A ventilation detection method and device for myasthenia gravis patients

Abstract

The present invention relates to a ventilation detection device for myasthenia gravis patients. The device comprises: a muscle strength monitoring module, configured to monitor the swallowing muscle and proximal limb muscle activities of the myasthenia gravis patient to obtain muscle strength data of the myasthenia gravis patient; a respiratory monitoring module, configured to detect the respiratory rate, the tidal volume of each respiratory cycle, and the inspiratory time of each respiratory cycle when the myasthenia gravis patient is subjected to mechanical assisted ventilation, and obtain the change trend over time; a control module, signal-connected to the muscle strength monitoring module and the respiratory monitoring module, and configured to determine that human-machine resistance exists during the mechanical assisted 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 inspiratory time over time is inconsistent with the change trend of pre-stored corresponding parameters.

Description

Technical Field

[0001] The present invention relates to the technical field of medical detection equipment, and in particular to a ventilation detection method and device for myasthenia gravis patients. Background Art

[0002] Acquired myasthenia gravis is an autoimmune disease that primarily affects the function of the neuromuscular junction. The cause of acquired myasthenia gravis is that the body's immune system mistakenly attacks the acetylcholine receptors at the neuromuscular junction, resulting in the inability of nerves to effectively transmit signals to the muscles. The main symptoms of acquired myasthenia gravis include: muscle weakness, especially of the eyes and facial muscles; drooping eyelids; diplopia (double vision); difficulty swallowing; and difficulty breathing. Existing diagnostic methods for acquired myasthenia gravis include: physical examination; blood tests (antibody tests); neuroelectrophysiological tests; and drug tests (such as the Nestin test). Traditional diagnostic methods rely on clinical evaluation and electrophysiological tests, which are usually not only time-consuming but also somewhat invasive to patients.

[0003] Nguyen, Minh NL, et al. "Tracking eye movements for diagnosis in myasthenia gravis: a comprehensive review." Journal of Neuro-Ophthalmology 42.4 (2022): 428-441. This paper explores the potential of quantitative eye and pupil tracking as noninvasive alternatives for diagnosing MG. It highlights how extraocular muscle fatigue can manifest as various abnormalities in eye movements, making eye tracking a valuable tool for early diagnosis, particularly in patients presenting with ocular symptoms.

[0004] Qin, Shixin, et al. "Application for measuring eyelid weakness in individuals with myasthenia gravis." 2021 IEEE Global Humanitarian TechnologyConference (GHTC). IEEE, 2021. This paper discusses a machine learning-based application that can quantitatively track and record eyelid weakness (ptosis), a common symptom of myasthenia gravis. This technology uses eye tracking to help clinicians diagnose myasthenia gravis and monitor disease progression.

[0005] Liang, Timothy, et al. "Analysis of electrooculography signals for the detection of Myasthenia Gravis." Clinical Neurophysiology 130.11 (2019):2105-2113. This study evaluated the potential of electrooculography (EOG) signals for the diagnosis of MG. It demonstrated the effectiveness of analyzing EOG signals to distinguish MG patients from controls, further demonstrating the relevance of eye tracking in the diagnosis of MG.

[0006] CN115177291A provides a method for identifying ICU-acquired myasthenia gravis, comprising: obtaining a patient's muscle ultrasound data, medical text data, and clinical examination data; and identifying the patient's ICU-acquired myasthenia gravis condition based on the muscle ultrasound data, the medical text data, and the clinical examination data using a pre-set multi-transactive memory network. This technical solution can identify the patient's ICU-acquired myasthenia gravis condition and severity without causing muscle trauma.

[0007] These neuroelectrophysiological examination methods for acquired myasthenia have limitations, including insufficient sensitivity. Repeated nerve stimulation (RNS) testing has low sensitivity in patients with ocular myasthenia, potentially leading to false-negative results. They also have low specificity, are highly procedure-dependent, have poor patient tolerance, and require high-quality equipment. Existing motor function assessment methods for acquired myasthenia also suffer from subjectivity, lack of standardization, limited testing time, difficulty quantifying disease severity and its impact on patient quality of life, and insufficient sensitivity.

[0008] Myasthenia gravis affects signal transmission between nerves and muscles, leading to abnormal fatigue and weakened skeletal muscles. This disease typically affects the extraocular muscles, facial muscles, throat muscles, and limb muscles. When myasthenia gravis progresses to a more severe stage, it may affect the muscles that control breathing. This condition, known as a "myasthenic crisis" or "respiratory muscle weakness," is an acute complication of myasthenia gravis. When respiratory muscles are affected, patients may experience difficulty breathing and, in severe cases, even become unable to breathe on their own, requiring emergency medical intervention, such as the use of a ventilator. Because myasthenia gravis is a disease of the neuromuscular junction, patients may experience varying degrees of neuromuscular weakness, which affects their need for ventilator support. Myasthenia gravis patients are more susceptible to ventilator-ventilator asynchrony when using a ventilator, meaning a mismatch between the patient's spontaneous breathing and the ventilator's assistance. This condition can cause discomfort and even worsen breathing difficulties. The existing technology lacks a detection device that can determine the spontaneous breathing status of myasthenia gravis patients and whether their spontaneous breathing matches the ventilator's assistance. Summary of the Invention

[0009] In response to the deficiencies of the existing technology, the present application provides a ventilation detection method for myasthenia gravis patients, which is applied to a respiratory assistance system, including: monitoring the muscle activity of the swallowing muscles and the proximal limb muscles of the myasthenia gravis patients; obtaining the muscle strength data of the myasthenia gravis patients based on the monitoring data of the swallowing muscle activity and the proximal limb muscle activity; when the myasthenia gravis patients are subjected to mechanical assisted ventilation, detecting the respiratory rate, the tidal volume of each respiratory cycle and the inspiratory time of each respiratory cycle; obtaining the changing trends of the respiratory rate, the tidal volume of each respiratory cycle and the inspiratory time over time; when the muscle strength data of the myasthenia gravis patient is lower than a preset muscle strength threshold, and the changing trends of the respiratory rate, the tidal volume of each respiratory cycle and the inspiratory time over time are inconsistent with the changing trends of the pre-stored corresponding parameters, it is determined that there is human-machine resistance during the mechanical assisted ventilation of the myasthenia gravis patient.

[0010] According to a preferred embodiment, obtaining muscle strength data of myasthenia gravis patients based on monitoring data of swallowing muscle activity and monitoring data of proximal limb muscle activity includes: collecting muscle strength data of a first time period when the patient performs a first standardized action as reference muscle strength data; collecting muscle strength data of a second time period of a preset time period after the patient performs the first standardized action; and analyzing muscle strength fluctuations based on the muscle strength data of the second time period and the reference muscle strength data.

[0011] According to a preferred embodiment, the first standardized action is swallowing and / or an arm raising action.

[0012] According to a preferred embodiment, after determining that there is human-machine resistance during mechanical assisted ventilation in a patient with myasthenia gravis, the method further includes: judging the type of human-machine resistance based on the changing trends of respiratory frequency, tidal volume of each respiratory cycle, and inspiratory time over time.

[0013] According to a preferred embodiment, if the increase in respiratory rate is accompanied by fluctuations in the tidal volume waveform, it is determined that there is a failure to effectively trigger the ventilator to deliver air.

[0014] According to a preferred embodiment, if the increase in respiratory rate is accompanied by an increase in inspiratory time, it is determined that the ventilator gas delivery is insufficient.

[0015] According to a preferred embodiment, if the tidal volume is lower than a preset tidal volume threshold, the inspiratory time is shorter than a preset inspiratory time threshold and the respiratory rate increases, it is determined that the ventilator has premature exhalation triggering.

[0016] The ventilation detection method of this application can be used to determine the presence and type of patient-ventilator resistance based on the patient's myasthenia gravis condition characteristics, their muscle strength, and the changing trends in tidal volume, respiratory rate, and inspiratory time during mechanically assisted exhalation. This method helps the ventilator promptly detect and adjust the presence of patient-ventilator resistance during mechanically assisted exhalation in patients with myasthenia gravis.

[0017] The present application also provides a ventilation detection device for myasthenia gravis patients, the device comprising: a muscle strength monitoring module configured to monitor the muscle activity of the swallowing muscles and the proximal limb muscles of the myasthenia gravis patient, and obtain the muscle strength data of the myasthenia gravis patient based on the monitoring data of the swallowing muscle activity and the monitoring data of the proximal limb muscle activity; a respiratory monitoring module configured to detect the respiratory rate, the tidal volume of each respiratory cycle and the inspiratory time of each respiratory cycle when the myasthenia gravis patient is subjected to mechanical assisted ventilation, and obtain the respiratory rate, the tidal volume of each respiratory cycle and the inspiratory time over time. a control module, which is connected to the muscle strength monitoring module and the respiratory monitoring module by signal, and is configured to obtain the muscle strength data of the myasthenia gravis patient from the muscle strength monitoring module, and obtain the respiratory rate, tidal volume of each respiratory cycle and inspiratory time of the myasthenia gravis patient over time from the respiratory monitoring module, wherein, when the muscle strength data of the myasthenia gravis patient is lower than the preset muscle strength threshold, and the respiratory rate, tidal volume of each respiratory cycle and inspiratory time over time are inconsistent with the pre-stored corresponding parameter change trends, it is determined that there is human-machine resistance during the mechanical assisted ventilation process of the myasthenia gravis patient.

[0018] According to a preferred embodiment, the control module is further configured to: if the increase in respiratory rate is accompanied by fluctuations in the tidal volume waveform, determine that there is a failure to effectively trigger the ventilator to deliver air.

[0019] According to a preferred embodiment, the control module is further configured to determine that the ventilator gas delivery is insufficient if the increase in respiratory rate is accompanied by an increase in inspiratory time.

[0020] The present application further provides a system that uses wearable devices to monitor patient muscle activity and maintain the muscle strength of the patient's respiratory muscles, especially the diaphragm. The system can analyze muscle strength data in real time to predict the risk of acquired muscle weakness and perform stimulation of the patient's diaphragm including vibration, compression, electric shock and / or infrared according to the test results.

[0021] The present application provides a respiratory assistance system for patients with acquired myasthenia gravis, comprising the aforementioned ventilation detection device. The system further comprises: a chest assistance component, comprising a first actuator configured for external mechanical contact with the patient's chest and a first control unit connected to the first actuator signal; an abdominal assistance component, comprising a second actuator configured for external mechanical contact with the patient's abdomen and a second control unit connected to the second actuator signal; wherein the first control unit and the second control unit are configured to cooperate with each other based on the patient's muscle strength data to respectively control the first actuator and the second actuator to provide mechanical stimulation and / or electrical stimulation to the patient's chest and abdomen to assist the patient's breathing.

[0022] According to a preferred embodiment, the system also includes: a first sensor configured to monitor the muscle activity of the swallowing muscles; a second sensor configured to monitor the muscle activity of the proximal limbs; and a data processing unit that receives detection data of muscle activity from the first sensor and / or the second sensor and calculates muscle strength data.

[0023] According to a preferred embodiment, the data processing unit is configured to collect muscle strength data of a first time period when the patient performs a first standardized action through a first sensor and / or a second sensor as reference muscle strength data, and to collect muscle strength data of a second time period of a preset time period after the patient performs the first standardized action through the first sensor and / or the second sensor to analyze muscle strength fluctuations.

[0024] According to a preferred embodiment, the first standardized action includes but is not limited to swallowing action and / or arm raising action, the proximal end of the limb is the patient's arm, the second sensor is configured to detect the movement acceleration of the patient's arm and the equivalent mass of the patient's arm, and the data processing unit calculates the muscle strength data of the user's arm during 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.

[0025] According to a preferred embodiment, the system further comprises a sensing component configured to measure the movement of the patient's respiratory muscles to collect movement information, and determine the movement state of the patient's respiratory muscles based on the collected movement information.

[0026] According to a preferred embodiment, the first control unit and the second control unit are configured to control the first actuator and the second actuator respectively based on the patient's motion state to provide mechanical stimulation and / or electrical stimulation to the patient's chest and abdomen that matches the motion state.

[0027] According to a preferred embodiment, the first actuator includes: a chest strap with adjustable tightness that can surround the chest cavity, a first electrode arranged on the chest strap for stimulating the pectoralis major muscle, a second electrode arranged on the chest strap for stimulating the intercostal muscles, and a first inflatable and deflable mechanical airbag arranged on both sides of the chest strap corresponding to the chest cavity.

[0028] According to a preferred embodiment, the second actuator includes: an abdominal belt with adjustable tightness that can surround the abdominal cavity, a third electrode arranged on the abdominal belt for stimulating the rectus abdominis muscle, a fourth electrode arranged on the abdominal belt for stimulating the external oblique muscle, and a second inflatable and deflable mechanical airbag arranged at a position on the front of the abdomen corresponding to the abdominal belt.

[0029] According to a preferred embodiment, the first actuator and the second actuator are configured to assist the patient in inhalation in the following manner: the first electrode activates the pectoralis major muscle to assist in lifting the thorax, and the second electrode activates the intercostal muscles to help expand the chest cavity; the third electrode and the fourth electrode respectively activate the rectus abdominis and external oblique abdominal muscles to assist in contracting the abdominal muscles and lowering the diaphragm; the first mechanical airbags on both sides of the chest cavity are inflated to assist in outward expansion of the chest cavity; the second mechanical airbag on the front of the abdomen is contracted to assist in lowering the diaphragm; electrical stimulation and mechanical assistance work together to increase the volume of the chest cavity, reduce the pressure in the chest cavity, and promote the entry of air into the lungs.

[0030] According to a preferred embodiment, the first actuator and the second actuator are configured to assist the patient in exhaling in the following manner: the stimulation intensity of the first electrode and the second electrode is reduced, allowing the chest cavity to fall back naturally; the stimulation intensity of the third electrode and the fourth electrode is increased to assist the abdominal muscles in contracting, squeezing the abdominal cavity, and pushing the diaphragm up; the first mechanical airbags on both sides of the chest cavity begin to deflate, allowing the chest cavity to retract; the second mechanical airbag on the front of the abdomen is inflated to assist in squeezing the abdominal cavity; electrical stimulation and mechanical assistance cooperate to increase the pressure in the chest cavity and push the gas out of the lungs.

[0031] The technical benefits of the respiratory assistance system for patients with acquired myasthenia gravis include real-time monitoring and early warning. Wearable devices monitor swallowing and proximal limb muscle activity in real time, enabling timely detection of subtle changes in muscle strength. By comparing muscle strength data from different time periods, the system can identify abnormal fluctuations in muscle strength early on, providing a basis for early intervention. The system collects muscle strength data from patients performing standardized movements as a reference to establish personalized baseline data. This approach accounts for individual differences, improving the accuracy and specificity of assessments. This is demonstrated in the following ways: First, dynamic assessment. By comparing muscle strength data from different time periods, the system can dynamically assess the patient's muscle status, reflecting the progression or improvement of the disease. Second, respiratory function protection. The system specifically focuses on diaphragmatic function, one of the most dangerous complications in patients with acquired myasthenia gravis. Through the respiratory assistance unit, the system can actively maintain diaphragmatic strength, reducing the risk of respiratory failure. Through multimodal intervention, the respiratory assistance unit offers multiple stimulation methods, allowing the most appropriate intervention method to be selected based on the patient's specific condition. Non-invasive monitoring using wearable devices reduces disruption to patients' daily lives and improves patient compliance. Data continuity and objectivity are guaranteed because the system provides continuous, objective data, overcoming the subjectivity and time-limited nature of traditional assessment methods. Leveraging predictive analytics, the system analyzes muscle strength fluctuation patterns to predict the onset or exacerbation of acquired muscle weakness, providing support for clinical decision-making. It also expands the possibilities for remote monitoring, providing a technical foundation for enabling medical teams to monitor patient conditions in real time and adjust treatment plans promptly. Furthermore, the system enables rehabilitation guidance, with the data collected being used to guide patient rehabilitation training and assist in developing personalized rehabilitation plans. Overall, through real-time, objective, and continuous monitoring and timely intervention, this system has the potential to significantly improve the management of acquired muscle weakness, enhance patient prognosis, and enhance quality of life. It provides an innovative, comprehensive solution for the diagnosis, treatment, and rehabilitation of acquired muscle weakness.

[0032] According to a preferred embodiment, the first sensor is a flexible sensor attached to the first area of ​​the skin surface of the patient's swallowing muscle, which obtains a first signal reflecting the change state of the strain of the first area and a second signal representing the change state of the curvature of the first area according to the deformation of the first area.

[0033] According to a preferred embodiment, the first sensor 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.

[0034] According to a preferred embodiment, the data processing unit obtains muscle strength data of the patient's swallowing muscles based on the first signal and the second signal collected by the first sensor.

[0035] According to a preferred embodiment, the data processing unit further includes: a data receiving module for receiving muscle activity detection data from the first sensor and the second sensor; a data analysis module for calculating muscle strength data based on the received muscle activity detection data, analyzing the volatility of muscle strength using a predetermined algorithm model, and outputting a risk assessment result of acquired muscle weakness; and a user interface for displaying the analyzed risk assessment results and suggestions.

[0036] According to a preferred embodiment, the preset time period is adjusted based on the patient's muscle strength data of the first period, historical muscle strength data and / or medical advice.

[0037] According to a preferred embodiment, the data analysis module is configured with a machine learning algorithm to learn and adapt to the muscle strength fluctuation characteristics of individual patients and update the algorithm model over time. The data analysis module is configured with an anomaly detection algorithm to detect and mark outliers in muscle strength data. The data analysis module is also configured with a trend analysis algorithm to identify long-term trends and short-term fluctuations in muscle strength data over consecutive time periods to assist in assessing the progression of muscle weakness. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 1 is a flow chart of a ventilation detection method for myasthenia gravis patients provided by the present invention;

[0039] Figure 2 This is a schematic diagram of the module structure of the ventilation detection device for myasthenia gravis patients provided by the present invention;

[0040] Figure 3 1 is a waveform diagram of an example of “failure to effectively trigger ventilator gas delivery” provided by an embodiment of the present invention;

[0041] Figure 4 1 is a waveform diagram of an example of “insufficient ventilator gas delivery” provided by an embodiment of the present invention;

[0042] Figure 5 is a waveform diagram of an example of “early exhalation triggering” provided in an embodiment of the present invention;

[0043] Figure 6 1 is a schematic diagram of the system structure of the respiratory assistance system for patients with acquired myasthenia gravis provided by the present invention;

[0044] Figure 7 It is a flow chart of an algorithm for acquired myasthenia gravis provided by the present invention;

[0045] Figure 8 is a device structure diagram of an embodiment of a respiratory assistance system for patients with acquired myasthenia gravis according to the present invention;

[0046] Figure 9 is a control flow chart of the inspiratory phase of an embodiment of the respiratory assistance system for patients with acquired myasthenia gravis of the present invention;

[0047] Figure 10 The present invention is a control flow chart of the exhalation phase of an embodiment of the respiratory assistance system for patients with acquired myasthenia gravis.

[0048] Reference Signs List

[0049] 100: Chest assistance assembly; 110: First actuator; 120: First control unit; 200: Abdominal assistance assembly; 210: Second actuator; 220: Second control unit; 111: Chest strap; 112: First electrode; 113: Second electrode; 114: First mechanical airbag; 211: Abdominal strap; 212: Third electrode; 213: Fourth electrode; 214: Second mechanical airbag; 130: Wearable device; 131: First sensor; 132: Communication module; 133: Second sensor; 134: EMG sensor; 140: Data processing unit; 141: Data receiving module; 142: Data caching module; 143: Data analysis module; 144: Processor; 145: Memory; 146: Storage device; 150: User interface; 151: Mobile application; 152: Web interface; 153: Risk assessment and management plan module; 154: Educational resources and support materials; 160: Cloud storage server. DETAILED DESCRIPTION

[0050] The following is a detailed description with reference to the accompanying drawings.

[0051] Myasthenia gravis is a chronic autoimmune disease that affects the transmission of signals between nerves and muscles, leading to abnormal fatigue and weakening of skeletal muscles. The disease usually affects the extraocular muscles, facial muscles, throat muscles, and muscles of the limbs. In some cases, myasthenia gravis can affect the muscles that control breathing, especially when the disease progresses to a more severe stage. This condition is called "myasthenic crisis" or "respiratory muscle weakness" and is an acute complication of myasthenia gravis. When the respiratory muscles are affected, patients may experience difficulty breathing, and in severe cases, they may not be able to breathe on their own and require emergency medical intervention, such as the use of a ventilator to assist breathing.

[0052] There are some special requirements and precautions for patients with myasthenia gravis who use ventilators to assist breathing compared to ordinary patients. Because myasthenia gravis is a disease of the neuromuscular junction, patients may show varying degrees of neuromuscular weakness, which will affect their need for ventilator support. Therefore, it is necessary to frequently assess the patient's neuromuscular status, including muscle strength, breathing depth and frequency, so that the ventilator settings can be adjusted in a timely manner. The use of quantitative neuromuscular transmission tests (such as single-fiber electromyography or repetitive nerve stimulation tests) can help better understand the status of neuromuscular function and thus guide treatment decisions.

[0053] The inventors discovered in their research that, during mechanically assisted ventilation, myasthenia gravis patients experience significant variations in muscle strength, particularly in respiratory muscles, due to differences in muscle weakness. Ventilator operating parameters are often set based on physician experience. Clinically, mismatches between ventilator settings and patient needs often lead to patient-machine resistance, such as failure to effectively trigger ventilator airflow, insufficient ventilator gas delivery, and premature exhalation. This situation is particularly common in patients with myasthenia gravis, whose muscle strength varies widely from individual to individual.

[0054] The present application provides a ventilation detection method for myasthenia gravis patients, which is applied to respiratory assistance systems, especially mechanical respiratory assistance systems, such as Figure 1 As shown, it includes: monitoring the muscle activity of the swallowing muscles and the proximal limb muscles of the myasthenia gravis patient; obtaining the muscle strength data of the myasthenia gravis patient based on the monitoring data of the swallowing muscle activity and the monitoring data of the proximal limb muscle activity; detecting the respiratory rate, the tidal volume of each respiratory cycle and the inspiratory time of each respiratory cycle when the myasthenia gravis patient is subjected to mechanical assisted ventilation; obtaining the changing trends of the respiratory rate, the tidal volume of each respiratory cycle and the inspiratory time over time; when the muscle strength data of the myasthenia gravis patient is lower than the preset muscle strength threshold, and the changing trends of the respiratory rate, the tidal volume of each respiratory cycle and the inspiratory time over time are inconsistent with the changing trends of the pre-stored corresponding parameters, it is determined that there is human-machine resistance during the mechanical assisted ventilation of the myasthenia gravis patient.

[0055] Tidal Volume (VT): This refers to the amount of air that moves in and out of the lungs with each breath, usually expressed in milliliters (mL). A typical tidal volume setting for adults is 5-8 mL / kg of ideal body weight. Respiratory Rate (RR): This represents the number of breaths per minute and is used to control minute ventilation (MV), the total amount of gas that moves in and out of the lungs in one minute. A common respiratory rate range for adults is 10-20 breaths / minute. Inspiratory Time (Ti): This refers to the length of time the inspiratory phase of each breath lasts. The choice of inspiratory time affects the I:E ratio and overall breathing pattern.

[0056] This application provides a ventilation monitoring method for patients with myasthenia gravis. By monitoring the patient's muscle activity and trends in ventilation parameters, the method aims to assess whether patient-machine resistance is occurring in real time and issue a timely alarm if resistance occurs. The swallowing muscles are one of the most commonly affected areas in patients with myasthenia gravis. Monitoring their activity can reflect the patient's neuromuscular function, especially when respiratory muscles are affected. Surface electromyography (sEMG) sensors are preferably attached to the patient's throat or neck to monitor the electrical activity of the swallowing muscles (such as the cricopharyngeus and suprahyoid muscles). These sensors can capture the electrical signals generated by muscle contraction, thereby quantifying muscle strength. For example, signal processing algorithms (such as filtering, denoising, and feature extraction) can be used to convert raw EMG signals into muscle strength data. Commonly used metrics include maximum myoelectric strength (RMS) and mean frequency (MNF).

[0057] Proximal limb muscles (such as shoulder girdle muscles and hip muscles) typically share similar neural innervation pathways with respiratory muscles. Therefore, changes in their muscle strength can indirectly reflect the status of these muscles. Surface electromyography (sEMG) sensors, preferably attached to the patient's scapula, quadriceps, and other areas, monitor the electrical activity of proximal limb muscles. Similar to swallowing muscles, signal processing algorithms are used to convert EMG signals into muscle strength data.

[0058] Preferably, based on clinical experience, the muscle strength of patients with myasthenia gravis usually decreases gradually as the disease progresses. In order to set a reasonable threshold, a baseline measurement can be performed when the patient is admitted to the hospital to record their normal muscle strength level. Subsequently, the threshold is dynamically adjusted according to changes in the patient's condition. For example, for healthy adults, the maximum myoelectric signal intensity (RMS) of the swallowing muscles is usually between 50-100 μV, and the RMS value of the proximal limb muscles is between 100-300 μV. For example, when the patient's muscle strength drops below 50% of the baseline value, it may indicate that neuromuscular function is significantly impaired and there is a higher risk of respiratory failure. For example, if the patient's swallowing muscle RMS value drops below 25 μV, or the proximal limb muscle RMS value drops below 150 μV, an alarm is triggered, indicating that further evaluation of respiratory support is required.

[0059] The inventors found in their research that there are differences in whether myasthenia gravis patients with different muscle strength levels have human-machine resistance and the possible consequences of human-machine resistance. When muscle strength is relatively high, patients can still maintain normal physiological activities through spontaneous breathing combined with ventilator assistance, and weak human-machine resistance generally does not significantly affect the patient's normal breathing. When muscle strength is severely reduced, human-machine resistance may cause serious damage to the patient. According to a specific embodiment, the muscle strength threshold is 30%, 50% or 70% of the baseline value. Medical institutions or detection device manufacturers can set corresponding pre-stored respiratory rate, tidal volume of each respiratory cycle and change trend of inspiratory time according to different muscle strength ranges, and can also set personalized pre-stored respiratory rate, tidal volume of each respiratory cycle and change trend of inspiratory time according to the patient's condition, so as to more accurately judge whether human-machine resistance occurs and evaluate the possible physiological impact on the patient.

[0060] Preferably, obtaining muscle strength data of myasthenia gravis patients based on monitoring data of swallowing muscle activity and monitoring data of proximal limb muscle activity includes: collecting muscle strength data of a first time period when the patient performs a first standardized action as reference muscle strength data; collecting muscle strength data of a second time period of a preset time period after the patient performs the first standardized action; and analyzing muscle strength fluctuations based on the muscle strength data of the second time period and the reference muscle strength data.

[0061] Preferably, the first standardized action is a swallowing and / or an arm raising action.

[0062] Preferably, after determining that there is human-machine resistance during mechanical assisted ventilation of a myasthenia gravis patient, the method further comprises: judging the type of human-machine resistance based on the changing trends of respiratory frequency, tidal volume of each respiratory cycle, and inspiratory time over time.

[0063] Preferably, if Figure 3 As shown, if the increase in respiratory rate is accompanied by fluctuations in the tidal volume waveform, it is determined that the ventilator has failed to effectively trigger gas delivery.

[0064] Preferably, if Figure 4 As shown, if the increase in respiratory rate is accompanied by an increase in inspiratory time, it is determined that the ventilator gas delivery is insufficient, wherein the inspiratory time can be characterized by the pressure change of the ventilator airway.

[0065] Preferably, if Figure 5 As shown, if the tidal volume is lower than the preset tidal volume threshold, the inspiratory time is shorter than the preset inspiratory time threshold and the respiratory rate increases, it is determined that the ventilator has premature exhalation triggering, wherein the inspiratory time can be characterized according to the pressure change of the ventilator airway.

[0066] Regarding the detection of patient-ventilator resistance in patients with myasthenia gravis using a ventilator, considering the special pathological characteristics of the neuromuscular junction in these patients, selecting appropriate ventilation parameters is crucial for accurately determining patient-ventilator resistance. Based on the clinical characteristics of patients with myasthenia gravis and their need for mechanical ventilation, this application provides a method for monitoring and evaluating patient-ventilator resistance and the type of patient-ventilator resistance using muscle strength, tidal volume (VT), respiratory rate (RR), and inspiratory time (Ti).

[0067] Patients with myasthenia gravis may have weak respiratory muscles, so their tidal volumes may be small or erratic. By monitoring changes in tidal volume, it is possible to identify if the patient is trying to increase their inspiratory effort but is unable to effectively trigger the ventilator to deliver air. Patients with myasthenia gravis may exhibit an increased respiratory rate during an exacerbation of their disease. If the respiratory rate is significantly higher than the set value, or if there are frequent spontaneous breathing attempts, this may indicate that the patient is struggling to overcome the limitations of the ventilator settings. Inspiratory time reflects the length of time the inspiratory phase of each breath lasts. Patients with myasthenia gravis may take longer to inhale in order to obtain enough gas due to weak respiratory muscles. If an abnormally prolonged inspiratory time is observed, or if it does not match the set I / E ratio, this may indicate that there is potential patient-ventilator resistance.

[0068] Preferably, during mechanical ventilation, data for the three ventilation parameters are collected in real time and waveforms are generated over time. The acquired waveforms of tidal volume, respiratory rate, and inspiratory time are analyzed for their changing trends, with a focus on whether these parameters exhibit regular, abnormal fluctuations or deviations from expected patterns.

[0069] Preferably, the determination method includes:

[0070] Baseline establishment: First, establish the normal range or baseline waveform for each ventilation parameter based on the patient's baseline condition and current treatment plan. This can be accomplished by collecting data during the initial stabilization period.

[0071] Real-time comparison: Compare the real-time monitored ventilation parameter waveform with the baseline waveform. If the waveform of tidal volume, respiratory rate, or inspiratory time shows a trend significantly different from the baseline, such as: too small or too large tidal volume accompanied by a significant increase in respiratory rate; respiratory rate frequently exceeds the set value accompanied by short and irregular inspiratory attempts; inspiratory time is abnormally prolonged, resulting in an imbalance between inspiration and expiration; these conditions may indicate the occurrence of patient-ventilation resistance.

[0072] Confirming human-machine resistance: When multiple parameters show abnormal trends at the same time, it can be more confidently determined that human-machine resistance exists.

[0073] Preferably, the human-machine resistance type including ineffective inspiratory triggering and premature expiratory triggering is determined by monitoring and analyzing tidal volume, respiratory rate and inspiratory time using the following method.

[0074] Ineffective triggering refers to the patient's attempt to initiate a breath but the failure to successfully trigger the ventilator to deliver air, resulting in an unresponsive respiratory effort. Preferably, ineffective triggering is confirmed by tidal volume (VT) waveform analysis and respiratory rate (RR) waveform analysis. Monitor the tidal volume waveform to observe whether there are small fluctuations in tidal volume (usually less than 20% of the set tidal volume). These small fluctuations may represent the patient's spontaneous respiratory efforts, but due to insufficient trigger sensitivity or improper ventilator settings, they fail to effectively trigger air delivery. If the respiratory rate waveform shows frequent short inspiratory attempts and the tidal volume does not increase significantly after each attempt, it indicates that ineffective triggering may exist. Further preferably, the airway pressure waveform can also be checked to look for negative pressure peaks (i.e., negative pressure generated when the patient inhales), but without subsequent positive pressure delivery. This indicates that the patient attempted to inhale but failed to trigger the ventilator.

[0075] Premature Cycling Off refers to the situation where the ventilator switches to the expiratory phase too early before the end of the inspiratory phase, resulting in the patient failing to obtain sufficient tidal volume, affecting gas exchange. Monitor the tidal volume waveform to observe whether the tidal volume is significantly lower than the set value. If the tidal volume is often lower than expected, it indicates that there may be premature expiratory triggering. Further preferably, check the airway flow rate waveform, especially at the end of the inspiratory phase, to observe whether the flow rate drops to the baseline level too early. This indicates that the ventilator switches to the expiratory phase too early, resulting in insufficient inspiratory time. Observe the airway pressure waveform, especially at the end of the inspiratory phase, to see if the pressure drops too early. If the pressure of the inspiratory phase fails to maintain for a sufficient time, it indicates premature expiratory triggering.

[0076] Preferably, the patient's respiratory rate is monitored in real time through the flow sensor or pressure sensor built into the ventilator. The respiratory rate is usually expressed in breaths per minute (breaths / minute). Under normal circumstances, the respiratory rate of MG patients should be maintained between 10-20 breaths / minute. If the respiratory rate suddenly increases to more than 25 breaths / minute and lasts for more than 30 seconds, it may indicate that the patient is struggling to overcome the limitations of the ventilator settings. The tidal volume of each respiratory cycle is monitored in real time through the flow sensor built into the ventilator. Under normal circumstances, the tidal volume of MG patients should be maintained between 400-600 mL. If the tidal volume drops significantly to below 300 mL and is accompanied by an increase in respiratory rate, it may indicate that the patient has failed to effectively trigger the ventilator to deliver air and there is a risk of ineffective inspiratory triggering.

[0077] The ventilation detection method of this application can be used to determine the presence and type of patient-ventilator resistance based on the patient's myasthenia gravis condition characteristics, their muscle strength, and the changing trends in tidal volume, respiratory rate, and inspiratory time during mechanically assisted exhalation. This method helps the ventilator promptly detect and adjust the presence of patient-ventilator resistance during mechanically assisted exhalation in patients with myasthenia gravis.

[0078] The present application also provides a ventilation detection device for myasthenia gravis patients, such as Figure 2As shown, the device includes: a muscle strength monitoring module, configured to monitor the muscle activity of the swallowing muscles and the proximal limb muscles of the myasthenia gravis patient, and obtain the muscle strength data of the myasthenia gravis patient based on the monitoring data of the swallowing muscle activity and the proximal limb muscle activity; a respiratory monitoring module, configured to detect the respiratory rate, the tidal volume of each respiratory cycle and the inspiratory time of each respiratory cycle when the myasthenia gravis patient is subjected to mechanical assisted ventilation, and obtain the change trend of the respiratory rate, the tidal volume of each respiratory cycle and the inspiratory time over time; a control module, and The muscle strength monitoring module is signal-connected to the respiratory monitoring module and is configured to obtain the muscle strength data of the myasthenia gravis patient from the muscle strength monitoring module, and obtain the respiratory rate, tidal volume of each respiratory cycle, and inspiratory time change trends of the myasthenia gravis patient from the respiratory monitoring module. When the muscle strength data of the myasthenia gravis patient is lower than the preset muscle strength threshold, and the respiratory rate, tidal volume of each respiratory cycle, and inspiratory time change trends over time are inconsistent with the change trends of the pre-stored corresponding parameters, it is determined that there is human-machine resistance during the mechanical assisted ventilation of the myasthenia gravis patient. The inspiratory time of each respiratory cycle is monitored in real time through the built-in flow sensor of the ventilator. Under normal circumstances, the inspiratory time of MG patients should be maintained between 0.8 and 1.2 seconds. If the inspiratory time is significantly extended to more than 1.5 seconds, or shortened to less than 0.6 seconds, it may indicate the risk of delayed expiratory triggering or premature expiratory triggering.

[0079] Preferably, when a patient's muscle strength data (e.g., RMS values ​​for swallowing muscles and proximal limb muscles) falls below a preset muscle strength threshold, this indicates significant impairment of the patient's neuromuscular function and a possible risk of respiratory muscle weakness. Furthermore, when the temporal trends of respiratory rate, tidal volume, and inspiratory time are inconsistent with pre-stored trends for the corresponding parameters, the possibility of ventilator resistance is further confirmed. Specifically, if a patient's muscle strength data falls below the threshold (e.g., RMS values ​​for swallowing muscles < 25 μV, RMS values ​​for proximal limb muscles < 150 μV), and the respiratory rate suddenly increases to above 25 breaths / minute, the tidal volume drops below 300 mL, and the inspiratory time prolongs to above 1.5 seconds, the system will determine that ventilator resistance is present and issue an alarm. If the patient's muscle strength data falls below the threshold, but the trends of ventilation parameters are consistent with pre-stored trends (e.g., respiratory rate remains stable at 12-16 breaths / minute, tidal volume remains stable at 450-550 mL, and inspiratory time remains stable at 1.0 second), then significant ventilator resistance is not considered, and monitoring will continue.

[0080] Ideally, when a patient's muscle strength data approaches the threshold (e.g., swallowing muscle RMS < 30 μV, proximal limb muscle RMS < 180 μV), but ventilation parameters have not yet shown significant abnormalities, healthcare providers should closely monitor the patient's condition. If a patient's muscle strength data falls below the threshold and ventilation parameters show abnormal changes, healthcare providers should immediately check the ventilator settings and take appropriate measures (e.g., adjusting trigger sensitivity, increasing PEEP, etc.). If a patient's muscle strength data falls far below the threshold (e.g., swallowing muscle RMS < 20 μV, proximal limb muscle RMS < 120 μV), and ventilation parameters are severely abnormal, healthcare providers should prepare for emergency intubation or adjust the ventilation mode. Based on the test results, healthcare providers can adjust ventilator settings, such as trigger sensitivity, inspiratory time, and ventilation mode, to improve patient-ventilator synchronization. If necessary, other clinical interventions, such as medication and psychological support, can be combined to comprehensively improve the patient's ventilation status.

[0081] The above technical solution allows for more precise identification and management of potential patient-ventilator resistance during mechanical ventilation in patients with myasthenia gravis. This solution not only relies on traditional ventilation parameters (such as respiratory rate, tidal volume, and inspiratory time), but also incorporates the patient's muscle strength data to provide a more comprehensive assessment. Taking into account the unique pathological characteristics of MG patients and their need for mechanical ventilation, this application provides a safe and effective ventilation management approach to ensure optimal respiratory support for patients with myasthenia gravis.

[0082] According to a preferred embodiment, the control module is further configured to: if the increase in respiratory rate is accompanied by fluctuations in the tidal volume waveform, determine that there is a failure to effectively trigger the ventilator to deliver air.

[0083] According to a preferred embodiment, the control module is further configured to determine that the ventilator gas delivery is insufficient if the increase in respiratory rate is accompanied by an increase in inspiratory time.

[0084] The present application provides a respiratory assistance system for patients with acquired myasthenia. Preferably, the respiratory assistance system includes the aforementioned ventilation detection device. Figure 8 As shown, the system also includes: a chest assist component 100, including a first actuator 110 configured for external mechanical contact with the patient's chest and a first control unit 120 connected to the first actuator 110 by signal; an abdominal assist component 200, including a second actuator 210 configured for external mechanical contact with the patient's abdomen and a second control unit 220 connected to the second actuator 210 by signal; wherein the first control unit 120 and the second control unit 220 are configured to cooperate with each other based on the patient's muscle strength data to respectively control the first actuator 110 and the second actuator 210 to provide mechanical stimulation and / or electrical stimulation to the patient's chest and abdomen to assist the patient's breathing.

[0085] Based on this system, first define the following variables:

[0086] : chest muscle strength as a function of time;

[0087] : Abdominal muscle strength as a function of time;

[0088] : the intensity of the stimulus applied by the chest actuator as a function of time;

[0089] : the intensity of the stimulus applied by the abdominal actuator as a function of time;

[0090] : Patient's respiratory function index as a function of time.

[0091] The system is described by the following mathematical model:

[0092] The control equation of the chest power assist component is:

[0093] ;

[0094] The control equation of the abdominal power assist component is:

[0095] ;

[0096] The respiratory function improvement equation is:

[0097] .

[0098] in, 、 and It is a nonlinear function determined by clinical data and machine learning algorithms.

[0099] According to a specific embodiment, the following linear model is used for processing:

[0100] ;

[0101] ;

[0102] .

[0103] in, 、 is the control gain coefficient, 、 are the maximum muscle strength of the corresponding chest muscles and abdominal muscles of healthy people, 、 、 、 is the weight coefficient.

[0104] Specifically, clinical data samples of 10 patients are provided in Table 1 below.

[0105] Table 1

[0106]

[0107] Table 1 lists the clinical data of the 10 patients, including their serial number, age, gender, muscle weakness, chest muscle strength, abdominal muscle strength, chest stimulation intensity, abdominal stimulation intensity, tidal volume, respiratory rate, and blood oxygen saturation. Chest muscle strength, abdominal muscle strength, chest stimulation intensity, and abdominal stimulation intensity are standardized scores ranging from 0 to 100. This can be determined by establishing a mapping of chest muscle strength to chest stimulation intensity. This can be determined by establishing a mapping of abdominal muscle strength to abdominal stimulation intensity. This can be determined by establishing a mapping from (chest muscle strength, abdominal muscle strength, chest stimulation intensity, abdominal stimulation intensity) to (tidal volume, respiratory rate, blood oxygen saturation) respectively.

[0108] Preferably, if Figure 8 As shown, the first actuator 110 comprises a chest strap 111 with adjustable tightness that can be worn around the chest, a first electrode 112 positioned on the strap 111 for stimulating the pectoralis major muscle, a second electrode 113 positioned on the strap 111 for stimulating the intercostal muscles, and first inflatable mechanical airbags 114 positioned on either side of the strap 111 corresponding to the chest. Specifically, the strap 111 is constructed from an elastic fabric, such as a nylon and spandex blend, and features an elastic buckle system. The strap has a chest circumference range of 70 to 130 cm. The first electrode 112 is a surface electrode made of a flexible conductive material, such as conductive silicone. It is oval in shape, with a major axis of 8 cm and a minor axis of 5 cm. It is positioned on the inside of the strap 111, corresponding to the pectoralis major muscle, and is connected to the first control unit 120 via a flexible printed circuit. The second electrode 113 is a strip-shaped electrode made of a flexible conductive material, approximately 15 cm long and 2 cm wide. It is positioned on the inside of the strap 111, aligned along the ribs, and connected to the first control unit 120 via a flexible printed circuit. The first mechanical airbag 114 is made of medical-grade silicone and is circular, approximately 10 cm in diameter. Its thickness can expand from 0.5 cm to 3 cm. It is embedded in the chest strap structure on both sides of the chest strap 111 and connected to a micro-electric pump via a flexible hose for inflation and deflation. A pressure sensor can also be integrated into the first mechanical airbag for real-time pressure monitoring.

[0109] Preferably, if Figure 8As shown, the second actuator 210 comprises an adjustable abdominal band 211 that can be worn around the abdominal cavity; a third electrode 212 positioned on the band 211 for stimulating the rectus abdominis muscles; a fourth electrode 213 positioned on the band 211 for stimulating the external oblique abdominal muscles; and an inflatable second mechanical airbag 214 positioned on the band 211 at a location corresponding to the anterior abdomen. The band 211 is made of breathable elastic fabric, is approximately 20 cm wide, and has an abdominal circumference range of approximately 60 to 120 cm. The third electrode 212 is a rectangular surface electrode, 10 cm long and 5 cm wide, positioned on the inner side of the band 211 at a location corresponding to the rectus abdominis muscles and connected to the second control unit 220 via a flexible printed circuit. The fourth electrode 213 is a fan-shaped surface electrode, approximately 15 cm long and 5 cm wide at its widest point. It is positioned on the inner side of the band 211 at a location corresponding to the external oblique abdominal muscles and connected to the second control unit 220 via a flexible printed circuit. The second mechanical airbag 214 is made of medical-grade silicone and is elliptical, with a major axis of 20 cm and a minor axis of 15 cm. Its thickness can be expanded from 1 cm to 5 cm. It is embedded in the abdominal band structure at the corresponding position on the anterior abdomen. It is connected to a micro-electric pump via a flexible hose for inflation and deflation. The first mechanical airbag and the second mechanical airbag preferably use the same electric pump. A pressure sensor can also be integrated into the second mechanical airbag for real-time pressure monitoring.

[0110] The first control unit 120 and the second control unit 220 can adopt a low-power microcontroller (such as ARM Cortex-M4), which has an electrical stimulation module, a programmable current source with an output range of 0~100mA and an adjustable frequency (1~100Hz); an airbag control module, a PWM-controlled electric pump drive circuit; a communication interface: a Bluetooth low energy (BLE) module for communicating with the main controller; and a sensor interface: which can be used to connect an electromyography (EMG) sensor and a pressure sensor.

[0111] Preferably, if Figure 9 and Figure 10 As shown, the first actuator 110 and the second actuator 210 are configured to assist the patient's breathing in the following manner:

[0112] Inhalation phase: The first electrode 112 activates the pectoralis major muscle to help lift the thorax. The second electrode 113 activates the intercostal muscles to help expand the chest cavity. The third electrode 212 and the fourth electrode 213 slightly activate the rectus abdominis and external oblique muscles, respectively, to assist in contracting the abdominal muscles and lowering the diaphragm. The first mechanical airbags 114 on both sides of the chest cavity are inflated to assist in outward expansion of the chest cavity. The mechanical airbags on the front of the abdomen are contracted to assist in lowering the diaphragm. The electrical stimulation and mechanical assistance work together to increase the volume of the chest cavity, reduce the pressure in the chest cavity, and promote the entry of air into the lungs. Figure 9 shown.

[0113] Exhalation phase: The stimulation intensity of the first electrode 112 and the second electrode 113 is reduced, allowing the chest to fall back naturally. The stimulation intensity of the third electrode 212 and the fourth electrode 213 is increased to assist the contraction of the abdominal muscles, squeeze the abdominal cavity, and push the diaphragm up. The first mechanical airbags 114 on both sides of the chest cavity begin to deflate, allowing the chest cavity to retract. The second mechanical airbag 214 on the front of the abdomen is inflated to assist in squeezing the abdominal cavity. The electrical stimulation and mechanical assistance work together to increase the pressure in the chest cavity and push the gas out of the lungs. Figure 10 shown.

[0114] Through this precisely coordinated approach, the device is able to mimic the respiratory muscle movement patterns of healthy individuals, providing comprehensive respiratory assistance to patients with myasthenia gravis. This approach not only improves patients' respiratory function but also may help maintain and train these muscles, potentially positively impacting their long-term recovery.

[0115] According to a specific embodiment, the electrode stimulation parameters during the inspiration phase and the expiration phase are shown in Table 2 and Table 3 below:

[0116] Table 2: Inspiratory phase (duration 1-2 seconds)

[0117]

[0118] Table 3: Exhalation phase (duration 2-3 seconds)

[0119]

[0120] Preferably, the maximum capacity of the first mechanical airbag is 500ml per side, with an inflation rate of 250-350ml / s during inspiration and a deflation rate of 150-250ml / s during expiration. The maximum capacity of the second mechanical airbag is 1000ml, with an inflation rate of 300-400ml / s during inspiration and a deflation rate of 200-300ml / s during expiration.

[0121] Preferably, the electrical stimulation intensity of the electrode can be adjusted according to the patient's blood oxygen saturation.

[0122] ,

[0123] in, is the basic intensity of electrical stimulation, such as listed in Table 2 and Table 3, It is the electrode electrical stimulation intensity adjusted according to the patient's blood oxygen saturation. It is the current blood oxygen saturation percentage monitored in real time; is the adjustment factor, with a typical value of 0.02; Indicates the target blood sample saturation value, which is a constant and is usually set to 95~98.

[0124] According to a specific embodiment, the stimulation intensity of the first electrode, the second electrode, the third electrode and the fourth electrode is set according to the patient's detected muscle strength, blood oxygen saturation, respiratory rate and tidal volume.

[0125] ;

[0126] in, Indicates the stimulus intensity, Indicates the standardized muscle strength test value, ranging from 0 to 100. Indicates blood oxygen saturation, ranging from 0 to 100. Indicates respiratory rate, ranging from 12 to 20 times per minute. Indicates tidal volume, usually ranging from 4 to 8 ml / kg body weight. 、 、 、 It is a parameter set according to the patient's personalized clinical data measurement value. According to a specific embodiment, according to the clinical data measurement value of the test patient, for the first electrode, 、 、 、 are set to 0.3, 0.3, 0.2, and 0.2 respectively; for the second electrode, 、 、 、 are set to 0.3, 0.25, 0.25, and 0.2 respectively; for the third electrode, 、 、 、 are set to 0.2, 0.3, 0.22, and 0.28 respectively; for the fourth electrode, 、 、 、 Set to 0.24, 0.28, 0.22, and 0.26 respectively.

[0127] Preferably, the capacities of the first mechanical airbag 114 and the second mechanical airbag 214 can be adjusted according to the patient's vital capacity VC. The maximum capacity of the first mechanical airbag = 0.15×VC; the maximum capacity of the second mechanical airbag = 0.3×VC; where VC is the patient's vital capacity (ml).

[0128] Preferably, the upper limit of the electrical stimulation intensity of the electrode is 50 mA. The maximum assisted respiratory rate is set to 20 times / minute. An alarm is triggered when it is lower than 90% or no effective breathing is detected for 30 consecutive seconds.

[0129] According to a preferred embodiment, the respiratory assistance system for patients with acquired myasthenia further includes a first monitoring unit for monitoring the patient's thoracic volume, a second monitoring unit for monitoring the patient's abdominal volume, a third monitoring unit for collecting the patient's thoracic pressure gradient ΔPt, and a fourth monitoring unit for collecting the patient's abdominal pressure gradient ΔPa. The first control unit 120 and the second control unit 220 are configured to adjust the real-time electrical stimulation intensity of the first electrode, the second electrode, the third electrode, and the fourth electrode based on changes in the patient's thoracic volume collected by the first monitoring unit, changes in the patient's abdominal volume collected by the second monitoring unit, changes in the patient's thoracic pressure collected by the third monitoring unit, and changes in the patient's abdominal pressure collected by the fourth monitoring unit.

[0130] Specifically, the first monitoring unit is a chest circumference measurement resistance belt integrated into the chest strap 111, which can measure the patient's chest circumference changes in real time through resistance changes, thereby calculating the patient's chest cavity volume changes. The second monitoring unit is an abdominal circumference measurement resistance belt integrated into the abdominal belt 211, which can measure the patient's abdominal circumference changes in real time through resistance changes, thereby calculating the patient's abdominal cavity volume changes. The third monitoring unit can be an intrathoracic pressure monitor with multiple pressure measuring points to monitor different positions of the chest cavity. The fourth monitoring unit can be an intra-abdominal pressure monitor with multiple pressure measuring points to monitor intra-abdominal pressure at different positions.

[0131] Preferably, the first monitoring unit, the second monitoring unit, the third monitoring unit, and the fourth monitoring unit are respectively connected to the first control unit 120 and the second control unit 220 by signal. Specifically, the first monitoring unit, the second monitoring unit, the third monitoring unit, and the fourth monitoring unit are respectively connected to the first control unit 120 and the second control unit 220 by signal via a wireless communication module. The wireless communication module supports at least one communication mode of GPRS, 3G, 4G, 5G, Wi-Fi, ZIGBEE, and LoRa.

[0132] Specifically, the first control unit 120 and the second control unit 220 are configured to calculate the rate of change of the patient's thoracic volume ΔVt based on the patient's thoracic volume collected by the first monitoring unit, calculate the rate of change of the patient's abdominal volume ΔVa based on the patient's abdominal volume collected by the second monitoring unit, and calculate the volume phase difference θv according to the patient's thoracic volume that changes with time t and the patient's abdominal volume that changes with time t.

[0133] 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 dynamics synchronization index based on the patient's thoracic volume change rate ΔVt, abdominal volume change rate ΔVa, volume phase difference θv, thoracic pressure gradient ΔPt, and abdominal pressure gradient ΔPa. Among them, the chest and abdomen respiratory dynamics synchronization index Defined as:

[0134] ,

[0135] in, is the volume coordination coefficient, is the pressure balance factor, is the volume-pressure coupling index, is the phase compensation coefficient, is the kinetic efficiency ratio, is the normalized kinetic efficiency ratio.

[0136] Volume coordination coefficient The calculation formula is:

[0137] .

[0138] Pressure balance factor The calculation formula is:

[0139] .

[0140] Volume-pressure coupling index The calculation formula is:

[0141] .

[0142] Phase compensation coefficient The calculation formula is:

[0143] .

[0144] Dynamic efficiency ratio The calculation formula is:

[0145] .

[0146] Normalized kinetic efficiency ratio for:

[0147] .

[0148] It can be further normalized to ensure that its value is between 0 and 100.

[0149] The normalization formula is:

[0150] .

[0151] Among them, the volume coordination coefficient can evaluate the matching degree of chest and abdominal volume changes. It introduces the cosine function correction of the phase difference, and the numerical range is between 0 and 1. The pressure balance factor reflects the balance of the chest and abdominal pressure gradient, and uses an exponential function to ensure sensitivity. The smaller the pressure difference, the closer the coefficient is to 1. The volume-pressure coupling index is used to evaluate the matching degree between volume changes and pressure changes. The phase compensation coefficient specifically deals with the influence of phase difference, and reaches a maximum value of 1 when the phase difference is 0. The dynamic efficiency ratio can evaluate the efficiency of volume changes and pressure changes, and is normalized using the Sigmoid function to avoid the influence of outliers. By calculating Respiratory coordination can be quantitatively evaluated. This parameter can be used to facilitate long-term monitoring and comparison of patients' respiratory stability and recovery status, and can guide respiratory training and treatment.

[0152] According to a specific embodiment, the respiratory assistance system for patients with acquired myasthenia is configured to perform An adaptive electrical stimulation control scheme was developed. First, the basic parameters for EMG signal acquisition were set: a sampling frequency of 2000 Hz, a resolution of 16 bits, a signal-to-noise ratio greater than 60 dB, and a common-mode rejection ratio greater than 100 dB. The first electrode was placed between the 3rd and 5th intercostals, the second electrode between the 3rd and 7th intercostals, the third electrode 5 cm above and below the umbilicus, and the fourth electrode below the 8th and 9th intercostals. Basic electrode stimulation parameters were pulse width of 200–400 μs, frequency of 20–50 Hz, intensity of 10–40 mA, rise time of 0.5–1 s, duration of 1–2 s, and interstimulus interval of 2–4 s.

[0153] 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 has 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.

[0154] Furthermore, for example, the training set listed in Table 4 below can be used to train the An intelligent regulation mechanism for adjusting electrode stimulation parameters. This regulation mechanism can be customized and adjusted according to the patient's specific physiological state in clinical applications. The following training set is used as the basis An example of feedback regulation. According to a specific embodiment, The stimulation intensity and frequency of each of the first electrode, the second electrode, the third electrode and the fourth electrode are adjusted in a manner that is greater than or equal to 80, preferably greater than or equal to 90.

[0155] Table 4

[0156]

[0157] Table 4 lists examples of the training A training set of technical solutions for adjusting electrode stimulation parameters, including The value and correspondence of By using the training set to train and execute the above feedback adjustment mechanism, it is possible to objectively reflect the patient's respiratory coordination. Parameters are used to evaluate the patient's respiratory status in the long term and adjust the electrode stimulation parameters of the respiratory assistance system to achieve better assisted breathing effects.

[0158] Preferably, the system also includes: a first sensor 131, configured to monitor the muscle activity of the swallowing muscles; a second sensor 133, configured to monitor the muscle activity of the proximal limbs; 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.

[0159] Preferably, the data processing unit 140 is configured to collect muscle strength data of a first time period when the patient performs the first standardized action through the first sensor 131 and / or the second sensor 133 as reference muscle strength data, and to collect muscle strength data of a second time period of a preset time period after the patient performs the first standardized action through the first sensor 131 and / or the second sensor 133 to analyze muscle strength fluctuations.

[0160] Preferably, the first standardized action includes but is not limited to swallowing action and / or arm raising action, the proximal end of the limb is the patient's arm, the second sensor 133 is configured to detect the motion acceleration of the patient's arm and the equivalent mass of the patient's arm, and the data processing unit 140 calculates the muscle strength data of the user's arm during muscle activity based on the motion acceleration of the patient's arm and the equivalent mass of the patient's arm detected by the second sensor 133.

[0161] Preferably, the system further comprises a sensing component configured to measure the motion of the patient's respiratory muscles to collect motion information, and determine the motion state of the patient's respiratory muscles based on the collected motion information.

[0162] Preferably, the first control unit 120 and the second control unit 220 are configured to respectively control the first actuator 110 and the second actuator 210 based on the patient's motion state to provide mechanical stimulation and / or electrical stimulation to the patient's chest and abdomen that matches the motion state.

[0163] Preferably, the electrodes are configured to adjust the intensity of the electrical stimulation in turn based on the detected muscle strength fluctuation rate and the real-time detected blood oxygen saturation. According to another specific embodiment, the system is configured to, when the detected blood oxygen saturation is lower than 95, give priority to a control method for adjusting the electrode electrical stimulation intensity based on the real-time detected blood oxygen saturation. When the detected blood sample saturation is higher than 95, give priority to a control method for adjusting the electrode electrical stimulation intensity based on the detected muscle strength fluctuation rate. This control strategy cleverly balances the patient's immediate safety needs (by monitoring blood oxygen saturation) and long-term treatment effects (by monitoring muscle strength). This balance is particularly important for the management of patients with chronic diseases. By adjusting treatment parameters in real time, the system can provide tailored treatment based on the real-time condition of each patient. The system can not only respond to acute situations (such as a sudden drop in blood oxygen), but also prevent potential problems (such as excessive fatigue) by monitoring muscle status. This preventive approach may greatly reduce the risk of complications.

[0164] Long-term use of this system will accumulate a wealth of valuable data that can be used to further optimize treatment strategies and may even provide new insights into the study of related diseases. By optimizing stimulation intensity and frequency, the system may significantly improve patient comfort and quality of daily life, which is crucial for patients who use respiratory assistance devices for a long time. This control strategy demonstrates how to integrate multiple physiological parameters into an intelligent decision-making system, providing inspiration for the design of other medical devices.

[0165] Preferably, the first actuator 110 is configured to apply vibrations along the patient's intercostal space to enhance inspiration. Preferably, the first actuator 110 is configured to apply pressure to the pectoralis major and pectoralis minor muscles to enhance exhalation. Preferably, the second actuator 210 is configured to apply pressure or vibrations to the external and internal oblique abdominal muscles on the side of the patient's abdomen. Preferably, the second actuator 210 is configured to apply circumferential pressure or vibrations to the transverse abdominal muscle at the patient's waist.

[0166] Preferably, for the intercostal muscles, a small vibration device can be used to stimulate along the intercostal spaces. This helps to enhance the inhalation process. For the pectoralis major and minor muscles, a larger vibration pad or pressure pad can be used to cover the entire front of the chest. This helps to enhance the exhalation process. For the rectus abdominis, a belt vibrator or pressure belt can be used to stimulate from below the sternum to above the pubic bone. For the external and internal obliques, a diagonal stimulation belt can be used on the side of the abdomen. Stimulation of the transverse abdominis can be achieved by using a wrap-around stimulation belt around the waist.

[0167] Ideally, design a pattern that alternates chest and abdominal stimulation to mimic the rhythm of natural breathing. For example, stimulate the chest muscles for 1-2 seconds (to mimic inhalation), then stimulate the abdominal muscles for 2-3 seconds (to mimic exhalation). Start with a low intensity and frequency, gradually increasing to a level that the patient can comfortably tolerate. The frequency can start at 12-20 breaths per minute to mimic a normal breathing rate.

[0168] The system can analyze muscle strength data in real time to predict the risk of acquired muscle weakness and stimulate the patient's diaphragm, including vibration and squeezing, based on the test results. Figure 6 As shown, the present application provides a life support system for acquired muscle weakness. The system includes: a first sensor 131, configured to monitor the muscle activity of the swallowing muscles; a second sensor 133, configured to monitor the muscle activity of the proximal limbs; 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; a breathing assistance unit, configured to assist the patient's breathing by vibration, compression, electric shock and / or infrared. The data processing unit 140 is configured to collect the muscle strength data of the first time period when the patient performs the first standardized action through the first sensor 131 and / or the second sensor 133 as reference muscle strength data, collect the muscle strength data of the second time period of the preset time period after the patient performs the first standardized action through the first sensor 131 and / or the second sensor 133 to analyze the muscle strength fluctuation, and the breathing assistance unit stimulates the patient's diaphragm according to the muscle strength fluctuation to maintain the patient's diaphragm strength.

[0169] Preferably, the first sensor 131 and the second sensor 133 are configured in a wearable device. A 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 from the first sensor 131 and the second sensor 133 by time. Preferably, the system further includes a user interface 150 for displaying muscle weakness status and risk assessment results. Preferably, the wearable device further includes at least one electromyography (EMG) sensor 134 for monitoring and analyzing comprehensive muscle activity. The sensors transmit 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 from 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 a web interface 152 for patient access and interaction; a personalized risk assessment and management plan module 153 tailored to the patient's individual needs and conditions; and educational resources and support materials 154 designed to provide patients with knowledge and self-management strategies.

[0170] The interaction between the data processing unit 140 and the wearable device 130 is optimized as follows: data transmission protocol and communication protocol to ensure real-time transmission and real-time processing of muscle strength data; secure data encryption and authentication mechanism to protect patient privacy and data integrity; data synchronization and integration function with the cloud storage server 160 for data backup and remote access.

[0171] Preferably, the system further includes an enhanced connection to a cloud storage server 160, including: strong data security measures such as access control, data encryption, and regular security audits; scalable storage capacity to accommodate the growth of patient data volume; and data analysis tools for extracting valuable insights from accumulated patient data to promote system improvement and myasthenia gravis research.

[0172] The specific algorithm for evaluating myasthenia gravis involved in this application is as follows Figure 7 The specific instructions are as follows:

[0173] Symbol Definition

[0174] : No. Time period

[0175] : No. Muscle strength data for each period

[0176] : Standardized muscle strength data set for the first period

[0177] : No. Periods and Middle Muscle strength data associated with each data point

[0178] Judgment process

[0179] 1. Standardize the first period data

[0180] The muscle strength data of the first period Perform standardization to obtain a standardized set :

[0181] ,

[0182] in, yes The mean of is the standard deviation.

[0183] 2. Calculate muscle strength fluctuations

[0184] For each subsequent period (i > 1), calculate its muscle strength data and The difference between the corresponding data in:

[0185] .

[0186] 3. Determine acquired muscle weakness

[0187] if Exceeding the preset threshold , it is judged that acquired muscle weakness exists during this period.

[0188] The choice of threshold can be adjusted based on clinical experience or the patient's personal historical data. At the same time, data noise, individual differences among patients, and joint analysis of multi-muscle group data should be considered to optimize judgment accuracy.

[0189] Specifically, the clinical test data of several patients are listed in Table 5 below.

[0190] Table 5

[0191]

[0192] Table 5 lists the clinical test data of the two patients, including the corresponding swallowing muscle EMG data detected at time points T0 and T1 when the two patients (numbered 1 and 2) performed swallowing or hand raising movements, arm acceleration data, measured arm equivalent mass, calculated muscle strength data, and respiratory rate and tidal volume data at the corresponding time points. Swallowing muscle strength is approximately linearly correlated with EMG signal strength and can be calculated using the following formula:

[0193] ;

[0194] in, is the calculated swallowing muscle strength (unit: N), is the proportionality factor (unit: N / μV), which needs to be determined through calibration. is the measured electromyographic signal intensity (unit: μV).

[0195] According to one embodiment, swallowing muscle strength is measured using a surface electromyography (EMG) sensor, or EMG sensor 134, which records the amplitude and frequency of the myoelectric signals during swallowing. The measurement parameters for arm muscle strength are arm acceleration a and arm equivalent mass m. Muscle strength F = m × a. Here, F is muscle strength (Newtons, N), m is arm equivalent mass (kilograms, kg), and a is arm acceleration (meters per second squared, m / s²).

[0196] The test process is as follows: First period (reference data collection): perform standardized actions (such as swallowing or raising the arm) and record muscle strength data as a reference. Second period (fluctuation analysis): perform the standardized action again after a preset time period, record muscle strength data, compare it with the reference data, and analyze strength fluctuations. Strength fluctuations are characterized by the calculated strength fluctuation rate R. R=(F 第二时段 -F 第一时段 ) / F 第一时段 × 100%. Where R represents the power fluctuation rate, F 第二时段 Indicates the muscle strength detected in the second period, F 第一时段 Indicates the muscle strength detected in the first period.

[0197] The relationship between electrical stimulation intensity and muscle strength fluctuation rate is: I 调整后 = I 基础 ×(1-k×R); where I 基础 is the basic intensity of electrical stimulation, such as listed in Table 2 and Table 3, I 调整后 It is the electrode electrical stimulation intensity adjusted according to the patient's power fluctuation rate. R represents the power fluctuation rate, and k is the adjustment coefficient, with a value range of 0.1~0.3.

[0198] Preferably, the second actuator 210 is an abdominal pressure device that receives information about fluctuations in the patient's swallowing muscles and proximal upper limb strength from the data processing unit 140 and applies bionic breathing-like compressions to the patient's abdomen to maintain diaphragmatic and lung mobility. Preferably, the compression pattern and force are also configured in conjunction with the patient's intra-abdominal pressure. This pressure can be provided by an intra-abdominal pressure detection device.

[0199] For example, during muscle strength fluctuation monitoring, the system detected a 15% decrease in the patient's swallowing muscle strength over 10 minutes (from an initial 100N to 85N). Simultaneously, upper limb proximal muscle strength decreased by 20% over 15 minutes (from an initial 150N to 120N). Based on this data, the abdominal compression device activated and began performing bionic breathing-like compressions on the patient's abdomen. The initial compression rate was set to 12 compressions per minute, mimicking a normal breathing rate. The initial compression depth was set to 2cm to simulate mild breathing. The intra-abdominal pressure monitoring device measured the patient's baseline intra-abdominal pressure at 8 mmHg. The system is programmed to maintain a normal intra-abdominal pressure fluctuation range of 6-10 mmHg during normal breathing. If the system detects a drop in intra-abdominal pressure to 5 mmHg, it increases the compression force to a depth of 3 cm and the frequency to 14 compressions per minute. If the intra-abdominal pressure rises to 12 mmHg, the system decreases the compression force to a depth of 1.5 cm and the frequency to 10 compressions per minute. The system evaluates muscle strength and intra-abdominal pressure every 5 minutes and dynamically adjusts compression parameters. If muscle strength recovers to more than 95% of the initial value within 15 minutes (swallowing muscle strength recovers to 95N, proximal upper limb muscle strength recovers to 142.5N), the system will gradually reduce compression intensity and frequency. The system sets the maximum compression depth to no more than 4cm to prevent pressure on internal organs. The maximum compression frequency is limited to 20 times per minute to avoid hyperventilation. Based on the patient's height, weight and lung capacity, the system can personalize the initial parameters. 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. Through this precise, personalized dynamic adjustment, the respiratory assistance system can more effectively maintain the patient's diaphragmatic function and respiratory capacity while reducing the risk of complications.

[0200] According to one embodiment, the data processing unit 140 is configured to control the actuator to perform variable frequency vibration with a non-constant frequency within the operating frequency range of 5 Hz to 1000 Hz during a given time interval to assist in maintaining the patient's diaphragm muscle strength. For example, the actuator specifications are: weight: 50 grams, size: 5 cm × 3 cm × 1 cm, maximum output power: 2 watts. Vibration frequency range: minimum frequency: 5 Hz, maximum frequency: 1000 Hz, frequency adjustment accuracy: 1 Hz. Vibration modes include: low frequency band (5 Hz ~ 50 Hz): starting frequency: 10 Hz, lasting 5 seconds, gradually increasing to 30 Hz, lasting 10 seconds, and decreasing to 20 Hz, lasting 5 seconds. Medium frequency band (51 Hz ~ 500 Hz): starting from 100 Hz, increasing by 50 Hz every 2 seconds until reaching 300 Hz, maintaining at 300 Hz for 5 seconds, and then decreasing by 100 Hz every 3 seconds until returning to 100 Hz. High-frequency band (501Hz-1000Hz): Start at 600Hz, rapidly increase to 900Hz (within 1 second), maintain at 900Hz for 2 seconds, then slowly decrease to 700Hz (within 3 seconds), repeating this cycle three times. Interval settings: Total treatment time: 15 minutes, low-frequency band: 5 minutes, medium-frequency band: 7 minutes, high-frequency band: 3 minutes. The system preferably evaluates the patient's diaphragm electromyography (EMG) signal every 30 seconds. If the EMG signal strength decreases by more than 10%, the system increases the vibration intensity and duration. For example, if a decrease in the EMG signal is detected in the medium-frequency band, the system may increase the duration of 300Hz from 5 seconds to 8 seconds. The system can adjust the vibration intensity based on the patient's weight and muscle condition. For example, for a 60kg patient, the initial vibration intensity might be set to 1.5 watts, and for an 80kg patient, the initial vibration intensity might be set to 1.8 watts. Continuous high-frequency vibration (>800Hz) should not exceed 30 seconds. After every 5 minutes of treatment, the system is forced to pause for 30 seconds to prevent muscle fatigue. If the patient reports discomfort (via button or voice command), the system immediately reduces the frequency by 50%. If the discomfort persists, the system gradually reduces the frequency. Through this precise, dynamic vibration stimulation, the respiratory assistance system can effectively maintain and stimulate the patient's diaphragm muscle strength while providing a personalized and safe treatment plan. This approach may help prevent respiratory dysfunction in patients with acquired myasthenia gravis and improve their quality of life.

[0201] Preferably, the second actuator 210 further comprises a belt of at least two vibration modules, which are externally applied to the user's abdominal area to stimulate the diaphragm, thereby enhancing lung function.

[0202] Preferably, the first sensor 131 is a flexible sensor attached to the first area of ​​the skin surface of the patient's swallowing muscle, which obtains a first signal reflecting the change state of the strain of the first area and a second signal representing the change state of the curvature of the first area according to the deformation of the first area.

[0203] Preferably, the first sensor 131 includes a strain sensing unit and an optical sensing unit. The strain sensing unit generates a first signal according to the deformation of the first area. The optical sensing unit generates a second signal according to the deformation of the first area.

[0204] Preferably, the data processing unit 140 obtains muscle strength data of the patient's swallowing muscles according to the first signal and the second signal collected by the first sensor 131 .

[0205] Preferably, the sensor substrate is made of a flexible material (such as polydimethylsiloxane (PDMS)) to ensure skin adhesion and prevent interference with swallowing. The strain sensing unit is made of nanomaterials such as graphene or carbon nanotubes, offering high sensitivity and excellent flexibility. The optical sensing unit can utilize fiber Bragg grating (FBG) technology to accurately measure even minute changes in curvature. The sensor surface is coated with a biocompatible material to reduce skin irritation. The sensor adheres tightly to the surface of the swallowing muscle, accurately capturing muscle movement. The flexible design does not interfere with the patient's normal swallowing, enhancing comfort during long-term monitoring. The strain sensing unit measures muscle deformation through changes in resistance and converts this into an electrical signal. The optical sensing unit measures curvature by using phase changes in light waves in the optical fiber and converts this into an optical signal. A high-precision analog-to-digital converter (ADC) is used to convert the analog signal into a digital signal. A low-noise amplification circuit is designed to improve signal quality. This approach allows for simultaneous acquisition of strain and curvature information, comprehensively reflecting the dynamic state of the swallowing muscle. A high sampling rate (e.g., 1000 Hz) enables the capture of rapid swallowing movements. Digital filtering algorithms (such as Butterworth filters) are used to remove environmental noise and baseline drift. Machine learning algorithms (such as support vector machines (SVMs) or deep neural networks) are applied to extract features from the raw signals. Strain and curvature data are converted into muscle force data and a mathematical model is established to enable real-time data processing and provide immediate feedback. In this way, swallowing muscle strength can be accurately estimated, providing an objective basis for clinical assessment.

[0206] Preferably, the data processing unit 140 further includes: a data receiving module 141 for receiving muscle activity detection data from the first sensor 131 and the second sensor 133; a data analysis module 143 for calculating muscle strength data based on the received muscle activity detection data, analyzing the fluctuation of 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 results and suggestions.

[0207] Preferably, the data processing unit 140 is connected to the wearable device, and is used to receive muscle strength data from the first sensor 131 and / or the second sensor 133, and analyze the data to predict the risk of acquired muscle weakness; wherein the muscle strength data is divided according to multiple different time periods, specifically: muscle strength data of the first time period, that is, reference muscle strength data collected when the patient performs the first standardized action; muscle strength data of the second time period, that is, muscle strength data collected after a preset time period after the patient performs the first standardized action, and used to predict muscle strength fluctuations.

[0208] In this way, the wearable device integrates a first sensor 131 and a second sensor 133 for collecting muscle strength data. The data processing unit 140 is wirelessly connected to the wearable device to receive and analyze the muscle strength data. During the first period, the patient performs a first standardized action (such as a grip strength test or a swallowing test) to collect reference muscle strength data. During the second period, the patient performs the same action again within a preset time period (such as 30 minutes) after performing the first standardized action to collect muscle strength data for prediction. The data processing unit 140 first performs data preprocessing, filtering and denoising the raw sensor data. It then performs feature extraction, extracting key features from the processed data, such as peak force, duration, and decay rate. It then performs data comparison, comparing the muscle strength data from the first and second periods, calculating the percentage change in force, and finally performing a risk assessment. Based on the data comparison results, a machine learning algorithm (such as a support vector machine or random forest) is used to predict the risk of acquired myasthenia gravis and output a stimulation pattern according to a preset model, activating the corresponding respiratory assistance device. Stimulation modes include compression, vibration, electric shock, or infrared mode selection, as well as specific setting parameters for each mode. This technical solution, by comparing changes in muscle strength over a short period of time, can identify potential risks of acquired myasthenia gravis early, buying valuable time for clinical intervention. Using a wearable device 130 for testing reduces the discomfort associated with traditional examination methods and improves patient compliance. This non-invasive wearable device testing enables 24-hour, uninterrupted monitoring, providing a comprehensive understanding of a patient's muscle strength changes. Taking into account individual differences, the system can perform customized risk assessments based on the patient's individual baseline data. Data can be transmitted to medical institutions in real time, enabling doctors to remotely monitor the patient's condition and promptly adjust treatment plans. This technical solution is suitable for data accumulation and analysis. Long-term data collection facilitates in-depth research into the pathogenesis and influencing factors of acquired myasthenia gravis. Through standardized data collection and analysis processes, subjective judgment errors are reduced, improving the objectivity and accuracy of diagnosis. Early warning and timely intervention can reduce the occurrence of serious complications, thereby reducing overall medical costs. This acquired myasthenia gravis risk prediction system and respiratory / life support system, based on the wearable device 130 and data analysis, can significantly improve the prevention, diagnosis, and rehabilitation treatment of acquired myasthenia gravis, providing better medical care for patients.

[0209] Preferably, the wearable device further includes a communication module 132 for wirelessly transmitting the muscle strength data collected by the first sensor 131 and the second sensor 133 to the data processing unit 140 .

[0210] Preferably, the preset time period is adjusted based on the patient's muscle strength data of the first period, historical muscle strength data and / or medical advice.

[0211] Preferably, the data processing unit 140 further communicates with a cloud storage server 160 to transmit the analyzed muscle strength fluctuation data and risk assessment results to the cloud storage server 160 for storage; receive historical data from the cloud storage server 160 to assist in the analysis and judgment of current data and improve the accuracy of prediction; and allow authorized medical personnel to access the patient's historical and current muscle strength data for remote diagnosis and treatment planning.

[0212] 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.

[0213] Preferably, the user interface 150 is a touch screen for providing interactive operations, guiding the patient to complete standardized movements through the user interface 150, and displaying muscle strength data and evaluation results in real time.

[0214] Preferably, the data receiving module 141 and the data analysis module 143 are embedded software modules that run on the 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 cache module 142 for temporarily storing real-time muscle strength data received from the wearable device to ensure that data is not lost when the wireless network is unstable or interrupted. Preferably, the data analysis module 143 is configured with a machine learning algorithm to learn and adapt to the muscle strength fluctuation characteristics of individual patients and update the algorithm model over time. The data analysis module 143 is configured with an anomaly detection algorithm to detect and mark abnormal points in muscle strength data. The data analysis module 143 is also configured with a trend analysis algorithm to identify long-term trends and short-term fluctuations from muscle strength data of continuous time periods to assist in assessing the progression of muscle weakness.

[0215] It should be noted that the above-mentioned specific embodiments are exemplary, and those skilled in the art can come up with various solutions inspired by the disclosure of the present invention, and these solutions also fall within the scope of the disclosure of the present invention and fall within the scope of protection of the present invention. Those skilled in the art should understand that the present invention specification and its drawings are illustrative and do not constitute a limitation on the claims. The scope of protection of the present invention is defined by the claims and their equivalents. The present invention specification contains multiple inventive concepts, such as "preferably", "according to a preferred embodiment" or "optionally", which means that the corresponding paragraph discloses an independent concept, and the applicant reserves the right to file a divisional application based on each inventive concept.

Claims

1. A ventilation detection device for myasthenia gravis patients, characterized in that: The ventilation detection device includes: a muscle strength monitoring module configured to monitor the muscle activity of the swallowing muscles and the muscle activity of the arms of the myasthenia gravis patient, and obtain muscle strength data of the myasthenia gravis patient based on the monitoring data of the swallowing muscle activity and the monitoring data of the arm muscle activity; a respiratory monitoring module configured to detect the respiratory rate, the tidal volume of each respiratory cycle, and the inspiratory time of each respiratory cycle when the myasthenia gravis patient is receiving mechanically assisted ventilation, and obtain a change trend of the respiratory rate over time, a change trend of the tidal volume of each respiratory cycle over time, and a change trend of the inspiratory time of each respiratory cycle over time; The control module is connected to the muscle strength monitoring module and the respiratory monitoring module, and is configured to obtain muscle strength data of the myasthenia gravis patient from the muscle strength monitoring module, and obtain the time-varying trend of the respiratory rate of the myasthenia gravis patient, the time-varying trend of the tidal volume of each respiratory cycle, and the time-varying trend of the inspiratory time of each respiratory cycle from the respiratory monitoring module. Among them, when the muscle strength data of the myasthenia gravis patient is lower than the preset muscle strength threshold, and the changing trend of the respiratory rate over time, the changing trend of the tidal volume of each respiratory cycle over time, and the changing trend of the inspiratory time of each respiratory cycle over time are inconsistent with the changing trend of the pre-stored corresponding parameters, it is determined that there is human-machine resistance during the mechanical assisted ventilation process of the myasthenia gravis patient, and the type of human-machine resistance is judged according to the changing trend of the respiratory rate over time, the changing trend of the tidal volume of each respiratory cycle over time, and the changing trend of the inspiratory time of each respiratory cycle over time. The control module is further configured to: If the respiratory rate increases and is accompanied by fluctuations in the tidal volume waveform, it is judged that the ventilator has not been effectively triggered to deliver air; If the respiratory rate increases and is accompanied by an increase in inspiratory time, the ventilator is judged to be insufficient in gas delivery; If the tidal volume is lower than the preset tidal volume threshold and the inspiratory time is shorter than the preset inspiratory time threshold and the respiratory rate increases, it is determined that the ventilator has premature expiratory triggering.

2. A ventilation detection method for myasthenia gravis patients, applied to the ventilation detection device according to claim 1, characterized in that: The ventilation detection method comprises: Monitor swallowing muscle activity and arm muscle activity in patients with myasthenia gravis; Obtaining muscle strength data of myasthenia gravis patients based on monitoring data of swallowing muscle activity and monitoring data of arm muscle activity; In patients with myasthenia gravis receiving mechanical ventilation, respiratory rate, tidal volume per respiratory cycle, and inspiratory time per respiratory cycle were measured; Obtain the changing trend of respiratory rate over time, the changing trend of tidal volume of each respiratory cycle over time, and the changing trend of inspiratory time of each respiratory cycle over time; When the muscle strength data of the myasthenia gravis patient is lower than a preset muscle strength threshold, and the changing trends of the respiratory rate over time, the changing trends of the tidal volume of each respiratory cycle over time, and the changing trends of the inspiratory time of each respiratory cycle over time are inconsistent with the changing trends of the pre-stored corresponding parameters, it is determined that there is human-machine resistance during the mechanical assisted ventilation of the myasthenia gravis patient, and the type of human-machine resistance is determined based on the changing trends of the respiratory rate over time, the changing trends of the tidal volume of each respiratory cycle over time, and the changing trends of the inspiratory time of each respiratory cycle over time; If the respiratory rate increases and is accompanied by fluctuations in the tidal volume waveform, it is judged that the ventilator has not been effectively triggered to deliver air; If the respiratory rate increases and is accompanied by an increase in inspiratory time, the ventilator is judged to be insufficient in gas delivery; If the tidal volume is lower than the preset tidal volume threshold and the inspiratory time is shorter than the preset inspiratory time threshold and the respiratory rate increases, it is determined that the ventilator has premature expiratory triggering.

3. The ventilation detection method for myasthenia gravis patients according to claim 2, characterized in that: The muscle strength data of myasthenia gravis patients obtained based on the monitoring data of swallowing muscle activity and arm muscle activity include: collecting muscle strength data of a first period of time when a myasthenia gravis patient performs a first standardized action as reference muscle strength data; collecting muscle strength data of a second period of a preset time period after the myasthenia gravis patient performs the first standardized action; The muscle strength fluctuations were analyzed based on the muscle strength data of the second period and the reference muscle strength data.

4. The ventilation detection method for myasthenia gravis patients according to claim 3, characterized in that: The first standardized movements are swallowing and arm raising.

Images