技术领域technical field
本发明属于医学测量技术领域，涉及一种无袖带穿戴式无创血压长时连续监测装置。The invention belongs to the technical field of medical measurement, and relates to a cuffless wearable non-invasive blood pressure long-term continuous monitoring device.
背景技术Background technique
目前，心血管疾病的患病率越来越高，因此提高心血管疾病的知晓率、治疗率以及控制率，降低心血管疾病对国民健康的威胁，遏制心血管疾病的上升趋势，是目前面临的重要而艰巨的任务之一。At present, the prevalence of cardiovascular disease is getting higher and higher. Therefore, improving the awareness rate, treatment rate and control rate of cardiovascular disease, reducing the threat of cardiovascular disease to national health, and curbing the rising trend of cardiovascular disease are currently facing the one of the most important and difficult tasks.
与间断测量相比，无创血压连续测量在医学研究和临床上的重要性都越来越突出。无论是日常家庭护理，还是对心血管疾病患者的监护，甚至是在航空航天等特殊职业中的应用，以及利用收缩压和舒张压的斜率作为动态动脉硬化指数(ASSI)来反映动脉硬化等方面，无创血压连续测量都因其能够实时监测动脉血压的波形变化，从而展现出了间断测量无法比拟的优势。Compared with intermittent measurement, continuous measurement of non-invasive blood pressure is becoming more and more important in medical research and clinical practice. Whether it is daily home care, monitoring of patients with cardiovascular disease, or even in special occupations such as aerospace, and the use of the slope of systolic and diastolic blood pressure as a dynamic arteriosclerosis index (ASSI) to reflect aspects such as arteriosclerosis The continuous measurement of non-invasive blood pressure shows the incomparable advantages of intermittent measurement because of its ability to monitor the waveform changes of arterial blood pressure in real time.
近年来，基于可穿戴式设备的人体生理状况监测系统成为生物医学工程领域的研究热点之一。穿戴式设备既要符合人机工程学原理、满足穿戴的舒适性，又要符合医学上生理信号检测的标准、为临床诊断提供依据。In recent years, the human physiological condition monitoring system based on wearable devices has become one of the research hotspots in the field of biomedical engineering. Wearable devices should not only comply with ergonomic principles and meet the comfort of wearing, but also meet the standards of medical physiological signal detection to provide a basis for clinical diagnosis.
基于穿戴式设备的无创血压连续监测系统能够为高血压、冠心病等心血管疾病的预防、诊断以及治疗提供有力帮助。目前，大多数血压监测设备采用的是基于示波法进行测量的，这需要患者长时间佩戴充气袖带，而袖带的长时间束缚会造成患者的强烈不适感，同时也会对患者的日常生活、行动以及睡眠造成严重影响。而基于穿戴式设备的无创血压连续监测系统可以在不影响患者正常生理活动的前提下，对人体血压进行连续非充气式的测量，对患者不会造成严重的不适感。The non-invasive blood pressure continuous monitoring system based on wearable devices can provide powerful help for the prevention, diagnosis and treatment of cardiovascular diseases such as hypertension and coronary heart disease. At present, most blood pressure monitoring equipment is based on the oscillometric method, which requires the patient to wear an inflatable cuff for a long time, and the long-term restraint of the cuff will cause strong discomfort to the patient, and will also affect the patient's daily life. Serious impact on life, movement and sleep. The non-invasive blood pressure continuous monitoring system based on wearable devices can continuously measure the blood pressure of the human body without affecting the normal physiological activities of the patient without causing serious discomfort to the patient.
综上，亟需一种基于无袖带穿戴式设备的无创血压长时连续监测装置，通过心电和脉搏波传感器来监测血压的连续变化，及时了解人体心脑血管的功能情况，为心脑血管病的预防、诊断以及治疗提供丰富、有效的临床可用的诊断依据。In summary, there is an urgent need for a non-invasive long-term continuous monitoring device for blood pressure based on a cuffless wearable device, which can monitor the continuous changes of blood pressure through ECG and pulse wave sensors, and timely understand the function of human cardiovascular and cerebrovascular, so as to provide the The prevention, diagnosis and treatment of vascular disease provide rich and effective clinically available diagnostic evidence.
发明内容SUMMARY OF THE INVENTION
有鉴于此，本发明的目的在于提供一种无袖带穿戴式无创血压长时连续监测装置，实现连续血压无创动态监测的同时，能够及时了解人体心脑血管的功能情况，为心脑血管病的预防、诊断以及治疗提供丰富、有效的临床可用的诊断依据。In view of this, the purpose of the present invention is to provide a cuffless wearable non-invasive blood pressure long-term continuous monitoring device, which can realize the continuous non-invasive dynamic monitoring of blood pressure, and can timely understand the function of human cardiovascular and cerebrovascular diseases. The prevention, diagnosis and treatment of the disease provide a rich and effective clinically available diagnostic basis.
为达到上述目的，本发明提供如下技术方案：To achieve the above object, the present invention provides the following technical solutions:
一种无袖带穿戴式无创血压长时连续监测装置，包括上位机端和下位机端；所述下位机端包括：信号采集模块、信号处理模块、微控制器模块、数据存储模块、数据传输模块、LCD显示模块以及电源模块；所述上位机端包括：基于移动终端的APP或基于PC的应用软件；所述信号采集模块包括：心电信号检测模块、脉搏波信号检测模块以及三轴加速度信号检测模块；所述信号处理模块包括心电信号处理模块以及脉搏波信号处理模块；A cuffless wearable non-invasive blood pressure long-term continuous monitoring device includes an upper computer end and a lower computer end; the lower computer end includes: a signal acquisition module, a signal processing module, a microcontroller module, a data storage module, a data transmission module module, LCD display module and power supply module; the host computer end includes: mobile terminal-based APP or PC-based application software; the signal acquisition module includes: ECG signal detection module, pulse wave signal detection module and triaxial acceleration a signal detection module; the signal processing module includes an ECG signal processing module and a pulse wave signal processing module;
所述数据存储模块用于将经过滤波处理的心电信号、脉搏波信号以及三轴加速度信号存储至SD卡中；The data storage module is used to store the filtered ECG signal, pulse wave signal and triaxial acceleration signal in the SD card;
所述LCD显示模块用于将经过滤波处理的心电信号以及脉搏波信号进行实时显示，并通过微控制器模块内置的血压预测算法得到每个心跳节拍的血压值；The LCD display module is used to display the filtered ECG signal and the pulse wave signal in real time, and obtain the blood pressure value of each heartbeat beat through the built-in blood pressure prediction algorithm of the microcontroller module;
所述数据传输模块用于将经过滤波处理的心电信号、脉搏波信号以及三轴加速度信号实时传输至上位机端，上位机端对信号进一步的处理和分析，得到被测者除血压外更多的生理特征参数。The data transmission module is used to transmit the filtered ECG signal, pulse wave signal and triaxial acceleration signal to the host computer in real time. many physiological parameters.
进一步，所述心电信号检测模块由贴片电极及导联线组成；所述脉搏波信号采集模块由基于压电聚偏氟乙烯(Piezoelectric Polyvinylidene Fluoride，PVDF)的脉搏波传感器及其导联线组成，所述三轴加速度信号采集模块由三轴加速度传感器组成。Further, the ECG signal detection module is composed of patch electrodes and lead wires; the pulse wave signal acquisition module is composed of a piezoelectric polyvinylidene fluoride (Piezoelectric Polyvinylidene Fluoride, PVDF)-based pulse wave sensor and its lead wires The three-axis acceleration signal acquisition module is composed of a three-axis acceleration sensor.
进一步，所述心电信号处理模块由前置放大电路、0.5-100Hz带通滤波电路、50Hz陷波电路、二级放大电路、光耦隔离电路、电平抬升电路组成；所述脉搏波处理电路由前置放大电路、0.1-20Hz带通滤波电路、二级放大电路、电平抬升电路组成。Further, the ECG signal processing module is composed of a preamplifier circuit, a 0.5-100Hz bandpass filter circuit, a 50Hz trap circuit, a secondary amplifier circuit, an optocoupler isolation circuit, and a level boost circuit; the pulse wave processing circuit It consists of a preamplifier circuit, a 0.1-20Hz band-pass filter circuit, a secondary amplifier circuit, and a level boost circuit.
所述基于移动终端的APP与下位机端之间采用蓝牙方式进行数据传输；所述基于PC的应用软件通过读取下位机端的SD卡来获取下位机端采集的信号数据。The data transmission between the mobile terminal-based APP and the lower computer is performed by means of Bluetooth; the PC-based application software obtains the signal data collected by the lower computer by reading the SD card of the lower computer.
进一步，所述上位机端利用基于三轴加速度传感器信号进行相关信号处理，在上位机端对心电信号以及脉搏波信号的运动伪迹和基线漂移进行滤除，并对滤波后的心电信号和脉搏波信号进行特征点识别，利用内置的基于脉搏波传导时间及脉搏波特征参数的连续血压预测模型，实时计算每搏舒张压和每搏收缩压，并对计算得到的每搏舒张压和每搏收缩压在移动终端的APP或PC端的应用软件上进行显示和分析。所述基于移动终端的APP能够将经过滤波处理的心电信号和脉搏波信号、计算得到的每搏舒张压和每搏收缩压数据上传至云平台，以供医生查看及诊断。Further, the host computer performs related signal processing based on the triaxial acceleration sensor signal, filters out the motion artifact and baseline drift of the ECG signal and the pulse wave signal on the host computer, and filters the filtered ECG signal. Perform feature point identification with the pulse wave signal, and use the built-in continuous blood pressure prediction model based on pulse wave transit time and pulse wave characteristic parameters to calculate the diastolic blood pressure and systolic blood pressure per pulse in real time. The systolic blood pressure per beat is displayed and analyzed on the APP of the mobile terminal or the application software of the PC terminal. The mobile terminal-based APP can upload the filtered ECG signal and pulse wave signal, calculated diastolic blood pressure per beat and systolic blood pressure per beat data to the cloud platform for doctors to view and diagnose.
进一步，基于三轴加速度传感器信号的运动伪迹滤除，包括以下步骤：Further, the motion artifact filtering based on the three-axis acceleration sensor signal includes the following steps:
(1)基于三轴加速度传感器信号的处理暂停：根据三轴加速度传感器得到的加速度数据，利用事先设定好的阈值athreshold进行判断，从而得出使用者是否处于剧烈运动状态，进而判断是否暂停数据采集；(1) Suspension of processing based on three-axis acceleration sensor signals: According to the acceleration data obtained by the three-axis acceleration sensor, the pre-set threshold athreshold is used to determine whether the user is in a state of vigorous exercise, and then determine whether to pause or not. data collection;
(2)基于三轴加速度传感器信号的运动伪迹滤除：首先，利用三轴加速度传感器测量得到心电信号/脉搏波信号，包含了人体的心电信号/脉搏波信号以及运动带来的干扰信号；在采集心电信号/脉搏波信号的同时，通过三轴加速度传感器采集人体的运动信号并以此作为自适应滤波器的参考输入信号，然后使用自适应滤波器对心电信号/脉搏波信号进行滤波处理，得到去除运动干扰的心电信号/脉搏波信号。(2) Motion artifact filtering based on triaxial acceleration sensor signal: First, the ECG signal/pulse wave signal is obtained by measuring the triaxial acceleration sensor, including the human body's ECG signal/pulse wave signal and the interference caused by movement Signal; while collecting the ECG signal/pulse wave signal, the motion signal of the human body is collected by the three-axis acceleration sensor and used as the reference input signal of the adaptive filter, and then the adaptive filter is used to analyze the ECG signal/pulse wave signal. The signal is filtered to obtain the ECG signal/pulse wave signal with the motion interference removed.
进一步，所述基于三轴加速度传感器信号的暂停处理判断规则为：当三轴加速度传感器的总加速度则暂停处理；否则继续基于三轴加速度传感器信号的处理；其中，ax、ay、az分别为三轴加速度传感器信号在x、y、z轴上的分加速度。Further, the judgment rule for the suspension processing based on the triaxial acceleration sensor signal is: when the total acceleration of the triaxial acceleration sensor Then the processing is suspended; otherwise, the processing based on the three-axis acceleration sensor signal is continued; wherein ax , ay , and az are the component accelerations of the three-axis acceleration sensor signal on the x, y, and z axes, respectively.
进一步，所述脉搏波信号的滤波方法为基于双树复小波和三次样条插值的脉搏波信号去噪算法，具体包括以下步骤：Further, the filtering method of the pulse wave signal is a pulse wave signal denoising algorithm based on dual-tree complex wavelet and cubic spline interpolation, which specifically includes the following steps:
(1)对原始含噪脉搏波信号进行双树复小波分解，对各层小波系数采用贝叶斯最大后验估计阈值去噪；(1) Perform dual-tree complex wavelet decomposition on the original noisy pulse wave signal, and use the Bayesian maximum a posteriori estimation threshold to denoise the wavelet coefficients of each layer;
(2)进行双树复小波逆变换，得到滤除高频噪声后的脉搏波信号；(2) Inverse double-tree complex wavelet transform is performed to obtain the pulse wave signal after filtering out high-frequency noise;
(3)将得到的滤除了高频噪声的脉搏波信号采用滑窗法检测出信号中的波谷点；(3) adopting the sliding window method to detect the trough point in the obtained pulse wave signal that has filtered out the high-frequency noise;
(4)采用三次样条插值法拟合出近似基线漂移曲线；(4) Using cubic spline interpolation method to fit approximate baseline drift curve;
(5)用滤除了高频噪声的脉搏波信号减去拟合出的基线漂移曲线，从而实现高频噪声及基线漂移的滤除。(5) The fitted baseline drift curve is subtracted from the pulse wave signal from which the high frequency noise has been filtered, thereby realizing the filtering of the high frequency noise and the baseline drift.
进一步，所述心电信号的滤波方法为基于双树复小波和形态学滤波的心电信号去噪算法，具体包括以下步骤：Further, the filtering method of the ECG signal is an ECG signal denoising algorithm based on dual-tree complex wavelet and morphological filtering, which specifically includes the following steps:
(1)对原始含噪心电信号进行双树复小波分解，对各层小波系数采用贝叶斯最大后验估计阈值去噪；(1) Perform dual-tree complex wavelet decomposition on the original noisy ECG signal, and use the Bayesian maximum a posteriori estimation threshold to denoise the wavelet coefficients of each layer;
(2)进行双树复小波逆变换，得到滤除高频噪声后的心电信号；(2) Inverse double-tree complex wavelet transform is performed to obtain the ECG signal after filtering out high-frequency noise;
(3)采用扁平型结构元素对滤除高频噪声的心电信号进行形态学开运算滤波，滤除心电信号中的正脉冲；(3) The flat structure element is used to perform morphological open operation filtering on the ECG signal that filters out the high-frequency noise, and the positive pulse in the ECG signal is filtered out;
(4)对滤除了正脉冲的心电信号进行形态学闭运算滤波，消除心电信号中的负脉冲，从而得到滤除了正脉冲和负脉冲的信号序列，即为基线漂移量；(4) Perform morphological closed operation filtering on the ECG signal with positive pulses filtered out to eliminate negative pulses in the ECG signal, thereby obtaining a signal sequence with positive and negative pulses filtered out, which is the baseline drift amount;
(5)用步骤(2)中得到的去除了高频噪声的心电信号减去步骤(4)式中的基线漂移量，从而得到不含高频噪声和基线漂移的心电信号。(5) The baseline drift amount in the formula in step (4) is subtracted from the ECG signal obtained in step (2) with the high frequency noise removed, so as to obtain the ECG signal without high frequency noise and baseline drift.
进一步，对滤波后的脉搏波信号进行特征点识别的具体步骤为：Further, the specific steps of identifying the characteristic points of the filtered pulse wave signal are as follows:
(1)采用滑窗法检测出脉搏波信号波谷点位置，即为b点的位置；(1) The position of the trough point of the pulse wave signal is detected by the sliding window method, which is the position of point b;
(2)在相邻两个b点之间寻找最大值，即为主波波峰c点的位置；(2) Find the maximum value between two adjacent b points, that is, the position of the main wave peak c point;
(3)求脉搏波信号的一阶差分信号，在指定范围中寻找极值点，若有则在这一范围内寻找极大值的最大值以及极小值的最小值，分别对应特征点g和f；若在指定范围中无极值点，则求脉搏波信号的二阶差分信号并判断这一范围内是否有拐点，有则求曲率最小和最大的两个点，分别对应特征点g和f；(3) Find the first-order differential signal of the pulse wave signal, find the extreme point in the specified range, if there is, find the maximum value of the maximum value and the minimum value of the minimum value within this range, corresponding to the feature point g respectively and f; if there is no extreme point in the specified range, find the second-order differential signal of the pulse wave signal and determine whether there is an inflection point in this range, and find the two points with the smallest and largest curvature, corresponding to the feature points g and f respectively. f;
(4)求脉搏波信号的一阶差分信号在c点和f点之间寻找极小值点，若有则寻找极小值的最小值作为特征点d；若无，则求脉搏波信号的二阶差分信号并寻找这一范围的拐点，即为特征点d；(4) Find the first-order differential signal of the pulse wave signal Find the minimum point between point c and point f, if there is, find the minimum value of the minimum value as the characteristic point d; if not, find the minimum value of the pulse wave signal The second-order differential signal and looking for the inflection point in this range is the characteristic point d;
(5)对脉搏波信号进行5层双树复小波分解，d5层信号中特征点d和f对应位置之间存在最大值点对应原脉搏波信号中的特征点e。(5) Perform 5-layer dual-tree complex wavelet decomposition on the pulse wave signal. There is a maximum point between the corresponding positions of the feature points d and f in the d5-layer signal, which corresponds to the feature point e in the original pulse wave signal.
进一步，对滤波后的心电信号进行特征点识别的具体步骤为：Further, the specific steps of performing feature point identification on the filtered ECG signal are as follows:
(1)对滤波后的心电信号进行4层双树复小波分解；(1) Perform 4-layer dual-tree complex wavelet decomposition on the filtered ECG signal;
(2)采用滑窗法识别出d4层心电信号的模极大值点；(2) Using the sliding window method to identify the modulo maximum point of the d4-layer ECG signal;
(3)对应回滤波后的心电信号中，从而实现心电信号R波的识别。(3) Corresponding to the filtered ECG signal, so as to realize the identification of the R wave of the ECG signal.
本发明的有益效果在于：本发明实现了无创血压长时连续动态连续监测，从而能够及时了解人体心脑血管的功能情况，为心脑血管病的预防、诊断以及治疗提供丰富、有效的临床可用的诊断依据。The beneficial effects of the present invention are: the present invention realizes long-term continuous dynamic continuous monitoring of non-invasive blood pressure, so that the function of human cardiovascular and cerebrovascular can be known in time, and the prevention, diagnosis and treatment of cardiovascular and cerebrovascular diseases provide rich and effective clinically available diagnostic basis.
本发明的其他优点、目标和特征在某种程度上将在随后的说明书中进行阐述，并且在某种程度上，基于对下文的考察研究对本领域技术人员而言将是显而易见的，或者可以从本发明的实践中得到教导。本发明的目标和其他优点可以通过下面的说明书来实现和获得。Other advantages, objects, and features of the present invention will be set forth in the description that follows, and will be apparent to those skilled in the art based on a study of the following, to the extent that is taught in the practice of the present invention. The objectives and other advantages of the present invention may be realized and attained by the following description.
附图说明Description of drawings
为了使本发明的目的、技术方案和优点更加清楚，下面将结合附图对本发明作优选的详细描述，其中：In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be preferably described in detail below with reference to the accompanying drawings, wherein:
图1为本发明所述连续监测装置结构图；Fig. 1 is the structure diagram of the continuous monitoring device according to the present invention;
图2为基于三轴加速度传感器的信号处理暂停的流程图；Fig. 2 is a flow chart of signal processing suspension based on three-axis acceleration sensor;
图3为基于三轴加速度传感器的运动伪迹滤除的流程示意图；3 is a schematic flowchart of motion artifact filtering based on a three-axis acceleration sensor;
图4为心电信号去噪流程图；Fig. 4 is the flow chart of denoising of ECG signal;
图5为心电信号R波识别流程图。Fig. 5 is a flow chart of R wave identification of ECG signal.
具体实施方式Detailed ways
以下通过特定的具体实例说明本发明的实施方式，本领域技术人员可由本说明书所揭示的内容轻易地了解本发明的其他优点与功效。本发明还可以通过另外不同的具体实施方式加以实施或应用，本说明书中的各项细节也可以基于不同观点与应用，在没有背离本发明的精神下进行各种修饰或改变。需要说明的是，以下实施例中所提供的图示仅以示意方式说明本发明的基本构想，在不冲突的情况下，以下实施例及实施例中的特征可以相互组合。其中，附图仅用于示例性说明，表示的仅是示意图，而非实物图，不能理解为对本发明的限制。The embodiments of the present invention are described below through specific specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the drawings provided in the following embodiments are only used to illustrate the basic idea of the present invention in a schematic manner, and the following embodiments and features in the embodiments can be combined with each other without conflict. Wherein, the accompanying drawings are only used for exemplary description, and are only schematic diagrams rather than actual drawings, and should not be construed as limiting the present invention.
如图1所示，本发明提供的一种无袖带穿戴式无创血压长时连续监测装置，包括上位机端和下位机端；下位机端包括：信号采集模块、信号处理模块、微控制器模块、数据存储模块、数据传输模块、LCD显示模块以及电源模块；上位机端包括：基于移动终端的APP或基于PC的应用软件。信号采集模块包括：心电信号检测模块、脉搏波信号检测模块以及三轴加速度信号检测模块。As shown in FIG. 1 , a cuffless wearable non-invasive blood pressure long-term continuous monitoring device provided by the present invention includes an upper computer end and a lower computer end; the lower computer end includes: a signal acquisition module, a signal processing module, a microcontroller module, data storage module, data transmission module, LCD display module and power supply module; the host computer includes: APP based on mobile terminal or application software based on PC. The signal acquisition module includes: an ECG signal detection module, a pulse wave signal detection module and a triaxial acceleration signal detection module.
信号处理模块包括心电信号处理模块以及脉搏波信号处理模块；心电信号检测模块由贴片电极及导联线组成；所述脉搏波信号采集模块由基于PVDF的脉搏波传感器及其导联线组成，所述三轴加速度信号采集模块由三轴加速度传感器组成。心电信号处理模块主要由前置放大电路、0.5-100Hz带通滤波电路、50Hz陷波电路、二级放大电路、光耦隔离电路、电平抬升电路组成；所述脉搏波处理电路主要由前置放大电路、0.1-20Hz带通滤波电路、二级放大电路、电平抬升电路组成。The signal processing module includes an ECG signal processing module and a pulse wave signal processing module; the ECG signal detection module is composed of patch electrodes and lead wires; the pulse wave signal acquisition module is composed of a PVDF-based pulse wave sensor and its lead wires The three-axis acceleration signal acquisition module is composed of a three-axis acceleration sensor. The ECG signal processing module is mainly composed of a preamplifier circuit, a 0.5-100Hz band-pass filter circuit, a 50Hz trap circuit, a secondary amplifier circuit, an optocoupler isolation circuit, and a level boost circuit; the pulse wave processing circuit is mainly composed of a preamplifier circuit. It consists of amplifier circuit, 0.1-20Hz band-pass filter circuit, secondary amplifier circuit and level boost circuit.
数据存储模块将经过滤波处理的心电信号、脉搏波信号以及三轴加速度信号存储至SD卡中；LCD显示模块将经过滤波处理的心电信号以及脉搏波信号进行实时显示，并通过微控制器模块内置的血压预测算法得到每个心跳节拍的血压值；数据传输模块将经过滤波处理的心电信号、脉搏波信号以及三轴加速度信号实时传输至上位机端，可以是移动终端或PC，通过移动终端的APP或基于PC的应用软件对信号进一步的处理和分析，得到被测者除血压外更多的生理特征参数。The data storage module stores the filtered ECG signal, pulse wave signal and triaxial acceleration signal in the SD card; the LCD display module displays the filtered ECG signal and pulse wave signal in real time, and displays it through the microcontroller The built-in blood pressure prediction algorithm of the module obtains the blood pressure value of each heartbeat; the data transmission module transmits the filtered ECG signal, pulse wave signal and triaxial acceleration signal to the host computer in real time, which can be a mobile terminal or a PC. The APP of the mobile terminal or the PC-based application software further processes and analyzes the signal, and obtains more physiological characteristic parameters of the subject except blood pressure.
基于移动终端的APP与下位机端之间采用蓝牙方式进行数据传输；所述基于PC的应用软件通过读取下位机端的SD卡来获取下位机端采集的信号数据。Bluetooth is used for data transmission between the mobile terminal-based APP and the lower computer; the PC-based application software obtains the signal data collected by the lower computer by reading the SD card of the lower computer.
上位机端利用基于三轴加速度传感器信号进行相关信号处理，在上位机端对心电信号以及脉搏波信号的运动伪迹和基线漂移等噪声进行滤除，并对滤波后的心电信号和脉搏波信号进行特征点识别，利用内置的基于脉搏波传导时间及脉搏波特征参数的连续血压预测模型，实时计算每搏舒张压和每搏收缩压，并对计算得到的每搏舒张压和每搏收缩压在移动终端的APP或PC端的应用软件上进行显示和分析。其中，基于移动终端的APP能够将经过滤波处理的心电信号和脉搏波信号、计算得到的每搏舒张压和每搏收缩压数据上传至云平台，以供医生查看及诊断。The host computer uses the signal based on the three-axis acceleration sensor to perform relevant signal processing, filters out the motion artifacts and baseline drift of the ECG signal and the pulse wave signal on the host computer, and filters the ECG signal and pulse wave. Identify the characteristic points of the wave signal, and use the built-in continuous blood pressure prediction model based on the pulse wave transit time and pulse wave characteristic parameters to calculate the diastolic blood pressure and the systolic blood pressure in real time. The systolic blood pressure is displayed and analyzed on the APP of the mobile terminal or the application software of the PC. Among them, the APP based on the mobile terminal can upload the filtered ECG signal and pulse wave signal, the calculated diastolic blood pressure per beat and systolic blood pressure data to the cloud platform for doctors to view and diagnose.
所述连续血压预测模型能够实现无袖带式血压无创动态长时监测过程中的模型结构及不同神经元之间连接权值的自适应动态调整，保证整个监测过程的血压预测精度，实现真正的动态连续节拍的血压长时监测，避免有创测量带来的创伤及袖带血压监测充放气的束缚。某一时刻的血压预测模型是由软件子系统根据测量得到的心电信号和光电容积脉搏波信号的特征参数自适应从血压无创动态监测模型簇中匹配类别确定的，在血压动态长时测量过程中实现血压预测模型的自校正，而不需要进行血压预测模型的人为校正。The continuous blood pressure prediction model can realize the adaptive dynamic adjustment of the model structure and the connection weights between different neurons in the non-invasive dynamic long-term monitoring process of cuffless blood pressure, ensure the blood pressure prediction accuracy of the whole monitoring process, and realize the real Long-term monitoring of blood pressure with dynamic continuous beats avoids the trauma caused by invasive measurement and the constraints of inflation and deflation of cuff blood pressure monitoring. The blood pressure prediction model at a certain time is determined by the software subsystem according to the characteristic parameters of the measured ECG signal and the photoplethysmography signal adaptively from the matching categories in the blood pressure non-invasive dynamic monitoring model cluster. During the dynamic long-term blood pressure measurement process The self-correction of the blood pressure prediction model is realized in the system without manual correction of the blood pressure prediction model.
基于三轴加速度传感器信号的运动伪迹滤除，包括以下步骤：The motion artifact filtering based on the three-axis accelerometer signal includes the following steps:
1)基于三轴加速度传感器信号的处理暂停：根据三轴加速度传感器得到的加速度数据，利用事先设定好的阈值athreshold进行判断，从而得出使用者是否处于剧烈运动状态，进而判断是否暂停数据采集。1) The processing based on the three-axis acceleration sensor signal is suspended: according to the acceleration data obtained by the three-axis acceleration sensor, the pre-set threshold athreshold is used to judge, so as to determine whether the user is in a state of vigorous exercise, and then judge whether to suspend the data collection.
如图2所示，判断规则为：当三轴加速度传感器的总加速度则暂停处理；否则继续基于三轴加速度传感器信号的处理；其中，ax、ay、az分别为三轴加速度传感器信号在x、y、z轴上的分加速度。As shown in Figure 2, the judgment rule is: when the total acceleration of the three-axis acceleration sensor Then the processing is suspended; otherwise, the processing based on the three-axis acceleration sensor signal is continued; wherein ax , ay , and az are the component accelerations of the three-axis acceleration sensor signal on the x, y, and z axes, respectively.
2)基于三轴加速度传感器信号的运动伪迹滤除，如图3所示：首先，利用三轴加速度传感器测量得到心电信号/脉搏波信号，包含了人体的心电信号/脉搏波信号以及运动带来的干扰信号；在采集心电信号/脉搏波信号的同时，通过三轴加速度传感器采集人体的运动信号(即加速度信号)并以此作为自适应滤波器的参考输入信号，然后使用自适应滤波器对心电信号/脉搏波信号进行滤波处理，得到去除运动干扰的心电信号/脉搏波信号。2) The motion artifact filtering based on the triaxial acceleration sensor signal, as shown in Figure 3: First, the ECG signal/pulse wave signal is obtained by measuring the triaxial acceleration sensor, including the human body ECG signal/pulse wave signal and The interference signal caused by motion; while collecting the ECG signal/pulse wave signal, the motion signal (ie acceleration signal) of the human body is collected by the three-axis acceleration sensor and used as the reference input signal of the adaptive filter, and then the automatic The adaptive filter performs filtering processing on the ECG signal/pulse wave signal to obtain the ECG signal/pulse wave signal from which the motion interference is removed.
脉搏波信号的滤波方法为基于双树复小波和三次样条插值的脉搏波信号去噪算法，具体包括以下步骤：The filtering method of the pulse wave signal is a pulse wave signal denoising algorithm based on double tree complex wavelet and cubic spline interpolation, which specifically includes the following steps:
(1)对原始含噪脉搏波信号进行双树复小波分解，对各层小波系数采用贝叶斯最大后验估计阈值去噪；(1) Perform dual-tree complex wavelet decomposition on the original noisy pulse wave signal, and use the Bayesian maximum a posteriori estimation threshold to denoise the wavelet coefficients of each layer;
(2)进行双树复小波逆变换，得到滤除高频噪声后的脉搏波信号；(2) Inverse double-tree complex wavelet transform is performed to obtain the pulse wave signal after filtering out high-frequency noise;
(3)将得到的滤除了高频噪声的脉搏波信号采用滑窗法检测出信号中的波谷点；(3) adopting the sliding window method to detect the trough point in the obtained pulse wave signal that has filtered out the high-frequency noise;
(4)采用三次样条插值法拟合出近似基线漂移曲线；(4) Using cubic spline interpolation method to fit approximate baseline drift curve;
(5)用滤除了高频噪声的脉搏波信号减去拟合出的基线漂移曲线，从而实现高频噪声及基线漂移的滤除。(5) The fitted baseline drift curve is subtracted from the pulse wave signal from which the high frequency noise has been filtered, thereby realizing the filtering of the high frequency noise and the baseline drift.
如图4所示，心电信号的滤波方法为基于双树复小波和形态学滤波的心电信号去噪算法，具体包括以下步骤：As shown in Figure 4, the filtering method of the ECG signal is an ECG signal denoising algorithm based on dual-tree complex wavelet and morphological filtering, which specifically includes the following steps:
(1)对原始含噪心电信号进行双树复小波分解，对各层小波系数采用贝叶斯最大后验估计阈值去噪；(1) Perform dual-tree complex wavelet decomposition on the original noisy ECG signal, and use the Bayesian maximum a posteriori estimation threshold to denoise the wavelet coefficients of each layer;
(2)进行双树复小波逆变换，得到滤除高频噪声后的心电信号；(2) Inverse double-tree complex wavelet transform is performed to obtain the ECG signal after filtering out high-frequency noise;
(3)采用扁平型结构元素对滤除高频噪声的心电信号进行形态学开运算滤波，滤除心电信号中的正脉冲；(3) The flat structure element is used to perform morphological open operation filtering on the ECG signal that filters out the high-frequency noise, and the positive pulse in the ECG signal is filtered out;
(4)对滤除了正脉冲的心电信号进行形态学闭运算滤波，消除心电信号中的负脉冲，从而得到滤除了正脉冲和负脉冲的信号序列，即为基线漂移量；(4) Perform morphological closed operation filtering on the ECG signal with positive pulses filtered out to eliminate negative pulses in the ECG signal, thereby obtaining a signal sequence with positive and negative pulses filtered out, which is the baseline drift amount;
(5)用步骤(2)中得到的去除了高频噪声的心电信号减去步骤(4)式中的基线漂移量，从而得到不含高频噪声和基线漂移的心电信号。(5) The baseline drift amount in the formula in step (4) is subtracted from the ECG signal obtained in step (2) with the high frequency noise removed, so as to obtain the ECG signal without high frequency noise and baseline drift.
对滤波后的脉搏波信号进行特征点识别的具体步骤为：The specific steps of identifying the feature points of the filtered pulse wave signal are as follows:
(1)采用滑窗法检测出脉搏波信号波谷点位置，即为b点的位置；(1) The position of the trough point of the pulse wave signal is detected by the sliding window method, which is the position of point b;
(2)在相邻两个b点之间寻找最大值，即为主波波峰c点的位置；(2) Find the maximum value between two adjacent b points, that is, the position of the main wave peak c point;
(3)求脉搏波信号的一阶差分信号，在指定范围中寻找极值点，若有则在这一范围内寻找极大值的最大值以及极小值的最小值，分别对应特征点g和f；若在指定范围中无极值点，则求脉搏波信号的二阶差分信号并判断这一范围内是否有拐点，有则求曲率最小和最大的两个点，分别对应特征点g和f；(3) Find the first-order differential signal of the pulse wave signal, find the extreme point in the specified range, if there is, find the maximum value of the maximum value and the minimum value of the minimum value within this range, corresponding to the feature point g respectively and f; if there is no extreme point in the specified range, find the second-order differential signal of the pulse wave signal and determine whether there is an inflection point in this range, and find the two points with the smallest and largest curvature, corresponding to the feature points g and f respectively. f;
(4)求脉搏波信号的一阶差分信号在c点和f点之间寻找极小值点，若有则寻找极小值的最小值作为特征点d；若无，则求脉搏波信号的二阶差分信号并寻找这一范围的拐点，即为特征点d；(4) Find the first-order differential signal of the pulse wave signal Find the minimum point between point c and point f, if there is, find the minimum value of the minimum value as the characteristic point d; if not, find the minimum value of the pulse wave signal The second-order differential signal and looking for the inflection point in this range is the characteristic point d;
(5)对脉搏波信号进行5层双树复小波分解，d5层信号中特征点d和f对应位置之间存在最大值点对应原脉搏波信号中的特征点e。(5) Perform 5-layer dual-tree complex wavelet decomposition on the pulse wave signal. There is a maximum point between the corresponding positions of the feature points d and f in the d5-layer signal, which corresponds to the feature point e in the original pulse wave signal.
如图5所示，对滤波后的心电信号R波识别的具体步骤为：As shown in Figure 5, the specific steps for identifying the R wave of the filtered ECG signal are:
(1)对滤波后的心电信号进行4层双树复小波分解；(1) Perform 4-layer dual-tree complex wavelet decomposition on the filtered ECG signal;
(2)采用滑窗法识别出d4层心电信号的模极大值点；(2) Using the sliding window method to identify the modulo maximum point of the d4-layer ECG signal;
(3)对应回滤波后的心电信号中，从而实现心电信号R波的识别。(3) Corresponding to the filtered ECG signal, so as to realize the identification of the R wave of the ECG signal.
本发明所述无袖带穿戴式无创血压长时连续监测装置的操作流程为：The operation process of the cuffless wearable non-invasive blood pressure long-term continuous monitoring device of the present invention is as follows:
(1)将心电电极贴在身体指定部位，并将心电导联线与心电电极连接好；(1) Attach the ECG electrodes to the designated parts of the body, and connect the ECG lead wires to the ECG electrodes;
(2)将压电聚偏氟乙烯脉搏波传感器安放在手腕指定部位，并将脉搏波导联线安装好；(2) Place the piezoelectric polyvinylidene fluoride pulse wave sensor on the designated part of the wrist, and install the pulse wave lead wire;
(3)将检测装置佩戴在身体指定部位，打开检测装置，开始记录数据；(3) Wear the detection device on the designated part of the body, turn on the detection device, and start recording data;
(4)打开Android手机蓝牙，与检测装置的蓝牙进行配对，同时打开Android手机APP，检测装置采集到的心电信号和脉搏波信号将在APP上实时显示，同时，每搏收缩压、每搏舒张压以及血液黏度值也将在APP上进行显示。(4) Turn on the Bluetooth of the Android mobile phone, pair it with the Bluetooth of the detection device, and open the Android mobile phone APP at the same time, the ECG signal and pulse wave signal collected by the detection device will be displayed on the APP in real time. Diastolic blood pressure and blood viscosity values will also be displayed on the APP.
(5)在记录完24小时数据之后，关闭检测装置，取出SD卡，插入PC端，打开PC端的应用软件，读取SD卡中的数据，并在应用软件的界面上显示心电信号、脉搏波信号及连续血压波形。(5) After recording 24 hours of data, close the detection device, take out the SD card, insert it into the PC, open the application software on the PC, read the data in the SD card, and display the ECG signal and pulse on the interface of the application software. Wave signal and continuous blood pressure waveform.
最后说明的是，以上实施例仅用以说明本发明的技术方案而非限制，尽管参照较佳实施例对本发明进行了详细说明，本领域的普通技术人员应当理解，可以对本发明的技术方案进行修改或者等同替换，而不脱离本技术方案的宗旨和范围，其均应涵盖在本发明的权利要求范围当中。Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be Modifications or equivalent replacements, without departing from the spirit and scope of the technical solution, should all be included in the scope of the claims of the present invention.
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| RJ01 | Rejection of invention patent application after publication | Application publication date:20190705 |