-wide bandwidth) biological radar human body echo signal enhancement method based on oversampling and double sampling |
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| CN104720808A (en)* | 2013-12-20 | 2015-06-24 | 中国移动通信集团公司 | Human sleep respiration detection method and device |
| CN104720808B (en)* | 2013-12-20 | 2017-09-08 | 中国移动通信集团公司 | A kind of method and apparatus for detecting sleep quality breathing |
| CN106061522A (en)* | 2014-02-28 | 2016-10-26 | 勒美加有限公司 | Suctioning device having artificial intelligence |
| US10722151B2 (en) | 2014-05-07 | 2020-07-28 | Siemens Aktiengesellschaft | Magnetic resonance device having a motion detection unit and a method for detecting a movement of a patient during a magnetic resonance examination |
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| CN105476602A (en)* | 2015-11-25 | 2016-04-13 | 方姝阳 | Non-contact human vital sign measurement method and device |
| CN105725994A (en)* | 2015-12-06 | 2016-07-06 | 宣建民 | Rescue instrument with function of sleep monitoring |
| CN105342571A (en)* | 2015-12-09 | 2016-02-24 | 许昌学院 | Intelligent terminal power supply integrated health measurement and assessment system and assessment method thereof |
| CN106990232B (en)* | 2016-01-01 | 2019-05-28 | 海南欣泰康医药科技有限公司 | Blood Kit analysis system and analysis method |
| CN106990232A (en)* | 2016-01-01 | 2017-07-28 | 黄友锋 | Blood Kit analysis system and analysis method |
| CN108778106A (en)* | 2016-03-11 | 2018-11-09 | 欧利景无线有限公司 | Method, apparatus, server and the system for detecting and monitoring for vital sign |
| CN108778106B (en)* | 2016-03-11 | 2021-09-07 | 欧利景无线有限公司 | Method, apparatus, server and system for vital sign detection and monitoring |
| CN109068992A (en)* | 2016-04-15 | 2018-12-21 | 皇家飞利浦有限公司 | Sleep signal regulating device and method |
| CN109068992B (en)* | 2016-04-15 | 2022-04-19 | 皇家飞利浦有限公司 | Sleep signal conditioning apparatus and method |
| CN109068980A (en)* | 2016-04-22 | 2018-12-21 | 诺基亚技术有限公司 | Control the measurement of one or more vital signs of living body main body |
| CN109068980B (en)* | 2016-04-22 | 2021-11-19 | 诺基亚技术有限公司 | Controlling measurement of one or more vital signs of a living subject |
| CN105997086B (en)* | 2016-06-20 | 2019-07-19 | 北京大学 | A kind of breath detection method |
| WO2017219604A1 (en)* | 2016-06-20 | 2017-12-28 | 北京大学 | Respiration detection method |
| CN105997086A (en)* | 2016-06-20 | 2016-10-12 | 北京大学 | Respiration detection method |
| CN107550516A (en)* | 2016-06-30 | 2018-01-09 | 美国西门子医疗解决公司 | Frequency spectrum Doppler detects |
| US10813626B2 (en) | 2016-06-30 | 2020-10-27 | Siemens Medical Solutions Usa, Inc. | Spectral doppler detection |
| WO2018045499A1 (en)* | 2016-09-07 | 2018-03-15 | 中国科学院微电子研究所 | Pulse wave diagnostic system having respiratory wave collection function |
| CN109952058A (en)* | 2016-09-19 | 2019-06-28 | 瑞思迈传感器技术有限公司 | For detecting device, the system and method for physiological movement from audio and multi-modal signal |
| CN106901695B (en)* | 2017-02-22 | 2019-08-23 | 北京理工大学 | A kind of life-signal extraction method and device |
| CN106901695A (en)* | 2017-02-22 | 2017-06-30 | 北京理工大学 | A kind of life-signal extraction method and device |
| CN106913335A (en)* | 2017-03-07 | 2017-07-04 | 南京工业职业技术学院 | A kind of detection method of apnea detection system |
| CN106913335B (en)* | 2017-03-07 | 2020-09-11 | 南京工业职业技术学院 | an apnea detection system |
| CN110650685A (en)* | 2017-03-24 | 2020-01-03 | 爱尔西斯有限责任公司 | Method for assessing a psychophysiological state of a person |
| CN110650685B (en)* | 2017-03-24 | 2024-02-20 | 爱尔西斯有限责任公司 | Method for assessing psychophysiological state of human |
| CN108652632B (en)* | 2017-03-31 | 2023-07-14 | 中国人民解放军总医院 | Forced oscillation pulmonary function test device and method |
| CN108652632A (en)* | 2017-03-31 | 2018-10-16 | 张政波 | Forced oscillation pulmonary function test (pft) device and method |
| CN109745026A (en)* | 2017-11-07 | 2019-05-14 | 深圳欧德蒙科技有限公司 | A kind of method for measuring heart rate and system |
| CN107981844A (en)* | 2017-12-08 | 2018-05-04 | 绵眠（上海）智能科技有限公司 | A kind of sound of snoring recognition methods and system based on piezoelectric membrane |
| CN109907744A (en)* | 2017-12-12 | 2019-06-21 | 昇雷科技股份有限公司 | Contactless Self-Injection Lock Sensor |
| CN109907744B (en)* | 2017-12-12 | 2022-04-08 | 昇雷科技股份有限公司 | Contactless Self-Injection Lock Sensor |
| CN111712161B (en)* | 2017-12-28 | 2023-04-25 | 数眠公司 | Bed with sensor features for determining snoring and breathing parameters of two sleepers |
| CN111712161A (en)* | 2017-12-28 | 2020-09-25 | 数眠公司 | Bed with sensor features to determine snoring and breathing parameters of two sleepers |
| CN108542420B (en)* | 2018-02-23 | 2021-03-16 | 山东沃尔德生物技术有限公司 | Old person nurses system |
| CN108542420A (en)* | 2018-02-23 | 2018-09-18 | 山东沃尔德生物技术有限公司 | A kind of nursing old people system |
| CN111936043A (en)* | 2018-04-12 | 2020-11-13 | 欧姆龙株式会社 | Biological information measurement device, method, and program |
| CN108664912A (en)* | 2018-05-04 | 2018-10-16 | 北京学之途网络科技有限公司 | A kind of information processing method, device, computer storage media and terminal |
| CN108664912B (en)* | 2018-05-04 | 2022-12-20 | 北京学之途网络科技有限公司 | Information processing method and device, computer storage medium and terminal |
| CN108852313A (en)* | 2018-06-08 | 2018-11-23 | 中国科学院计算技术研究所 | Non-interference Intellisense method and system based on radar |
| CN112584753A (en)* | 2018-08-09 | 2021-03-30 | 柯惠有限合伙公司 | Video-based patient monitoring system and related methods for detecting and monitoring respiration |
| CN110907898A (en)* | 2018-09-18 | 2020-03-24 | 天津大学青岛海洋技术研究院 | Doppler radar circuit structure for restraining direct current bias by utilizing radio frequency switch |
| CN109539509A (en)* | 2018-10-23 | 2019-03-29 | 珠海格力电器股份有限公司 | Air conditioner and human body detection method and control method thereof |
| CN111198346B (en)* | 2018-11-20 | 2022-05-31 | 佳能医疗系统株式会社 | Magnetic resonance imaging apparatus |
| CN111198346A (en)* | 2018-11-20 | 2020-05-26 | 佳能医疗系统株式会社 | Magnetic resonance imaging apparatus |
| US11241161B2 (en) | 2018-12-11 | 2022-02-08 | Industrial Technology Research Institute | Method, device and system for measuring physiological state information based on channel state information |
| CN111345793B (en)* | 2018-12-21 | 2023-04-18 | 纬创资通股份有限公司 | Non-contact vital sign detecting system and method |
| TWI687202B (en)* | 2018-12-21 | 2020-03-11 | 緯創資通股份有限公司 | Non-contact vital-sign monitoring system and method |
| CN111345793A (en)* | 2018-12-21 | 2020-06-30 | 纬创资通股份有限公司 | Non-contact vital sign detection system and method |
| CN109730659A (en)* | 2018-12-29 | 2019-05-10 | 广东三水合肥工业大学研究院 | A kind of intelligent mattress based on microwave signal monitoring |
| CN110013252A (en)* | 2019-04-18 | 2019-07-16 | 北京邮电大学 | Method, apparatus, electronic device, and readable storage medium for obtaining respiratory status |
| CN110013252B (en)* | 2019-04-18 | 2021-03-23 | 北京邮电大学 | Method and device for acquiring respiratory state, electronic equipment and readable storage medium |
| CN109965884A (en)* | 2019-04-19 | 2019-07-05 | 哈尔滨理工大学 | A body surface respiratory motion measurement system based on accelerometer |
| CN110448290B (en)* | 2019-08-19 | 2022-05-03 | 中电科思仪科技股份有限公司 | Terahertz through-wall radar-based remote personnel heart rate detection method, device and system |
| CN110448290A (en)* | 2019-08-19 | 2019-11-15 | 中电科仪器仪表有限公司 | A kind of remote personnel's heart rate detection method, apparatus and system based on Terahertz through-wall radar |
| CN112438710A (en)* | 2019-08-30 | 2021-03-05 | 纬创资通股份有限公司 | Dynamic physiological information detection system and method |
| TWI743551B (en)* | 2019-08-30 | 2021-10-21 | 緯創資通股份有限公司 | Dynamic vital-sign detection system and method |
| CN110987150B (en)* | 2019-12-16 | 2021-07-23 | 上海交通大学 | A method and system for ultra-micro-amplitude vibration measurement based on single-frequency continuous wave radar |
| CN110987150A (en)* | 2019-12-16 | 2020-04-10 | 上海交通大学 | A method and system for ultra-micro-amplitude vibration measurement based on single-frequency continuous wave radar |
| CN115515481A (en)* | 2020-05-29 | 2022-12-23 | 深圳迈瑞生物医疗电子股份有限公司 | Monitoring equipment with non-contact physiological sign monitoring function |
| CN111743516A (en)* | 2020-07-08 | 2020-10-09 | 中科芯未来微电子科技成都有限公司 | Household sleep apnea monitoring system |
| CN112205971A (en)* | 2020-09-17 | 2021-01-12 | 四川长虹电器股份有限公司 | Non-contact pulse wave velocity measuring device |
| CN112137604A (en)* | 2020-10-22 | 2020-12-29 | 温州大学 | Respiration and heartbeat detection method and system based on continuous wave Doppler radar |
| CN112220464B (en)* | 2020-10-27 | 2024-11-22 | 广西脉吾科技有限责任公司 | A human breathing and heartbeat signal detection method and system based on UWB radar |
| CN112220464A (en)* | 2020-10-27 | 2021-01-15 | 广西脉吾科技有限责任公司 | Human body respiration and heartbeat signal detection method and system based on UWB radar |
| CN112741760A (en)* | 2021-01-07 | 2021-05-04 | 德阳市人民医院 | Lower limb varicosity rehabilitation treatment system and method for vascular surgery nursing |
| CN112965060A (en)* | 2021-02-19 | 2021-06-15 | 加特兰微电子科技(上海)有限公司 | Detection method and device for vital sign parameters and method for detecting physical sign points |
| CN115153426A (en)* | 2021-04-06 | 2022-10-11 | 光禾感知科技股份有限公司 | Monitoring system and monitoring method for sleep apnea |
| CN113223268A (en)* | 2021-04-23 | 2021-08-06 | 上海为彪汽配制造有限公司 | Safety monitoring method and system for closed area |
| CN114010172A (en)* | 2021-10-22 | 2022-02-08 | 西北工业大学 | A non-contact motion information demodulation method based on arc-string approximation principle |
| CN114041784B (en)* | 2021-11-11 | 2023-09-19 | 天津大学 | A two-person gait disease diagnosis device based on commercial Wi-Fi |
| CN114041784A (en)* | 2021-11-11 | 2022-02-15 | 天津大学 | Two-person gait disorder judgment device based on commercial Wi-Fi |
| CN115089162A (en)* | 2022-05-30 | 2022-09-23 | 合肥工业大学 | A method and device for detecting respiratory rate based on UAV video |
| CN115040092A (en)* | 2022-06-13 | 2022-09-13 | 天津大学 | Heart rate monitoring method and respiratory event detection method based on channel state information |
| CN115736915A (en)* | 2022-11-21 | 2023-03-07 | 大连理工大学 | Patient physical ability assessment method based on multi-source information fusion |
| CN115736915B (en)* | 2022-11-21 | 2024-04-26 | 大连理工大学 | Patient physical fitness assessment method based on multi-source information fusion |
| EP4424237A1 (en)* | 2023-02-23 | 2024-09-04 | Draeger Medical Systems, Inc. | Electrocardiogram ("ecg") signal analysis |
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