Disclosure of Invention
Aiming at the defects of the prior art, the application provides a ventilation detection method for a myasthenia gravis patient, which is applied to a respiratory assistance system and comprises the steps of monitoring the muscle activity of a deglutition muscle and the muscle activity of a limb near end of the myasthenia gravis patient, obtaining muscle strength data of the myasthenia gravis patient according to the monitoring data of the muscle activity of the deglutition muscle and the monitoring data of the muscle activity of the limb near end, detecting the respiratory rate, the tidal volume of each respiratory cycle and the inspiration time of each respiratory cycle under the condition of mechanically assisting ventilation of the myasthenia gravis patient, acquiring the change trend of the respiratory rate, the tidal volume of each respiratory cycle and the inspiration time along with time, and determining that man-machine resistance exists in the mechanically assisting ventilation process of the myasthenia gravis patient when the muscle strength data of the myasthenia gravis patient is lower than a preset muscle strength threshold and the change trend of the respiratory rate, the tidal volume of each respiratory cycle and the inspiration time along with the change trend of pre-stored corresponding parameters.
According to a preferred embodiment, obtaining muscle strength data of a patient suffering from myasthenia gravis based on the monitoring data of the muscle activity of the deglutition muscle and the monitoring data of the muscle activity of the proximal end of the limb comprises collecting muscle strength data of a first period of time when the patient performs a first standardized action as reference muscle strength data, collecting muscle strength data of a second period of time of a preset period of time after the patient performs the first standardized action, and analyzing muscle strength fluctuations based on the muscle strength data of the second period of time and the reference muscle strength data.
According to a preferred embodiment, the first standardized action is a swallowing and/or an arm raising action.
According to a preferred embodiment, after determining that there is a human-machine resistance during mechanical assisted ventilation of a myasthenia gravis patient, the method further comprises determining the type of human-machine resistance based on the respiratory rate, tidal volume per respiratory cycle, and the trend of inspiration time over time.
According to a preferred embodiment, if the respiratory rate increases with fluctuations in tidal volume waveform, a determination is made that there is a failure to effectively trigger ventilator delivery.
According to a preferred embodiment, if the respiratory rate increases with an increase in inspiration time, a ventilator under-delivery is determined.
According to a preferred embodiment, if the tidal volume is below a preset tidal volume threshold, the inspiration time is shorter than the preset inspiration time threshold and the breathing frequency is increased, then it is determined that an early expiration trigger is present for the ventilator.
By applying the ventilation detection method, whether the condition of man-machine resistance exists and the type of man-machine resistance can be judged according to the condition characteristics of the myasthenia gravis patient and the muscle strength of the myasthenia gravis patient and the variation trend of tidal volume, respiratory frequency and inspiration time in the mechanical auxiliary expiration process of the myasthenia gravis patient. The method is helpful for timely finding whether man-machine resistance exists and timely adjusting when the breathing machine performs mechanical assisted breathing on the myasthenia gravis patient.
The application further provides a ventilation detection device for the myasthenia gravis patient, which comprises a muscle strength monitoring module, a respiration monitoring module and a control module, wherein the muscle strength monitoring module is configured to monitor the muscle activity of the deglutition muscle of the myasthenia gravis patient and the muscle activity of the proximal end of the limb, obtain the muscle strength data of the myasthenia gravis patient according to the monitoring data of the muscle activity of the deglutition muscle and the monitoring data of the muscle activity of the proximal end of the limb, and is configured to detect the respiration rate, the tidal volume of each respiration cycle and the inspiration time of each respiration cycle under the condition of mechanically assisted ventilation of the myasthenia gravis patient, acquire the change trend of the respiration rate, the tidal volume of each respiration cycle and the inspiration time along with time, and the control module is in signal connection with the muscle strength monitoring module and the respiration monitoring module, is configured to acquire the muscle strength data of the myasthenia gravis patient from the muscle strength monitoring module, the tidal volume of each respiration cycle and the change along with time, wherein when the muscle strength data of the myasthenia gravis patient is lower than a preset muscle strength threshold, and the pre-stored change trend of the muscle strength data of the myasthenia patient along with the respiration rate, the tidal volume and the time along with the time change trend of the time does not correspond to the mechanical ventilation trend along with the change trend of the human-machine.
According to a preferred embodiment, the control module is further configured to determine that there is a failure to effectively trigger ventilator delivery if the respiratory rate increases with fluctuations in the tidal volume waveform.
According to a preferred embodiment, the control module is further configured to determine that the ventilator is under-delivered if the respiratory rate increases with an increase in inspiratory time.
The present application further provides a system for monitoring patient muscle activity and maintaining the muscle strength of a patient's respiratory muscles, particularly the diaphragm, using a wearable device, which is capable of analyzing muscle strength data in real time, thereby predicting the risk of acquired muscle weakness and stimulating the patient's diaphragm including vibration, compression, electrical shock and/or infrared based on the detection results.
The application provides a respiratory assistance system for an acquired muscle weakness patient, comprising the ventilation detection device. The system further includes a chest assist assembly including a first actuator configured for external mechanical contact with the chest of the patient and a first control unit in signal connection with the first actuator, and an abdomen assist assembly including a second actuator configured for external mechanical contact with the abdomen of the patient and a second control unit in signal connection with the second actuator, wherein the first control unit and the second control unit are configured to cooperatively control the first actuator and the second actuator to provide mechanical and/or electrical stimulation to the chest and abdomen of the patient, respectively, based on muscle strength data of the patient to assist in breathing of the patient.
According to a preferred embodiment, the system further comprises a first sensor configured to monitor muscle activity of the deglutition muscle, a second sensor configured to monitor muscle activity of the proximal end of the limb, and a data processing unit receiving detection data of the muscle activity from the first sensor and/or the second sensor and calculating muscle strength data.
According to a preferred embodiment, the data processing unit is configured to acquire muscle strength data of a first period of time when the patient performs the first standardized action as reference muscle strength data by means of the first sensor and/or the second sensor, and to analyze muscle strength fluctuations by means of the first sensor and/or the second sensor acquiring muscle strength data of a second period of time of a preset period of time after the patient performs the first standardized action.
According to a preferred embodiment, the first standardized motion comprises, but is not limited to, a swallowing motion and/or an arm lifting motion, the limb proximal to the patient's arm, the second sensor is configured to detect a movement acceleration of the patient's arm and an equivalent mass of the patient's arm, and the data processing unit calculates muscle strength data of the user's arm in the muscle activity based on the movement acceleration of the patient's arm and the equivalent mass of the patient's arm detected by the second sensor.
According to a preferred embodiment, the system further comprises a sensing component configured to measure the motion of the patient's respiratory muscles to collect motion information, and to determine the motion state of the patient's respiratory muscles based on the collected motion information.
According to a preferred embodiment, the first and second control units are configured to control the first and second actuators, respectively, based on the motion state of the patient to provide mechanical and/or electrical stimulation to the chest and abdomen of the patient that matches the motion state.
According to a preferred embodiment, the first actuator comprises a chest strap capable of encircling the chest cavity with adjustable tightness, a first electrode arranged on the chest strap and used for stimulating pectoral major muscles, a second electrode arranged on the chest strap and used for stimulating intercostal muscles, and first inflatable and deflatable mechanical airbags arranged at positions on two sides of the chest strap corresponding to the chest cavity.
According to a preferred embodiment, the second actuator comprises an adjustable tightness abdominal belt capable of encircling the abdominal cavity, a third electrode arranged on the abdominal belt for stimulating rectus abdominis, a fourth electrode arranged on the abdominal belt for stimulating extraabdominal oblique muscles and an inflatable second mechanical balloon arranged at a position on the abdominal belt corresponding to the front side of the abdomen.
According to a preferred embodiment, the first and second actuators are configured to assist the patient in inhaling by the first electrode activating pectoral major muscles to assist in lifting the thorax, the second electrode activating intercostal muscles to assist in expanding the thorax, the third and fourth electrodes activating rectus abdominus muscles and extraabdominal oblique muscles to assist in contracting the abdominal muscles to lower the diaphragm, the first mechanical balloon on either side of the thorax being inflated to assist in expanding the thorax outwardly, the second mechanical balloon on the front side of the abdomen being contracted to assist in lowering the diaphragm, the electrical stimulation and mechanical assistance acting together to increase the volume of the thorax, lower the pressure in the thorax, and to assist in air access to the lungs.
According to a preferred embodiment, the first and second actuators are configured to assist the patient in exhaling by a reduced stimulation intensity of the first and second electrodes allowing the ribcage to fall back naturally, an increased stimulation intensity of the third and fourth electrodes assisting the abdominal muscle to contract, squeeze the abdominal cavity, push the diaphragm up, a first mechanical balloon on both sides of the chest cavity to begin deflating, allowing the ribcage to retract, a second mechanical balloon on the front side of the abdomen to inflate, assisting in squeezing the abdominal cavity, an electrical stimulation and mechanical assistance in cooperation, increasing the intrathoracic pressure, pushing the gas to exhale from the lungs.
The respiratory assistance system for the acquired muscle weakness patients has the technical effects that the respiratory assistance system for the acquired muscle weakness patients has real-time monitoring and early warning, and can timely capture the tiny changes of muscle strength by monitoring the movements of the deglutition muscles and the proximal muscles of limbs through wearable equipment. By comparing the muscle strength data at different time intervals, the system can identify abnormal fluctuation of the muscle strength at an early stage, and provides basis for early intervention. The system establishes personalized baseline data by collecting muscle strength data of the patient when performing standardized actions as a reference. This method takes into account individual differences, improving the accuracy and pertinence of the evaluation, which is particularly apparent in the following aspects. First, by comparing muscle strength data over different time periods, the system is able to dynamically assess the patient's muscle status, reflecting disease progression or improvement. Next to this is respiratory function protection, the system is concerned with diaphragm function in particular, which is one of the most dangerous complications for patients with acquired muscle weakness. By means of the breathing assistance unit, the system is able to actively maintain diaphragm strength, reducing the risk of respiratory failure. By providing multiple modes of intervention, the breathing assistance unit provides multiple stimulation modes, the most appropriate intervention mode may be selected according to the patient's specific situation. By adopting non-invasive monitoring and using the wearable device for monitoring, the interference to the daily life of the patient is reduced, and the compliance of the patient is improved. The continuity and objectivity of the data are ensured, and the defects of strong subjectivity and time point limitation in the traditional evaluation method are overcome because the system provides continuous and objective data. By analyzing the fluctuation mode of muscle strength with predictive analysis, the system has the potential to predict onset or exacerbation of acquired muscle weakness, providing support for clinical decisions. Meanwhile, the possibility of remote monitoring is expanded, and the system provides a technical foundation for remote monitoring, so that a medical team can master the condition of a patient in real time and adjust the treatment scheme in time. Further, rehabilitation guidance is achieved, and data collected by the system can be used for guiding rehabilitation training of a patient to help to make a personalized rehabilitation plan. Overall, the system has the potential to significantly improve the management level of acquired muscle weakness, improve patient prognosis and improve quality of life by real-time, objective and continuous monitoring and timely intervention. It provides an innovative and comprehensive solution for the diagnosis, treatment and rehabilitation of acquired muscle weakness.
According to a preferred embodiment, the first sensor is a flexible sensor that is fitted to a first region of the skin surface of the patient's deglutition muscle, which derives a first signal reflecting the state of change of strain of the first region and a second signal characterizing the state of change of curvature of the first region from the deformation of the first region.
According to a preferred embodiment, the first sensor comprises a strain sensing unit and an optical sensing unit. The strain sensing unit obtains a first signal according to the deformation of the first area. The optical sensing unit obtains a second signal according to the deformation of the first area.
According to a preferred embodiment, the data processing unit obtains muscle strength data of the patient's deglutition muscle from the first signal and the second signal acquired by the first sensor.
According to a preferred embodiment, the data processing unit further comprises a data receiving module for receiving muscle activity detection data from the first sensor and the second sensor, a data analyzing module for calculating muscle strength data from the received muscle activity detection data, analyzing the volatility of the muscle strength by means of a predetermined algorithm model and outputting a risk assessment result of acquired muscle weakness, and a user interface for displaying the analyzed risk assessment result and advice.
According to a preferred embodiment, the preset time period is adjusted according to muscle strength data, historical muscle strength data and/or physician order associations of the first period of the patient.
According to a preferred embodiment, the data analysis module configures a machine learning algorithm to learn and adapt to the muscle strength fluctuation characteristics of the individual patient and update the algorithm model over time. The data analysis module configures an anomaly detection algorithm for detecting and marking anomaly points in the muscle strength data. The data analysis module is further configured with a trend analysis algorithm for identifying long-term trends and short-term fluctuations from the continuous period of muscle strength data to aid in assessing progression of muscle weakness.
Detailed Description
The following detailed description refers to the accompanying drawings.
Myasthenia gravis is a chronic autoimmune disease that affects the signaling between nerves and muscles, resulting in abnormal fatigue and strength attenuation of skeletal muscles. Such diseases typically affect the extraocular muscles, facial muscles, throat muscles, and the muscles of the extremities. In some cases, myasthenia gravis affects the muscles that control breathing, especially as the disease progresses to more severe stages. This condition is known as "myasthenia crisis" or "respiratory muscle weakness" and is an acute complication of myasthenia gravis. When respiratory muscles are affected, the patient may experience dyspnea, and in severe cases even failure to breathe spontaneously, requiring urgent medical intervention, such as the use of a ventilator to assist in breathing.
There are some special requirements and precautions with respect to the average patient for the myasthenia gravis to use the ventilator to assist in breathing. Since myasthenia gravis is a disease of the neuromuscular junction, patients may exhibit varying degrees of neuromuscular weakness, which can affect their support needs for the ventilator. Accordingly, there is a need to frequently evaluate the neuromuscular status of a patient, including muscle strength, depth of respiration, frequency, etc., in order to adjust ventilator settings in a timely manner. The use of quantitative neuromuscular transfer tests (such as single fiber electromyography or repeated neurostimulation tests) can help to better understand the status of neuromuscular function and thus guide therapeutic decisions.
The inventor finds that, in the mechanical assisted respiration process, the muscle strength of different patients, especially the difference of respiratory related muscle strength is larger due to the difference of the muscle weakness conditions of different patients, and the operation parameters of the breathing machine are often set according to the experience of doctors, so that the situation that the breathing machine parameter setting is inconsistent with the requirements of the patients to cause man-machine resistance frequently occurs clinically, for example, the breathing machine air supply, the insufficient gas delivery of the breathing machine, the early expiration triggering and the like cannot be effectively triggered. This is more common in patients with myasthenia gravis where the muscular strength is unstable and the difference in muscular strength between individuals is large.
The application provides a ventilation detection method for a patient suffering from myasthenia gravis, which is applied to a respiratory assistance system, in particular to a mechanical respiratory assistance system, as shown in figure 1, and comprises the steps of monitoring the muscle activity of the deglutition muscle and the muscle activity of the proximal end of a limb of the patient suffering from myasthenia gravis, obtaining muscle strength data of the patient suffering from myasthenia gravis according to the monitoring data of the muscle activity of the deglutition muscle and the monitoring data of the muscle activity of the proximal end of the limb, detecting the respiratory rate, the tidal volume of each respiratory cycle and the inspiration time of each respiratory cycle under the condition of mechanical assistance ventilation of the patient suffering from myasthenia gravis, obtaining the change trend of the respiratory rate, the tidal volume of each respiratory cycle and the inspiration time with time, and determining that man-machine resistance exists in the mechanical assistance ventilation process of the patient suffering from myasthenia gravis when the muscle strength data of the patient suffering from myasthenia gravis is lower than a preset muscle strength threshold and the change trend of the corresponding parameters prestored with time.
Tidal Volume (VT) refers to the amount of air that enters and exits the lungs per breath, typically expressed in milliliters (mL). The conventional tidal volume of adults is set to 5-8 mL/kg of ideal body weight. Respiratory Rate (RR) is a measure of the number of breaths per minute used to control minute ventilation (MV, minute Ventilation), the total amount of gas that enters or exits the lungs in a minute. The common respiratory rate range for adults is 10-20 times per minute. Inspiration time (Ti, inspiratory Time) the length of time that the inspiration phase lasts in each breath. The choice of inspiration time will affect the ratio of I to E and the overall breathing pattern.
The application provides a ventilation detection method for a patient with myasthenia gravis, which aims to evaluate whether man-machine resistance occurs or not in real time by monitoring the muscle activity and the change trend of ventilation parameters of the patient and timely give an alarm when the man-machine resistance occurs. The deglutition muscle is one of the commonly affected sites of patients with myasthenia gravis, and monitoring the muscle activity of the deglutition muscle can reflect the neuromuscular functional status of the patient, especially in cases where the respiratory muscle is affected. Preferably, the electrical activity of the deglutition muscles (e.g., the cricopharynx, supraglottic muscle groups, etc.) is monitored using a surface electromyography (sEMG) sensor attached to the patient's throat or neck. These sensors can capture the electrical signals generated when the muscle contracts, thereby quantifying the muscle strength. For example, the raw electromyographic signals may be converted to muscle strength data by signal processing algorithms (e.g., filtering, denoising, feature extraction, etc.). Common indicators include maximum electromyographic signal intensity (RMS), average frequency (MNF), etc.
Muscles at the proximal end of the limb (e.g., shoulder strap muscles, hip muscles, etc.) typically have a similar innervating pathway to respiratory muscles, so that their changes in muscle strength may indirectly reflect the state of respiratory muscles. Preferably, the electrical activity of the proximal muscle of the limb is monitored using a surface electromyogram (sEMG) sensor, attached to the patient's shoulder blade area, quadriceps femoris, etc. Similar to the deglutition muscles, the electromyographic signals are converted into muscle strength data by a signal processing algorithm.
Preferably, according to clinical experience, the muscle strength of a patient suffering from myasthenia gravis will generally decrease gradually as the condition progresses. To set reasonable thresholds, baseline measurements may be taken at the time of patient admission, recording their normal muscle strength levels. The threshold is then dynamically adjusted according to patient condition changes. For example, for healthy adults, the maximum electromyographic signal intensity (RMS) of the deglutition muscles is typically between 50-100 μv, and the RMS value of the proximal muscles of the limb is between 100-300 μv. For example, when the patient's muscle strength drops below 50% of baseline, it may be indicated that neuromuscular function is significantly impaired, with a higher risk of respiratory failure. For example, if the patient's deglutition muscle RMS value falls below 25 μv, or the limb proximal muscle RMS value falls below 150 μv, an alarm is triggered, suggesting a need for further assessment of respiratory support.
The inventors have found in the study that myasthenia gravis patients of different muscle strength levels, determine whether they develop man-machine resistance, and that man-machine resistance may have differences in the consequences. When the muscle strength is relatively high, the patient can also maintain normal physiological activity through spontaneous breathing in combination with the assistance of the ventilator, and weak man-machine resistance generally does not significantly affect the normal breathing of the patient. When muscle strength is severely reduced, human resistance may cause serious damage to the patient. According to one embodiment, the muscle strength threshold is 30%,50% or 70% of the baseline value. The medical institution or the detecting device manufacturing enterprises can set corresponding pre-stored respiratory frequency, the variation trend of the tidal volume and the inspiration time of each respiratory period according to different muscle strength ranges, and can set personalized pre-stored respiratory frequency, the variation trend of the tidal volume and the inspiration time of each respiratory period according to the illness state of a patient so as to judge whether man-machine resistance occurs or not more accurately and evaluate the possible physiological influence on the patient.
Preferably, obtaining muscle strength data of a patient suffering from myasthenia gravis based on the monitoring data of the muscle activity of the deglutition muscle and the monitoring data of the muscle activity of the proximal end of the limb includes collecting muscle strength data of a first period of time when the patient performs a first standardized action as reference muscle strength data, collecting muscle strength data of a second period of time for a preset period of time after the patient performs the first standardized action, and analyzing muscle strength fluctuations based on the muscle strength data of the second period of time and the reference muscle strength data.
Preferably, the first standardized action is a swallowing and/or an arm lifting action.
Preferably, after determining that there is a human-machine resistance during the mechanical assist ventilation of the myasthenia gravis patient, the method further includes determining a type of human-machine resistance based on the respiratory rate, the tidal volume of each respiratory cycle, and the trend of the inspiratory time over time.
Preferably, as shown in FIG. 3, if the respiratory rate increases with fluctuations in tidal volume waveforms, it is determined that there is a failure to effectively trigger ventilator delivery.
Preferably, as shown in FIG. 4, if the respiratory rate increases with an increase in inspiratory time, which is characterized by a change in pressure of the ventilator airway, the ventilator is judged to be under-delivered.
Preferably, as shown in fig. 5, if the tidal volume is below a preset tidal volume threshold, the inspiration time is shorter than a preset inspiration time threshold and the breathing frequency increases, then it is determined that an early expiration trigger is present for the ventilator, wherein the inspiration time is characterizable by a pressure change of the ventilator airway.
For human-machine resistance detection when a breathing machine is used for patients with myasthenia gravis, the selection of proper ventilation parameters is important for accurately judging human-machine resistance by taking the special pathological characteristics of neuromuscular junctions of the patients into consideration. Based on clinical features of myasthenia gravis patients and the need for mechanical ventilation, the present application provides methods for monitoring and assessing human resistance and types of human resistance using muscle strength, tidal Volume (VT), respiratory Rate (RR), and inspiratory time (Ti).
The respiratory muscle strength of myasthenia gravis patients may be weak and thus their tidal volume may be small or unstable. By monitoring changes in tidal volume, it can be identified whether the patient is attempting to increase inspiratory effort but is not effectively triggering ventilator delivery. Patients with myasthenia gravis may exhibit increased respiratory rate during exacerbation of the disease. If the respiratory rate is significantly higher than the set point, or frequent spontaneous breathing attempts occur, this may indicate that the patient is striving to overcome the limitations of the ventilator settings. The inspiration time reflects the length of time that the inspiration phase lasts in each breath. A patient with myasthenia gravis may have prolonged inspiration to obtain sufficient gas due to the weakness of the respiratory muscle. If an abnormal extension of the inspiration time is observed, or if there is no match with the set inspiration-to-expiration ratio, this indicates that human-machine resistance may exist.
Preferably, the data of the three ventilation parameters are collected in real time during the mechanical ventilation of the user and a time-varying waveform is generated. Analysis of the trend of the acquired tidal volume, respiratory rate, and inspiratory time waveforms is important to note whether these parameters exhibit regular abnormal fluctuations or deviate from the expected pattern.
Preferably, the judging method includes:
baseline establishment first, a normal range or baseline waveform for each ventilation parameter is established based on the patient's basal state and the current treatment regimen. This may be accomplished by data collection during the initial stationary phase.
And comparing the ventilation parameter waveform monitored in real time with the baseline waveform. If a trend in the waveform of tidal volume, respiratory rate, or inspiratory time that is significantly different from baseline is found, such as too small or too large tidal volume with a significant increase in respiratory rate, frequent exceeding of a set point respiratory rate with shortness and irregular inspiratory attempts, abnormally prolonged inspiratory time, resulting in an unbalanced inspiratory ratio, all of which may be indicative of the occurrence of human resistance.
Confirming man-machine resistance, namely judging that man-machine resistance exists more confidently when a plurality of parameters simultaneously display abnormal trend.
Preferably, the determination of the type of human resistance including inspiration null triggers and expiration early triggers is made by monitoring and analyzing tidal volume, respiratory rate and inspiration time by the following method.
Inhalation-ineffective triggering (INEFFECTIVE TRIGGERING) refers to a patient attempting to initiate a breath, but failing to trigger ventilator delivery, resulting in a respiratory effort that is not responded to. Preferably, the inhalation inefficiency trigger is confirmed by tidal Volume (VT) waveform analysis and Respiratory Rate (RR) waveform analysis. Tidal volume waveforms are monitored to see if there is a minute tidal volume fluctuation (typically less than 20% of the set tidal volume). These minor fluctuations may represent spontaneous respiratory effort by the patient, but may not be effective in triggering the delivery of air due to insufficient trigger sensitivity or improper ventilator setting. If the respiratory rate waveform shows frequent short breath attempts and the tidal volume does not increase significantly after each attempt, it is suggested that there may be an inhalation inefficiency trigger. It is further preferred that the airway pressure waveform be additionally examined to find out whether there is a negative pressure spike (i.e., negative pressure generated when the patient inhales) but then there is no concomitant positive pressure delivery. This indicates that the patient is attempting to inhale but fails to trigger the ventilator.
Premature expiration triggering (Premature Cycling Off) refers to the premature switching of the ventilator to the expiratory phase when the inspiratory phase has not yet ended, resulting in the patient failing to achieve sufficient tidal volume, affecting gas exchange. And monitoring the tidal volume waveform, and observing whether the tidal volume is significantly lower than a set value. If tidal volumes are often lower than expected, an early exhalation trigger may be prompted. It is further preferred to examine the airway flow velocity waveform, particularly at the end of the inspiratory phase, to see if there is a premature drop in flow velocity to baseline levels. This indicates that the ventilator switches prematurely to the expiratory phase, resulting in insufficient inspiration time. Airway pressure waveforms were observed, particularly at the end of the inspiratory phase, for premature pressure drops. If the pressure of the inspiratory phase is not maintained for a sufficient time, an early expiration trigger is indicated.
Preferably, the patient's respiratory rate is monitored in real time by a flow sensor or pressure sensor built into the ventilator. The respiratory rate is typically expressed in terms of breaths per minute (times/min). Under normal conditions, the respiratory rate of MG patients should be kept between 10-20 times/min. If the respiratory rate suddenly increases above 25 times/minute and continues for more than 30 seconds, it may be possible to prompt the patient that he is struggling to overcome the limitations set by the ventilator. The tidal volume of each respiratory cycle is monitored in real time by a flow sensor built in the ventilator. Under normal conditions, the tidal volume of MG patients should be kept between 400-600 mL. If the tidal volume drops significantly below 300 mL, with an accompanying increase in respiratory rate, the patient may be prompted to fail to effectively trigger ventilator delivery, with the risk of ineffective triggering of inspiration.
By applying the ventilation detection method, whether the condition of man-machine resistance exists and the type of man-machine resistance can be judged according to the condition characteristics of the myasthenia gravis patient and the muscle strength of the myasthenia gravis patient and the variation trend of tidal volume, respiratory frequency and inspiration time in the mechanical auxiliary expiration process of the myasthenia gravis patient. The method is helpful for timely finding whether man-machine resistance exists and timely adjusting when the breathing machine performs mechanical assisted breathing on the myasthenia gravis patient.
The application further provides a ventilation detection device for the myasthenia gravis patient, as shown in fig. 2, which comprises a muscle strength monitoring module, a respiration monitoring module and a control module, wherein the muscle strength monitoring module is configured to monitor the muscle activity of the deglutition muscle and the muscle activity of the proximal end of the limb of the myasthenia gravis patient, obtain the muscle strength data of the myasthenia gravis patient according to the monitoring data of the muscle activity of the deglutition muscle and the monitoring data of the muscle activity of the proximal end of the limb, and is configured to detect the respiratory rate, the tidal volume of each respiratory cycle and the inspiration time of each respiratory cycle under the condition of mechanically assisted ventilation of the myasthenia gravis patient, acquire the change trend of the respiratory rate, the tidal volume of each respiratory cycle and the inspiration time along with time, and the control module is in signal connection with the muscle strength monitoring module and the respiration monitoring module, is configured to acquire the muscle strength data of the myasthenia gravis patient from the muscle strength monitoring module, acquire the change trend along with time along with the respiratory rate, the tidal volume of each respiratory cycle and the inspiration time along with the respiratory rate of the corresponding mechanical trend of the human-machine-assisted ventilation when the muscle strength data of the myasthenia patient is lower than a preset muscle threshold and the respiratory rate, the change along with the time of the corresponding change trend of the respiratory cycle and the corresponding to the change trend of the respiratory cycle and the mechanical trend is not consistent with the change along with the time. The inspiration time of each breathing cycle is monitored in real time by a flow sensor built in the breathing machine. Under normal conditions, the inspiration time of the MG patient should be maintained between 0.8-1.2 seconds. If the inspiration time is significantly prolonged above 1.5 seconds, or shortened below 0.6 seconds, it may be suggestive of a risk of delayed or advanced expiration triggering.
Preferably, when the patient's muscle strength data (e.g., RMS values of the deglutition muscles and proximal muscles of the limb) is below a preset muscle strength threshold, it is suggested that the patient's neuromuscular function is significantly impaired, possibly with a risk of respiratory muscle weakness. Meanwhile, when the variation trend of the respiratory frequency, the tidal volume and the inspiration time along with time is inconsistent with the pre-stored corresponding parameter variation trend, the possibility of man-machine resistance is further confirmed. Specifically, if the patient's muscle strength data is below a threshold (e.g., deglutition muscle RMS < 25 μv, limb proximal muscle RMS < 150 μv), and the respiratory rate suddenly increases above 25 times/minute, the tidal volume drops below 300 mL, the inspiration time extends above 1.5 seconds, the system will determine that human-machine resistance is present, and an alarm is raised. If the patient's muscle strength data is below the threshold, but the ventilation parameter trend is consistent with the pre-stored trend (e.g., respiratory rate stabilized at 12-16 times/min, tidal volume stabilized at 450-550 mL, inspiratory time stabilized at 1.0 seconds), then no apparent man-machine resistance is considered to exist, and monitoring is continued.
Preferably, the healthcare worker needs to pay close attention to the patient's condition when the patient's muscle strength data is near a threshold (e.g., deglutition RMS < 30 μv, limb proximal muscle RMS < 180 μv), but no significant abnormalities in ventilation parameters have occurred. When the patient's muscle strength data falls below a threshold and abnormal changes in ventilation parameters occur, the healthcare worker should immediately check the ventilator settings and take corresponding action (e.g., adjust trigger sensitivity, increase PEEP, etc.). When the patient's muscle strength data is well below a threshold (e.g., deglutition muscle RMS < 20 μv, limb proximal muscle RMS < 120 μv) and the ventilation parameters are severely abnormal, the healthcare worker should prepare for emergency intubation or adjust the ventilation mode. According to the detection result, the medical staff can adjust the setting of the breathing machine, such as triggering sensitivity, inspiration time, ventilation mode and the like, so as to improve man-machine synchronism, and comprehensively improve the ventilation state of the patient by combining other clinical intervention measures, such as drug treatment, psychological support and the like, if necessary.
By the technical scheme, the man-machine resistance phenomenon possibly occurring in the mechanical ventilation process of the myasthenia gravis patient can be accurately identified and treated. This approach not only relies on traditional ventilation parameters (e.g., respiratory rate, tidal volume, inspiration time), but also incorporates patient muscle strength data, providing a more comprehensive assessment. In combination with the special pathological features of the MG patient and the mechanical ventilation requirement, the application provides a safe and effective ventilation management method, and ensures that the patient with myasthenia gravis obtains optimal respiratory support.
According to a preferred embodiment, the control module is further configured to determine that there is a failure to effectively trigger ventilator delivery if the respiratory rate increases with fluctuations in the tidal volume waveform.
According to a preferred embodiment, the control module is further configured to determine that the ventilator is under-delivered if the respiratory rate increases with an increase in inspiratory time.
The present application provides a respiratory assistance system for an acquired muscle weakness patient. Preferably, the respiratory assistance system comprises the aforementioned ventilation detection means. As shown in fig. 8, the system further comprises a chest assist assembly 100 comprising a first actuator 110 configured for external mechanical contact with the chest of the patient and a first control unit 120 in signal connection with the first actuator 110, an abdomen assist assembly 200 comprising a second actuator 210 configured for external mechanical contact with the abdomen of the patient and a second control unit 220 in signal connection with the second actuator 210, wherein the first control unit 120 and the second control unit 220 are configured to cooperatively control the first actuator 110 and the second actuator 210, respectively, to provide mechanical and/or electrical stimulation to the chest and abdomen of the patient to assist in breathing of the patient based on muscle strength data of the patient.
Based on the system, the following variables are first defined:
chest muscle strength as a function of time;
Belly muscle strength as a function of time;
The intensity of the stimulus applied by the chest actuator as a function of time;
the intensity of the stimulus applied by the abdominal actuator as a function of time;
The respiratory function index of the patient is a function of time.
The system is described by the following mathematical model:
The control equation of the chest booster component is:
;
the control equation of the abdomen assisting component is as follows:
;
the respiratory function improvement equation is:
。
Wherein, the、AndIs a nonlinear function determined by clinical data and machine learning algorithms.
According to one embodiment, the following linear model process is employed:
;
;
。
Wherein, the、Is the control of the gain factor and,、Maximum muscle strength of the corresponding chest and abdomen muscles of a healthy person,、、、Is a weight coefficient.
Specifically, clinical data samples for 10 patients are provided below in table 1.
TABLE 1
Table 1 lists clinical data for 10 patients and records data for patient number, age, sex, muscle weakness status, chest muscle strength, abdominal muscle strength, chest stimulation strength, abdominal stimulation strength, tidal volume, respiratory rate, blood oxygen saturation. The chest muscle strength, the abdominal muscle strength, the chest stimulation strength and the abdominal stimulation strength are normalized to a normalized fraction of 0-100.Can be determined by establishing a mapping of chest muscle strength to chest stimulation intensity.Can be determined by establishing a mapping of abdominal muscle strength to abdominal stimulation intensity.Can be determined by establishing a mapping of (chest muscle strength, abdominal muscle strength, chest stimulation intensity, abdomen stimulation intensity) to (tidal volume, respiratory rate, blood oxygen saturation), respectively.
Preferably, as shown in FIG. 8, the first actuator 110 includes a chest strap 111 capable of encircling the chest cavity with adjustable tightness, a first electrode 112 disposed on the chest strap 111 for stimulating pectoral major muscles, a second electrode 113 disposed on the chest strap 111 for stimulating intercostal muscles, and inflatable first mechanical bladders 114 disposed on opposite sides of the chest strap 111 corresponding to the chest cavity. Specifically, the chest strap 111 is made of an elastic fabric blended by nylon and spandex, and is arranged as an elastic hasp system, and the chest circumference is 70-130 cm. The first electrode 112 is a surface electrode made of a flexible conductive material such as conductive silica gel, is configured as an ellipse, has a major axis of 8cm and a minor axis of 5cm, is disposed at a position corresponding to pectoral major muscle inside the pectoral girdle 111, and is connected to the first control unit 120 through a flexible printed circuit. The second electrode 113 is made of flexible conductive material into a strip-shaped electrode, and is about 15cm long and about 2cm wide, and is disposed on the inner side of the chest strap 111, arranged along the rib direction, and connected to the first control unit 120 through a flexible printed circuit. The first mechanical balloon 114 is made of medical grade silica gel into a round shape with the diameter of about 10cm, the thickness of the first mechanical balloon can be expanded from 0.5cm to 3cm, the first mechanical balloon is embedded into the chest strap structure at two sides of the chest strap 111, and the first mechanical balloon is connected with a miniature electric pump through a hose for inflation and deflation. The first mechanical air bag can be integrated with a pressure sensor for monitoring the pressure in real time.
Preferably, as shown in FIG. 8, the second actuator 210 includes an adjustable tightness abdomen band 211 capable of encircling the abdominal cavity, a third electrode 212 for stimulating rectus abdominis provided on the abdomen band 211, a fourth electrode 213 for stimulating extraabdominal oblique muscles provided on the abdomen band 211, and an inflatable second mechanical balloon 214 provided at a corresponding abdomen front side position of the abdomen band 211. The abdomen belt 211 is made of breathable elastic fabric and has a width of about 20cm and an abdomen circumference of about 60-120 cm. The third electrode 212 is a rectangular surface electrode having a length of 10cm and a width of 5cm, and is disposed at a position corresponding to rectus abdominis inside the abdomen binding band 211, and is connected to the second control unit 220 through a flexible printed circuit. The fourth electrode 213 is provided as a fan-shaped surface electrode having an arc length of about 15cm and a widest point of about 5cm, and is provided at a position corresponding to the extraabdominal oblique muscle inside the abdominal belt 211, and is connected to the second control unit 220 through a flexible printed circuit. The second mechanical balloon 214 is made of medical grade silica gel and has an oval shape with a long axis of 20cm and a short axis of 15cm, and can be inflated from 1cm to 5cm in thickness, and is embedded in the abdomen belt structure at the corresponding position on the front side of the abdomen. The air charging and discharging are carried out by connecting a hose with a miniature electric pump. The first mechanical bladder preferably uses the same electric pump as the second mechanical bladder. The second mechanical air bag can be integrated with a pressure sensor for monitoring the pressure in real time.
The first control unit 120 and the second control unit 220 may adopt a low-power microcontroller (such as ARM Cortex-M4), and have an electric stimulation module, a programmable current source, an output range of 0-100 mA, a frequency adjustable (1-100 Hz), an air bag control module, a PWM controlled electric pump driving circuit, a communication interface, a Bluetooth low-power (BLE) module, for communicating with a main controller, and a sensor interface, for connecting an Electromyography (EMG) sensor and a pressure sensor.
Preferably, as shown in fig. 9 and 10, the first actuator 110 and the second actuator 210 are configured to assist the patient in breathing in the following manner:
In the inspiration phase, the first electrode 112 activates pectoral major muscles, helps to lift the thorax, the second electrode 113 activates intercostal muscles, helps to expand the thorax, and the third electrode 212 and the fourth electrode 213 activate rectus abdominis and oblique abdominis, respectively, slightly, and help to contract abdominal muscles, so that the diaphragm is lowered. The first mechanical balloons 114 on both sides of the thorax are inflated to assist in the outward expansion of the thorax and the mechanical balloons on the front side of the abdomen are contracted to assist in the descent of the diaphragm. The combined action of electrical stimulation and mechanical assistance increases chest volume, reduces intrathoracic pressure, and promotes air access to the lungs, as shown in figure 9.
During the expiration phase, the stimulation intensity of the first electrode 112 and the second electrode 113 is reduced, allowing the thoracic cage to fall back naturally. The stimulation intensity of the third electrode 212 and the fourth electrode 213 increases, assisting the contraction of the abdominal muscle, squeezing the abdominal cavity, pushing the diaphragm up. The first mechanical balloon 114 on either side of the chest begins to deflate, allowing the chest to retract. The second mechanical balloon 214 on the anterior side of the abdomen inflates, assisting in squeezing the abdominal cavity. The electrical stimulation and mechanical assistance cooperate to increase intrathoracic pressure and push the gases out of the lungs, as shown in fig. 10.
By means of the precise coordination mode, the device can simulate the breathing muscle movement mode of a healthy person and provide comprehensive breathing assistance for a patient with muscle weakness. This approach not only improves the respiratory function of the patient, but may also help to maintain and train these muscles, potentially positively impacting the patient's long-term recovery.
According to one embodiment, the electrode stimulation parameters for the inspiration phase and expiration phase are shown in tables 2 and 3 below:
TABLE 2 inspiration phase (duration 1 to 2 seconds)
TABLE 3 expiration phase (duration 2-3 seconds)
Preferably, the maximum capacity of the first mechanical air bag is 500ml per side, the inflation rate in the inspiration stage is 250-350 ml/s, and the deflation rate in the expiration stage is 150-250 ml/s. The maximum capacity of the second mechanical air bag is 1000ml, the inflation rate in the inspiration stage is 300-400 ml/s, and the deflation rate in the expiration stage is 200-300 ml/s.
Preferably, the electrical stimulation intensity of the electrodes is adjustable in accordance with the detected blood oxygen saturation of the patient. In accordance with one embodiment of the present invention,
,
Wherein, theIs the electrical stimulation base intensity, for example as listed in tables 2 and 3,The electric stimulation intensity of the electrode is adjusted according to the blood oxygen saturation of the patient,Is the current blood oxygen saturation percentage monitored in real time; is an adjustment coefficient, typically 0.02; The saturation value of the target blood sample is a constant, and the set value is usually 95-98.
According to a specific embodiment, the stimulation intensities of the first electrode, the second electrode, the third electrode and the fourth electrode are set according to the detected muscle strength, blood oxygen saturation, respiratory rate and tidal volume of the patient. Preferably, the method comprises the steps of,
;
Wherein, theThe intensity of the stimulus is indicated and,Represents a standardized muscle strength measurement value ranging from 0 to 100,Represents the blood oxygen saturation, the range is 0 to 100,The respiratory rate is represented, the range is 12 to 20 times per minute,Tidal volume is indicated, typically in the range of 4 to 8ml/kg body weight.、、、Is a parameter set according to patient-personalized clinical data measurements. According to a specific embodiment, the statistical calculation is performed on the clinical data measured values of the test patient, for the first electrode,、、、Respectively set to 0.3, 0.2 and 0.2, and for the second electrode,、、、Respectively set to 0.3, 0.25, 0.2, for the third electrode,、、、Respectively set to 0.2, 0.3, 0.22 and 0.28, and for the fourth electrode,、、、Set to 0.24, 0.28, 0.22, 0.26, respectively.
Preferably, the capacities of the first mechanical balloon 114 and the second mechanical balloon 214 are adjustable according to the patient's vital capacity VC. First mechanical balloon maximum capacity = 0.15 x VC, second mechanical balloon maximum capacity = 0.3 x VC, where VC is patient's lung capacity (milliliters).
Preferably, the upper limit of the electrical stimulation intensity of the electrode is 50mA. The maximum secondary respiratory rate was set to 20 beats/min.An alarm is triggered below 90% or if no valid breath is detected for 30 seconds.
According to a preferred embodiment, the respiratory assistance system for an acquired muscle weakness patient further comprises a first monitoring unit for monitoring the thoracic volume of the patient, a second monitoring unit for monitoring the abdominal volume of the patient, a third monitoring unit for acquiring the thoracic pressure gradient Δpt of the patient and a fourth monitoring unit for acquiring the abdominal pressure gradient Δpa of the patient. The first control unit 120 and the second control unit 220 are configured to adjust the real-time electrical stimulation intensities of the first electrode, the second electrode, the third electrode, and the fourth electrode based on the change in the patient's thoracic volume acquired by the first monitoring unit, the change in the patient's abdominal volume acquired by the second monitoring unit, the change in the patient's thoracic pressure acquired by the third monitoring unit, and the change in the patient's abdominal pressure acquired by the fourth monitoring unit.
Specifically, the first monitoring unit is a chest circumference measurement resistance band integrated within the chest band 111, which is capable of measuring the patient's chest circumference change in real time by resistance change, thereby calculating the patient's chest volume change. The second monitoring unit is an abdominal circumference measuring resistance band integrated in the abdominal belt 211, which measures the abdominal circumference change of the patient in real time through the resistance change, thereby calculating the abdominal cavity volume change of the patient. The third monitoring unit can be an intrathoracic pressure monitor having a plurality of pressure taps for monitoring different locations of the chest. The fourth monitoring unit can be an intra-abdominal pressure monitor provided with a plurality of pressure measurement points to monitor intra-abdominal pressures at different locations.
Preferably, the first monitoring unit, the second monitoring unit, the third monitoring unit, and the fourth monitoring unit are respectively in signal connection with the first control unit 120 and the second control unit 220. Specifically, the first monitoring unit, the second monitoring unit, the third monitoring unit, and the fourth monitoring unit are respectively connected with the first control unit 120 and the second control unit 220 through wireless communication modules. The wireless communication module supports at least one communication mode of GPRS, 3G, 4G, 5G, wi-Fi, ZIGBEE and LoRa.
Specifically, the first control unit 120 and the second control unit 220 are configured to calculate a rate of change Δvt of the thoracic volume of the patient based on the thoracic volume of the patient acquired by the first monitoring unit, calculate a rate of change Δva of the abdominal volume of the patient based on the abdominal volume of the patient acquired by the second monitoring unit, and calculate a volume phase difference θv from the thoracic volume of the patient over time t and the abdominal volume of the patient over time t.
According to a specific embodiment, the first control unit 120 and the second control unit 220 are configured to calculate the patient's thoracic and abdominal respiratory kinetic synchronization index based on the patient's rate of change of thoracic volume Δvt, rate of change of abdominal volume Δva, volume phase difference θv, thoracic pressure gradient Δpt, abdominal pressure gradient Δpa. Wherein, the respiratory dynamics synchronous index of chest and abdomenThe definition is as follows:
,
Wherein, theAs a function of the volume coordination factor,As a pressure balance factor, the pressure balance factor,As a result of the volume-pressure coupling index,As the phase compensation coefficient, a phase compensation coefficient,In order to achieve a dynamic efficiency ratio,Is the normalized kinetic efficiency ratio.
Volume coordination coefficientThe calculation formula of (2) is as follows:
。
Pressure balance factorThe calculation formula of (2) is as follows:
。
Volume-pressure coupling indexThe calculation formula of (2) is as follows:
。
Phase compensation coefficientThe calculation formula of (2) is as follows:
。
kinetic efficiency ratioThe calculation formula of (2) is as follows:
。
Normalized kinetic efficiency ratioThe method comprises the following steps:
。
Normalization may be further performed to ensure that the value is between 0 and 100.
The normalization processing formula is:
。
The volume coordination coefficient can evaluate the matching degree of the chest and abdomen volume change, introduces cosine function correction of the phase difference, and has a numerical range of 0-1. The pressure balance factor reflects the balance of the chest-abdomen pressure gradient, and an exponential function is adopted to ensure sensitivity, and the smaller the pressure difference is, the closer the coefficient is to 1. The volume-pressure coupling index is used to evaluate the degree of matching of the volume change to the pressure change. The phase compensation coefficient deals exclusively with the influence of the phase difference, which reaches a maximum value of 1 when 0. The kinetic efficiency ratio can evaluate the efficiency of volume change versus pressure change, normalized with Sigmoid function to avoid the effects of outliers. By calculation ofThe respiratory coordination can be quantitatively evaluated, and the parameter is used for conveniently monitoring and comparing the respiratory stability and recovery state of a patient for a long time, and can guide respiratory training and treatment.
According to a specific embodiment, a respiratory assistance system for an acquired muscle weakness patient is configured to perform an adaptive muscle weakness based therapyIs provided. Firstly, setting electromyographic signal acquisition basic parameters, wherein the sampling frequency is 2000Hz, the resolution is 16 bits, the signal to noise ratio is greater than 60 dB, and the common mode rejection ratio is greater than 100 dB. The first electrode is arranged between the 3 th rib and the 5 th rib, the second electrode is arranged between the 3 th rib and the 7 th rib, the third electrode is arranged 5cm above and below the umbilicus, and the fourth electrode is arranged below the 8 th rib and the 9 th rib. The basic parameters of electrode stimulation are pulse width 200-400 mu s, frequency 20-50 Hz, intensity 10-40 mA, rising time 0.5-1 s, duration 1-2 s and stimulation interval 2-4 s.
Specifically, the first electrode has an initial intensity of 20 mA, a frequency of 35 Hz, and a pulse width of 300 μs. The second electrode had an initial intensity of 15 mA, a frequency of 40 Hz, and a pulse width of 250 μs. The third electrode has an initial intensity of 25 mA, a frequency of 30 Hz, and a pulse width of 350 μs. The fourth electrode has an initial intensity of 22 mA, a frequency of 32 Hz, and a pulse width of 325 μs.
Further, training based on, for example, training sets as listed in Table 4 below may be employedAnd adjusting an intelligent regulation mechanism of electrode stimulation parameters. The adjustment mechanism can be personalized and adjusted in clinical application according to the specific physiological state of the patient. The following training set is taken as a basisAn exemplary example of feedback adjustment is made. According to one specific embodiment, the method is such thatThe stimulation intensity and frequency of each of the first electrode, the second electrode, the third electrode, and the fourth electrode are adjusted so as to be equal to or greater than 80, preferably equal to or greater than 90.
TABLE 4 Table 4
Table 4 illustrates that the training base can be usedA training set of the technical scheme for adjusting electrode stimulation parameters comprisesValue sum and correspondence of (a)And the data of the first electrode regulating value, the second electrode regulating value, the third electrode regulating value and the fourth electrode regulating value are taken as values. By training and executing the above feedback adjustment mechanism using the training set, the breathing coordination of the patient can be objectively reflectedThe parameters are used for evaluating the respiratory state of a patient for a long time, and the electrode stimulation parameters of a respiratory assistance system are adjusted to realize better assisted respiratory effect.
Preferably, the system further comprises a first sensor 131 configured to monitor the muscle activity of the deglutition muscle, a second sensor 133 configured to monitor the muscle activity of the proximal end of the limb, and a data processing unit 140 receiving the detection data of the muscle activity from the first sensor 131 and/or the second sensor 133 and calculating the muscle strength data.
Preferably, the data processing unit 140 is configured to collect muscle strength data of a first period of time when the patient performs the first standardized action as reference muscle strength data by the first sensor 131 and/or the second sensor 133, and analyze muscle strength fluctuations by collecting muscle strength data of a second period of time of a preset period of time after the patient performs the first standardized action by the first sensor 131 and/or the second sensor 133.
Preferably, the first standardized motion includes, but is not limited to, a swallowing motion and/or an arm lifting motion, the limb proximal to the patient's arm, the second sensor 133 is configured to detect a movement acceleration of the patient's arm and an equivalent mass of the patient's arm, and the data processing unit 140 calculates muscle strength data of the user's arm in muscle activity based on the movement acceleration of the patient's arm and the equivalent mass of the patient's arm detected by the second sensor 133.
Preferably, the system further comprises a sensing component configured to measure the motion of the patient's respiratory muscles to collect motion information, and to determine the motion state of the patient's respiratory muscles based on the collected motion information.
Preferably, the first and second control units 120 and 220 are configured to control the first and second actuators 110 and 210, respectively, to provide mechanical and/or electrical stimulation to the chest and abdomen of the patient that matches the motion state of the patient.
Preferably, the electrodes are configured to adjust the intensity of the electrical stimulation sequentially by the detected muscle strength fluctuation and the real-time detected blood oxygen saturation. According to another embodiment, the system is configured to preferentially employ a control scheme that adjusts the electrode electrical stimulation intensity based on the detected blood oxygen saturation in real time when the detected blood oxygen saturation is below 95. When the detected saturation of the blood sample is higher than 95, a control mode of adjusting the electrode electric stimulation intensity based on the detected fluctuation rate of the muscle strength is preferably adopted. This control strategy skillfully balances the immediate safety needs of the patient (by monitoring blood oxygen saturation) and the long-term therapeutic effects (by monitoring muscle strength). This balance is particularly important for management of chronically ill patients. By adjusting the treatment parameters in real time, the system is able to provide tailored treatments according to the real-time condition of each patient. The system is not only able to cope with acute conditions (e.g. sudden blood oxygen decline) but also to prevent potential problems (e.g. overfatigue) by monitoring the muscle status. Such a prophylactic approach may greatly reduce the risk of complications.
Long-term use of such a system will accumulate a significant amount of valuable data that can be used to further optimize the treatment strategy and possibly even provide new insight into the study of related diseases. By optimizing the stimulation intensity and frequency, the system may significantly improve patient comfort and quality of daily life, which is critical for patients who use breathing assistance devices for long periods of time. This control strategy demonstrates how to integrate multiple physiological parameters into one intelligent decision system, providing a heuristic for the design of other medical devices.
Preferably, the first actuator 110 is configured to apply vibrations along the patient's rib clearance to enhance the inhalation process. Preferably, the first actuator 110 is configured to apply pressure to pectoral major and pectoral minor muscles to enhance the exhalation process. Preferably, the second actuator 210 is configured to apply pressure or vibration to the extraabdominal oblique muscle and the intraabdominal oblique muscle on the abdominal side of the patient. Preferably, the second actuator 210 is configured to apply a circumferential pressure or vibration to the lateral abdominal muscle at the waist of the patient.
Preferably, for intercostal muscles, a small vibration device may be used to stimulate along the intercostal space. This helps to enhance the inspiration process. For pectoral large and pectoral small muscles, a larger area vibrating pad or pressure pad may be used, covering the entire front side of the chest. This helps to enhance the exhalation process. For rectus abdominis, stimulation may be performed from below the sternum to above the pubis using a ribbon vibrator or pressure ribbon. For extraabdominal and intraabdominal oblique muscles, an oblique stimulation zone may be used on the abdominal side. Stimulation of the abdominal transverse muscle may be achieved by using a circumferential stimulation band around the waist.
Preferably, a pattern of alternating stimulation of the chest and abdomen is devised, mimicking the rhythm of natural breathing. For example, chest muscles are stimulated for 1-2 seconds (mimicking inspiration) and then abdominal muscles are stimulated for 2-3 seconds (mimicking expiration). Initially, low intensity and low frequency are used, gradually increasing to a level that is acceptable for patient comfort. The frequency can be from 12 to 20 times per minute, mimicking the normal breathing frequency.
The system can analyze muscle strength data in real time to predict the risk of acquired muscle weakness and to stimulate the patient's diaphragm, including vibration, compression, based on the detection. As shown in fig. 6, the present application provides a life support system for acquired muscle weakness. The system comprises a first sensor 131 configured to monitor the muscular activity of the deglutition muscle, a second sensor 133 configured to monitor the muscular activity of the proximal end of the limb, a data processing unit 140 receiving the detected data of the muscular activity from the first sensor 131 and/or the second sensor 133 and calculating muscle strength data, and a breathing assistance unit configured to assist the patient in breathing by means of vibration, squeezing, electric shock and/or infrared. The data processing unit 140 is configured to collect muscle strength data of a first period of time when the patient performs the first standardized action as reference muscle strength data by the first sensor 131 and/or the second sensor 133, analyze muscle strength fluctuations by collecting muscle strength data of a second period of time of a preset period of time after the patient performs the first standardized action by the first sensor 131 and/or the second sensor 133, and stimulate the diaphragm of the patient according to the muscle strength fluctuations to maintain the diaphragm strength of the patient.
Preferably, the first sensor 131 and the second sensor 133 are configured in a wearable device. The data processing unit 140 is in data communication with the first sensor 131 and the second sensor 133. The data processing unit 140 divides and analyzes the data of the first sensor 131 and the second sensor 133 by time. Preferably, the system further comprises a user interface 150 for presenting muscle weakness and risk assessment results. Preferably, the wearable device further comprises at least one Electromyography (EMG) sensor 134 for monitoring and analyzing the overall muscle activity. The sensor sends the data to the data processing unit 140 via a wireless protocol. Preferably, the data processing unit 140 further includes a set of advanced data processing algorithms for extracting and analyzing key features in the sensor data, and a set of machine learning and deep learning algorithms for refining the predictive model and enhancing system performance. Preferably, the user interface 150 includes a mobile application 151 or Web interface 152 that allows patient access and interaction, a personalized risk assessment and management planning module 153 that is tailored to the individual needs and conditions of the patient, educational resources and support materials 154 that are intended to provide knowledge and self-management policies to the patient.
The interaction between the data processing unit 140 and the wearable device 130 is optimized as a data transmission protocol and a communication protocol to ensure real-time transmission and real-time processing of muscle strength data, a secure data encryption and authentication mechanism to protect the privacy of the patient and the integrity of the data, and a data synchronization and integration function with the cloud storage server 160 for data backup and remote access.
Preferably, the system further includes an enhanced connection to the cloud storage server 160, including powerful data security measures such as access control, data encryption, and periodic security auditing, expandable storage capacity to accommodate increases in the amount of patient data, and data analysis tools for extracting valuable insight from accumulated patient data, facilitating system improvement and study of muscle weakness.
A specific algorithm for evaluating muscle weakness according to the present application is shown in fig. 7, and specifically described as follows:
symbol definition
First of allEach time period
First of allMuscle strength data for each session
Normalized muscle strength data set for a first period
First of allEach time periodMiddle (f)Data-associated muscle strength data
Judgment process
1. Normalizing first period data
Muscle strength data for a first period of timePerforming standardization processing to obtain a standardized set:
,
Wherein, theIs thatIs used for the average value of (a),Is the standard deviation.
2. Calculating muscle strength fluctuations
For each subsequent period of time(I > 1) calculating muscle strength data thereofAnd (3) withDifferences in corresponding data:
。
3. judging acquired muscle weakness
If it isExceeding a preset thresholdIt is judged that there is acquired muscle weakness in this period.
The selection of the threshold may be adjusted based on clinical experience or personal history data of the patient. Meanwhile, the joint analysis of data noise, individual differences of patients and multi-muscle group data should be considered to optimize the judgment accuracy.
Specifically, clinical test data for a digital patient are listed below in table 5.
TABLE 5
Table 5 lists clinical test data for two patients, including corresponding deglutition muscle EMG data, arm acceleration data, measured arm equivalent mass, calculated muscle strength data, respiratory rate and tidal volume data at corresponding time points, detected when two patients numbered 1, 2 respectively perform deglutition or lifting movements at two time points, T0 and T1 respectively. Deglutition muscle strength is approximately linearly related to EMG signal intensity and can be calculated using the following equation:
;
Wherein, theIs the calculated swallowing muscle strength (unit: N),Is the proportionality coefficient (unit: N/uv), needs to be determined by calibration,The measured electromyographic signal intensity (in. Mu.V).
According to one embodiment, the deglutition muscular strength measurement is performed using a surface electromyography sensor, i.e., EMG sensor 134, that records the amplitude and frequency of electromyographic signals during deglutition activities. The measurement parameter of the arm muscle strength measurement is the arm movement acceleration a and the arm equivalent mass m. Muscle strength f=m×a. Wherein F is muscle strength (Newton, N), m is equivalent mass of the arm (kg, kg), and a is acceleration of the arm (m/s 2 ).
The test procedure is a first session (reference data collection) in which standardized actions (such as swallowing or raising the arm) are performed and muscle strength data are recorded as a reference. And a second period (fluctuation analysis) of performing the standardized action again after the preset period, recording muscle strength data, and analyzing the strength fluctuation compared with the reference data. The force fluctuations are characterized using a calculated force fluctuation rate R. R= (F A second period of time-F First period of time) / F First period of time ×100%. Where R represents the force fluctuation rate, F A second period of time represents the muscle force detected in the second period, and F First period of time represents the muscle force detected in the first period.
The relation between the electric stimulation intensity and the muscle strength fluctuation rate is I After adjustment= I Foundation X (1-k X R), wherein I Foundation is the electric stimulation basic intensity, for example, the electric stimulation intensity is shown in the table 2 and the table 3, I After adjustment is the electrode electric stimulation intensity adjusted according to the patient strength fluctuation rate, R is the strength fluctuation rate, k is an adjustment coefficient, and the value range is 0.1-0.3.
Preferably, the second actuator 210 is an abdominal pressure device that receives information of fluctuation of muscle strength of the patient's deglutition muscles and the proximal ends of the upper limbs of the data processing unit 140, presses the abdomen of the patient in a bionic respiratory motion manner, and maintains the activities of the diaphragm muscles and the lungs of the patient. Preferably, the compression mode and force are also set in association with the intra-abdominal pressure of the patient. The intra-abdominal pressure of the patient may be provided by intra-abdominal pressure detection means.
For example, in muscle strength fluctuation detection, the system detects that the patient's strength of the deglutition muscles has decreased by 15% in 10 minutes (from initial 100N to 85N). At the same time, the upper limb proximal muscle strength was reduced by 20% in 15 minutes (from initial 150N to 120N). Based on these data, the abdominal pressure device is activated to begin pressing the patient's abdomen in a biomimetic respiratory motion. The initial compression frequency was set to 12 times per minute, mimicking the normal breathing frequency. The initial compression depth was set to 2cm to simulate light respiratory motion. The intra-abdominal pressure detecting means measures the basic intra-abdominal pressure of the patient to be 8 mmHg. The intra-abdominal pressure fluctuation range is set to be 6-10 mmHg during normal respiration. If an intra-abdominal pressure drop of 5 mmHg is detected, the system increases the compression force to a depth of 3cm, increasing the frequency to 14 per minute. If the intra-abdominal pressure increases to 12 mmHg, the system reduces the compression force to a depth of 1.5cm, and the frequency to 10 times per minute. The system evaluates muscle strength and intra-abdominal pressure every 5 minutes, dynamically adjusting compression parameters. If the muscle strength returns to more than 95% of the initial value within 15 minutes (swallowing muscle strength returns to 95N and upper limb proximal muscle strength returns to 142.5N), the system gradually reduces the compression strength and frequency. The system is set to a maximum compression depth of no more than 4cm to prevent stress on the viscera. The highest compression frequency was limited to 20 times per minute to avoid excessive ventilation. The system may personalize the initial parameters based on the patient's height, weight, and vital capacity. For example, for a patient with a height of 170cm and a weight of 65kg, the initial compression depth may be set to 2.2cm. With such accurate, personalized dynamic adjustments, the respiratory assistance system is able to more effectively maintain the patient's diaphragmatic function and respiratory ability while reducing the risk of complications.
According to a specific embodiment, the data processing unit 140 is configured to control the actuator to vibrate at a varying frequency having a non-constant frequency within the operating frequency range of 5Hz to 1000Hz during a given time interval to assist in maintaining the patient's diaphragmatic muscle strength. For example, the actuator specification is 50 grams in weight, 5cm 3cm 1cm in size, and 2 watts in maximum output. The vibration frequency range is that the lowest frequency is 5Hz, the highest frequency is 1000Hz, and the frequency adjustment precision is 1Hz. The vibration mode comprises a low frequency band (5 Hz-50 Hz) with an initial frequency of 10Hz for 5 seconds, gradually increasing to 30Hz for 10 seconds, and decreasing to 20Hz for 5 seconds. Mid-band (51 Hz to 500 Hz) is that starting from 100Hz, 50Hz is increased every 2 seconds until 300Hz is reached, 5 seconds are maintained at 300Hz, and then 100Hz is reduced every 3 seconds until 100Hz is reached. The high frequency band (501 Hz-1000 Hz) is that from 600Hz, the frequency is rapidly increased to 900Hz (within 1 second), the frequency is maintained at 900Hz for 2 seconds, the frequency is slowly reduced to 700Hz (within 3 seconds), and the cycle is repeated for 3 times. The time interval is set to 15 minutes for total treatment time, 5 minutes for low frequency band, 7 minutes for medium frequency band and 3 minutes for high frequency band. Preferably, the system evaluates the patient's diaphragmatic Electromyography (EMG) signal every 30 seconds. If the EMG signal strength drops by more than 10%, the system will increase the vibration strength and duration. For example, if an EMG signal drop is detected at the mid-frequency, the system may increase the 300Hz hold time from 5 seconds to 8 seconds. The system may adjust the vibration intensity based on the patient's weight and muscle condition. For example, the initial vibration intensity may be set to 1.5 watts for a patient weighing 60kg, and 1.8 watts for a patient weighing 80 kg. Continuous high frequency vibration (> 800 Hz) for no more than 30 seconds. After every 5 minutes of treatment, the system was forced to pause for 30 seconds to prevent muscle fatigue. If the patient reports discomfort (via a button or voice command), the system will immediately decrease the frequency by 50%. If the discomfort persists, the system will step down in frequency. With such accurate, dynamic vibratory stimulation, the respiratory assistance system is able to effectively maintain and stimulate the patient's diaphragmatic muscle strength while providing a personalized and safe treatment regimen. This approach may help to prevent respiratory hypofunction in patients with acquired muscle weakness and improve their quality of life.
Preferably, the second actuator 210 further comprises a band of at least two vibration modules externally applied to the abdominal region of the user to stimulate the diaphragm to enhance lung function.
Preferably, the first sensor 131 is a flexible sensor that is fitted to a first region of the skin surface of the patient's deglutition muscle, which derives a first signal reflecting the state of change in strain of the first region and a second signal characterizing the state of change in curvature of the first region from the deformation of the first region.
Preferably, the first sensor 131 includes a strain sensing unit and an optical sensing unit. The strain sensing unit obtains a first signal according to the deformation of the first area. The optical sensing unit obtains a second signal according to the deformation of the first area.
Preferably, the data processing unit 140 acquires muscle strength data of the patient's deglutition muscle based on the first signal and the second signal acquired by the first sensor 131.
Preferably, the sensor substrate is fabricated using a flexible material (such as polydimethylsiloxane PDMS), ensuring skin fit and not affecting swallowing movements. The strain sensing unit is made of nano materials such as graphene or carbon nano tubes, and has high sensitivity and good flexibility. The optical sensing unit can adopt Fiber Bragg Grating (FBG) technology, and can accurately measure tiny curvature change. The surface of the sensor is coated with a biocompatible material to reduce irritation to the skin. The sensor can be closely attached to the surface of the deglutition muscle, and can accurately capture muscle movement. The flexible design can not influence normal swallowing of a patient, and the comfort level of long-term monitoring is improved. The strain sensing unit measures muscle deformation through resistance change and converts the muscle deformation into electric signals for output. The optical sensing unit measures curvature by utilizing the phase change of the light wave in the optical fiber, and converts the curvature into an optical signal to be output. The analog signal is converted to a digital signal using a high precision analog-to-digital converter (ADC). And a low-noise amplifying circuit is designed to improve the signal quality. By the method, strain and curvature information can be acquired simultaneously, and the movement state of the deglutition muscles can be comprehensively reflected. High sampling rates (e.g., 1000 Hz) can capture rapid swallowing movements. A digital filtering algorithm (such as a butterworth filter) is used to remove ambient noise and baseline wander. A machine learning algorithm (such as a support vector machine SVM or deep neural network) is applied to extract features from the original signal. The strain and curvature data are converted into muscle strength data to build a mathematical model to realize real-time data processing and provide instant feedback. In this way, the muscular strength of the deglutition muscles is accurately estimated, and objective basis is provided for clinical evaluation.
Preferably, the data processing unit 140 further includes a data receiving module 141 for receiving the muscle activity detection data from the first sensor 131 and the second sensor 133, a data analyzing module 143 for calculating muscle strength data from the received muscle activity detection data, analyzing the fluctuation of the muscle strength using a predetermined algorithm model, and outputting a risk assessment result of acquired muscle weakness, and a user interface 150 for displaying the analyzed risk assessment result and advice.
Preferably, the data processing unit 140 is in communication with the wearable device for receiving muscle strength data from the first sensor 131 and/or the second sensor 133 and analyzing the data to predict the risk of acquired muscle weakness, wherein the muscle strength data is divided according to a plurality of time periods different from each other, in particular, the muscle strength data of a first time period, i.e. reference muscle strength data acquired when the patient performs a first normalization action, and the muscle strength data of a second time period, i.e. muscle strength data acquired after a preset time period after the patient performs the first normalization action, for predicting a fluctuation of the muscle strength.
In this way, the wearable device integrates the first sensor 131 and the second sensor 133 for collecting muscle strength data. The data processing unit 140 is wirelessly connected with the wearable device, receives and analyzes muscle strength data. During a first period of time, the patient performs a first standardized action (such as a grip test or a swallowing test), reference muscle strength data is collected. In a second period, the patient again performs the same action for a preset period of time (e.g., 30 minutes) after performing the first standardized action, and muscle strength data for prediction is collected. The data processing unit 140 performs data preprocessing, filtering, denoising and other processes on the original sensor data, performs feature extraction, extracts key features such as peak strength, duration time, attenuation rate and the like from the processed data, performs data comparison, compares muscle strength data in a first period and a second period, calculates a strength change percentage, performs risk assessment, predicts the acquired muscle weakness risk by using a machine learning algorithm (such as a support vector machine or random forest) based on the data comparison result, outputs a stimulation mode according to a preset model, and starts a corresponding breathing assistance device. The stimulation mode includes a selection of modes of compression, vibration, shock or infrared, and specific setting parameters of the corresponding modes. In the technical scheme, potential risks of acquired muscle weakness can be found early by comparing muscle strength changes in a short time, and precious time is won for clinical intervention. Detection with the wearable device 130 reduces discomfort of conventional inspection methods and improves patient compliance. Through this non-invasive wearing equipment detection, can realize 24 hours uninterrupted monitoring, master patient's muscle strength change condition comprehensively. The system may perform a customized risk assessment based on patient personal baseline data, taking into account individual differences. The data can be transmitted to the medical institution in real time, so that a doctor can conveniently monitor the condition of a patient remotely and adjust the treatment scheme in time. The technical scheme is suitable for data accumulation and analysis, and long-term collected data is helpful for in-depth research on pathogenesis and influencing factors of the acquired muscle weakness. Through standardized data acquisition and analysis flow, subjective judgment errors are reduced, and objectivity and accuracy of diagnosis are improved. Through early warning and timely intervention, the occurrence of serious complications can be reduced, so that the overall medical cost is reduced. The wearable device 130 and data analysis-based acquired muscle weakness risk prediction system and the respiratory/life support system can remarkably improve the prevention, diagnosis and rehabilitation treatment level of the acquired muscle weakness and provide better medical guarantee for patients.
Preferably, the wearable device further comprises a communication module 132 for wirelessly transmitting muscle strength data acquired by the first sensor 131 and the second sensor 133 to the data processing unit 140.
Preferably, the preset time period is adjusted based on muscle strength data, historical muscle strength data, and/or physician order associations of the patient's first time period.
Preferably, the data processing unit 140 is further in communication with a cloud storage server 160 for transmitting the analyzed muscle strength fluctuation data and risk assessment results to the cloud storage server 160 for storage, receiving historical data from the cloud storage server 160 for aiding in analysis determination of current data and improving accuracy of predictions, and allowing authorized medical personnel to access the patient's historical and current muscle strength data for remote diagnosis and planning of treatment sessions.
Preferably, the communication module 132 supports at least one wireless communication technology including, but not limited to, bluetooth, wi-Fi, NFC, or cellular network technology.
Preferably, the user interface 150 is a touch screen for providing interactive operation, guiding the patient through the user interface 150 to perform standardized actions, and displaying muscle strength data and assessment results in real time.
Preferably, the data receiving module 141 and the data analyzing module 143 are embedded software modules running on a hardware platform of the data processing unit 140, wherein the data processing unit 140 hardware platform includes, but is not limited to, a processor 144, a memory 145 and a storage device 146. Preferably, the data processing unit 140 is further configured with a data buffering module 142 for buffering real-time muscle strength data received from the wearable device, ensuring that the data is not lost when the wireless network is unstable or interrupted. Preferably, the data analysis module 143 configures a machine learning algorithm to learn and adapt to the muscle strength fluctuation characteristics of the individual patient and update the algorithm model over time. The data analysis module 143 configures an anomaly detection algorithm for detecting and marking anomalies in the muscle strength data. The data analysis module 143 also configures a trend analysis algorithm for identifying long-term trends and short-term fluctuations from the continuous period of muscle strength data to aid in assessing progression of muscle weakness.
It should be noted that the above-described embodiments are exemplary, and that a person skilled in the art, in light of the present disclosure, may devise various solutions that fall within the scope of the present disclosure and fall within the scope of the present disclosure. It should be understood by those skilled in the art that the present description and drawings are illustrative and not limiting to the claims. The scope of the invention is defined by the claims and their equivalents. The description of the invention includes a plurality of inventive concepts, such as "preferably", "according to a preferred embodiment" or "optionally" each meaning that the corresponding paragraph discloses a separate concept, the applicant reserves the right to filed a divisional application according to each inventive concept.