Respiratory nerve muscle stimulation control method and system

Abstract

The invention discloses a respiratory nerve muscle stimulation control method and system, and relates to the technical field of medical treatment, and the method comprises the steps: collecting the body parameter information of a user; determining stimulation parameters of the user according to the body parameter information and parameters stored in a database; before electrical stimulation is carried out on the body of a user, an initial muscle contraction signal obtained from a treatment terminal is processed through a signal processing module, and an initial muscle contraction filtering signal is obtained; wherein the initial muscle contraction signal is sensed by a treatment terminal attached to the body of the user; according to the stimulation parameters of the user, the initial muscle contraction filtering signal, the real-time muscle contraction filtering signal and the real-time breathing condition, the current of electrical stimulation generated by a treatment terminal arranged on the body of the user is controlled in real time through a self-adaptive current adjustment model which is trained and optimized. According to the invention, the efficiency of electrical stimulation is improved.

Description

Technical Field

[0001] The present application relates to the field of medical technology, and in particular to a respiratory neuromuscular stimulation control method and system. Background Art

[0002] Muscle atrophy is common in people who have been injured, had surgery, been bedridden for a long time, or have neglected their fitness. When muscles atrophy to a certain extent, many daily movements become difficult for people to complete.

[0003] Neuromuscular stimulation is a method of using low-frequency pulsed current to stimulate motor nerves and muscles to cause muscle contraction. When electrical stimulation acts on muscles, the motor nerves are activated first. When the motor nerves are activated by electrical stimulation, nerve impulses are generated. The nerve impulses are transmitted to the muscles and cause muscle contraction.

[0004] When using an electrical stimulation device to treat a user, medical staff usually set the stimulation time of the electrical stimulation device according to the severity of the user's respiratory muscle weakness or respiratory muscle fatigue. That is to say, in the prior art, when using an electrical stimulation device to treat muscle atrophy, the treatment method is relatively simple, and the stimulation time is only set according to medical experience before the electrical stimulation treatment.

[0005] To address the above-mentioned problems, no effective solution has been proposed yet. Summary of the invention

[0006] The embodiments of the present application provide a respiratory neuromuscular stimulation control method and system to solve the above-mentioned technical problems.

[0007] The present application provides a respiratory neuromuscular stimulation control method, comprising:

[0008] Collecting the user's body parameter information; determining the stimulation parameters of the user according to the body parameter information and the parameters stored in the database;

[0009] Before electrically stimulating the user's body, the initial muscle contraction signal obtained from the treatment terminal is processed by a signal processing module to obtain an initial muscle contraction filter signal; wherein the initial muscle contraction signal is sensed by the treatment terminal attached to the user's body;

[0010] According to the stimulation parameters of the user, the initial muscle contraction filter signal, the real-time muscle contraction filter signal and the real-time breathing condition, the current of the electrical stimulation generated by the treatment terminal disposed on the user's body is controlled in real time by the adaptive current adjustment model completed by training optimization;

[0011] Among them, the real-time breathing condition includes the user's inhalation time and breathing frequency; the electrical stimulation current acts on the user's nerves or muscles, the muscles include single muscle groups or multiple muscle groups, and the nerves include phrenic nerves.

[0012] Furthermore, the adaptive current adjustment model is constructed and trained and optimized based on the following methods:

[0013] Acquire historical user stimulation data, historical muscle contraction signals, historical respiration data, and real-time electrical stimulation current data; wherein the historical muscle contraction signals include initial muscle contraction signals and real-time stimulated muscle contraction signals; the historical respiration data include initial respiration data and real-time stimulated respiration data;

[0014] Determine a set of stimulus factors of the historical user stimulus data;

[0015] Determining a weight vector and a membership vector of each stimulus factor in the stimulus factor set;

[0016] Performing fuzzy transformation on the weight vector and the membership vector of each stimulus factor to obtain a first fuzzy evaluation matrix;

[0017] Performing fuzzy operation on the first fuzzy evaluation matrix and the weight vectors of each stimulus factor and normalizing the result to obtain a second fuzzy evaluation matrix;

[0018] A deep neural network is constructed according to the second fuzzy evaluation matrix, including an input layer, a basic network, and an adaptive weight layer; the basic network includes six convolutional layers, three fully connected layers, and a pooling layer; wherein the convolutional layer is connected to the pooling layer, and the pooling layer is used to aggregate adjacent region values; the three fully connected layers include a first fully connected layer, a second fully connected layer, and a third fully connected layer; the adaptive weight layer is used to complete the adaptive adjustment of current parameters through back propagation;

[0019] Performing feature extraction on the initial breathing data and the real-time stimulated breathing data to obtain an initial breathing feature vector and a real-time stimulated breathing feature vector;

[0020] Calculating the distance between the initial breathing feature vector and the real-time stimulated breathing feature vector;

[0021] According to the distance between the initial breathing feature vector and the real-time stimulated breathing feature vector, the initial muscle contraction signal, the real-time stimulated muscle contraction signal, the initial breathing feature vector, the real-time stimulated breathing feature vector and the real-time electrical stimulation current data, the constructed deep neural network is optimized and trained until the loss function converges.

[0022] Furthermore, the real-time electrical stimulation current data includes the stimulation intensity, pulse width, pulse frequency and stimulation duration of the current; the deep neural network constructed is optimized and trained according to the distance between the initial breathing feature vector and the real-time stimulated breathing feature vector, the initial muscle contraction signal, the real-time stimulated muscle contraction signal, the initial breathing feature vector, the real-time stimulated breathing feature vector and the real-time electrical stimulation current data until the loss function converges, including:

[0023] A time series sample vector is constructed according to the distance between the initial breathing feature vector and the real-time stimulated breathing feature vector, the initial breathing feature vector, and the real-time stimulated breathing feature vector:

[0024] Inputting the time series sample vector into the input layer of the deep neural network to obtain a respiratory time series output data set output by the first fully connected layer;

[0025] Based on the time series sample vector, non-negative matrix decomposition is performed on the initial muscle contraction signal and the real-time stimulated muscle contraction signal to obtain a signal non-negative matrix decomposition set;

[0026] Performing window processing on the signal non-negative matrix decomposition set to obtain time domain samples; performing short-time Fourier transform on the time domain samples to obtain frequency domain samples;

[0027] quantifying a first correlation between the frequency domain samples and the real-time electrical stimulation current data by means of information entropy and mutual information, and determining a first adaptive loss function according to a first adaptive adjustment function preset in the adaptive weight layer;

[0028] quantifying a second correlation between the frequency domain samples and the respiratory timing output data set by means of information entropy and mutual information, and determining a second adaptive loss function according to a second adaptive adjustment function preset in the adaptive weight layer;

[0029] The loss function is constructed according to the first adaptive loss function and the second adaptive loss function, and the deep neural network is optimized and trained until the loss function converges.

[0030] Furthermore, the physical parameter information includes age, joint mobility, muscle atrophy, spasticity parameters and lung function parameters; wherein the lung function parameters include vital capacity, forced vital capacity and forced expiratory volume in one second.

[0031] Furthermore, the treatment terminal includes multiple treatment electrodes; the multiple treatment electrodes include electrodes attached to the posterior edges of the left and right sternocleidomastoid muscles, the left and right subclavian pectoralis major muscles, the oblique abdominal muscles, the rectus abdominis muscles, the quadriceps femoris of both lower limbs, the tibialis anterior muscles, the common peroneal nerve, the tibialis anterior muscles, the peroneus longus and brevis muscles, the quadriceps femoris, the hip abductor muscles, and the wrist and finger extensor muscles.

[0032] The present application provides a respiratory nerve muscle stimulation control system, including a user stimulation parameter determination unit, an initial muscle contraction filter signal determination unit and an electrical stimulation current real-time control unit; wherein,

[0033] The user stimulation parameter determination unit is used to collect the user's body parameter information; and determine the user's stimulation parameters according to the body parameter information and the parameters stored in the database;

[0034] The initial muscle contraction filter signal determination unit is used to process the initial muscle contraction signal obtained from the treatment terminal through the signal processing module before electrically stimulating the user's body to obtain an initial muscle contraction filter signal; wherein the initial muscle contraction signal is sensed by the treatment terminal attached to the user's body;

[0035] The electrical stimulation current real-time control unit is used to control the electrical stimulation current generated by the treatment terminal arranged on the user's body in real time according to the user's stimulation parameters, the initial muscle contraction filter signal, the real-time muscle contraction filter signal and the real-time breathing condition through an adaptive current adjustment model that has been trained and optimized; wherein the real-time breathing condition includes the user's inhalation time and breathing frequency; the electrical stimulation current acts on the user's nerves or muscles, the muscles include single muscle groups or multiple muscle groups, and the nerves include phrenic nerves.

[0036] Based on the embodiment provided by the present application, the user's body parameter information is collected; according to the body parameter information and the parameters stored in the database, the user's stimulation parameters are determined; before the user's body is electrically stimulated, the initial muscle contraction signal obtained from the treatment terminal is processed by the signal processing module to obtain the initial muscle contraction filter signal; wherein, the initial muscle contraction signal is sensed by the treatment terminal attached to the user's body; according to the user's stimulation parameters, the initial muscle contraction filter signal, the real-time muscle contraction filter signal and the real-time breathing condition, the adaptive current adjustment model completed by training optimization controls the current of the electrical stimulation generated by the treatment terminal set on the user's body in real time; wherein, the real-time breathing condition includes the user's inhalation time and breathing frequency; the electrical stimulation current acts on the user's nerves or muscles, the muscles include single muscle groups or multiple muscle groups, and the nerves include phrenic nerves. Thus, the current data of the electrical stimulation device is adjusted in time according to the user's own physical condition and the reaction to the electrical stimulation, instead of adjusting the action time of the electrical stimulation device before the treatment starts based on the experience of medical staff alone, thereby improving the efficiency and accuracy of the electrical stimulation.

[0037] Specifically, it has the following beneficial effects: Collecting the user's body parameter information: It helps personalized treatment: By collecting detailed body data of individuals, a customized treatment plan can be provided for each user; Improving treatment effect: Accurate body parameter information helps to more accurately assess treatment needs and adjust treatment plans; Enhanced safety: Understanding the user's physical condition can prevent potential treatment risks and ensure treatment safety; Determine the user's stimulation parameters based on body parameter information and parameters stored in the database: Optimize treatment decisions: Combining historical data and real-time parameters, the stimulation parameters can be determined more scientifically to improve the effectiveness of treatment; Dynamic adjustment: The stimulation parameters can be dynamically adjusted according to changes in the user's condition to adapt to changes in treatment effect and user comfort; Processing the initial muscle contraction signal through the signal processing module: Improving signal quality: Filtering can remove noise and interference and extract more accurate muscle contraction signals; Precise control: Accurate signals are the basis for achieving precise control of electrical stimulation currents, which helps to improve treatment effects; Using initial and real-time muscle contraction filtering signals Real-time control of the signal: Real-time feedback: Real-time monitoring of muscle contraction signals can adjust the electrical stimulation parameters in time to achieve closed-loop control; Adaptive adjustment: According to the changes in real-time signals, the adaptive current adjustment model can dynamically adjust the electrical stimulation to match the user's immediate needs; Consider real-time breathing conditions: Synchronous treatment: Synchronizing electrical stimulation with the user's breathing cycle can improve the comfort and effect of treatment; Personalized adjustment: Considering breathing frequency and inhalation time, more personalized plans can be customized for different users; The current of electrical stimulation acts on the user's nerves or muscles: Targeted treatment: Electrical stimulation can directly act on the target nerves or muscles to provide targeted treatment; Enhance muscle function: By stimulating specific muscle groups, muscle strength and function can be enhanced, and muscle atrophy or dysfunction can be improved; The current of electrical stimulation generated by the treatment terminal is controlled in real time: Precise dosage: Real-time control of current can ensure that the user receives treatment of appropriate intensity and avoid excessive or insufficient stimulation; Improve efficacy: Precise current control helps to improve treatment effects, reduce side effects, and enhance the user's overall treatment experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] The drawings described herein are used to provide a further understanding of the embodiments of the present invention and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0039] Figure 1 is a flow chart of an optional respiratory nerve muscle stimulation control method according to an embodiment of the present application;

[0040] Figure 2 It is a structural diagram of an optional respiratory neuromuscular stimulation control system according to an embodiment of the present application.

[0041] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0042] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.

[0043] Alternatively, if Figure 1 As shown, the present application provides a respiratory nerve muscle stimulation control method, comprising:

[0044] S101, collecting the user's body parameter information; determining the user's stimulation parameters according to the body parameter information and the parameters stored in the database;

[0045] S102, before electrically stimulating the user's body, processing the initial muscle contraction signal obtained from the treatment terminal through a signal processing module to obtain an initial muscle contraction filter signal; wherein the initial muscle contraction signal is sensed by the treatment terminal attached to the user's body;

[0046] S103, based on the user's stimulation parameters, initial muscle contraction filter signal, real-time muscle contraction filter signal and real-time breathing conditions, the adaptive current adjustment model completed through training and optimization is used to control the electric stimulation current generated by the treatment terminal arranged on the user's body in real time; wherein the real-time breathing conditions include the user's inhalation time and breathing frequency; the electric stimulation current acts on the user's nerves or muscles, the muscles include single muscle groups or multiple muscle groups, and the nerves include phrenic nerves.

[0047] Based on the embodiment provided by the present application, the user's body parameter information is collected; the user's stimulation parameters are determined according to the body parameter information and the parameters stored in the database; before the user's body is electrically stimulated, the initial muscle contraction signal obtained from the treatment terminal is processed by the signal processing module to obtain the initial muscle contraction filter signal; wherein, the initial muscle contraction signal is sensed by the treatment terminal attached to the user's body; according to the user's stimulation parameters, the initial muscle contraction filter signal, the real-time muscle contraction filter signal and the real-time breathing condition, the adaptive current adjustment model completed by training optimization controls the current of the electrical stimulation generated by the treatment terminal set on the user's body in real time; wherein, the real-time breathing condition includes the user's inhalation time and breathing frequency; the electrical stimulation current acts on the user's nerves or muscles, the muscles include single muscle groups or multiple muscle groups, and the nerves include phrenic nerves. Thus, the electrical stimulation device is adjusted in time according to the user's own physical condition and the reaction to the electrical stimulation, and the efficiency and accuracy of the electrical stimulation are improved.

[0048] Specifically, it has the following beneficial effects: Collecting the user's body parameter information: It helps personalized treatment: By collecting detailed body data of individuals, a customized treatment plan can be provided for each user; Improving treatment effect: Accurate body parameter information helps to more accurately assess treatment needs and adjust treatment plans; Enhanced safety: Understanding the user's physical condition can prevent potential treatment risks and ensure treatment safety; Determine the user's stimulation parameters based on body parameter information and parameters stored in the database: Optimize treatment decisions: Combining historical data and real-time parameters, the stimulation parameters can be determined more scientifically to improve the effectiveness of treatment; Dynamic adjustment: The stimulation parameters can be dynamically adjusted according to changes in the user's condition to adapt to changes in treatment effect and user comfort; Processing the initial muscle contraction signal through the signal processing module: Improving signal quality: Filtering can remove noise and interference and extract more accurate muscle contraction signals; Precise control: Accurate signals are the basis for achieving precise control of electrical stimulation currents, which helps to improve treatment effects; Using initial and real-time muscle contraction filtering signals Real-time control of the signal: Real-time feedback: Real-time monitoring of muscle contraction signals can adjust the electrical stimulation parameters in time to achieve closed-loop control; Adaptive adjustment: According to the changes in real-time signals, the adaptive current adjustment model can dynamically adjust the electrical stimulation to match the user's immediate needs; Consider real-time breathing conditions: Synchronous treatment: Synchronizing electrical stimulation with the user's breathing cycle can improve the comfort and effect of treatment; Personalized adjustment: Considering breathing frequency and inhalation time, more personalized plans can be customized for different users; The current of electrical stimulation acts on the user's nerves or muscles: Targeted treatment: Electrical stimulation can directly act on the target nerves or muscles to provide targeted treatment; Enhance muscle function: By stimulating specific muscle groups, muscle strength and function can be enhanced, and muscle atrophy or dysfunction can be improved; The current of electrical stimulation generated by the treatment terminal is controlled in real time: Precise dosage: Real-time control of current can ensure that the user receives treatment of appropriate intensity and avoid excessive or insufficient stimulation; Improve efficacy: Precise current control helps to improve treatment effects, reduce side effects, and enhance the user's overall treatment experience.

[0049] Furthermore, the adaptive current regulation model is constructed and trained and optimized based on the following methods:

[0050] Acquire historical user stimulation data, historical muscle contraction signals, historical respiration data, and real-time electrical stimulation current data; wherein the historical muscle contraction signals include initial muscle contraction signals and real-time stimulated muscle contraction signals; and the historical respiration data include initial respiration data and real-time stimulated respiration data;

[0051] Determine a stimulus factor set of historical user stimulus data;

[0052] Determine the weight vector and the membership vector of each stimulus factor in the stimulus factor set;

[0053] Perform fuzzy transformation on the weight vector and the membership vector of each stimulus factor to obtain the first fuzzy evaluation matrix;

[0054] Perform fuzzy operation on the first fuzzy evaluation matrix and the weight vectors of each stimulus factor and perform normalization to obtain a second fuzzy evaluation matrix;

[0055] A deep neural network is constructed according to the second fuzzy judgment matrix, including an input layer, a basic network, and an adaptive weight layer; the basic network includes six convolutional layers, three fully connected layers, and a pooling layer; the convolutional layer is connected to the pooling layer, and the pooling layer is used to aggregate the values ​​of adjacent regions; the three fully connected layers include the first fully connected layer, the second fully connected layer, and the third fully connected layer; the adaptive weight layer is used to complete the adaptive adjustment of the current parameters through back propagation;

[0056] Extracting features from the initial breathing data and the real-time stimulated breathing data to obtain an initial breathing feature vector and a real-time stimulated breathing feature vector;

[0057] Calculating the distance between the initial breathing feature vector and the real-time stimulated breathing feature vector;

[0058] According to the distance between the initial breathing feature vector and the real-time stimulated breathing feature vector, the initial muscle contraction signal, the real-time stimulated muscle contraction signal, the initial breathing feature vector, the real-time stimulated breathing feature vector and the real-time electrical stimulation current data, the constructed deep neural network is optimized and trained until the loss function converges.

[0059] Based on the embodiments provided in this application, the following beneficial effects are achieved: obtaining historical and real-time data: data-driven decision-making: by analyzing historical and real-time data, the adaptive current adjustment model can be constructed and optimized more accurately; enhancing the generalization ability of the model: combining historical and real-time data, the model can adapt to changes of different users and different time points; determining the set of stimulus factors: comprehensiveness: ensuring that the model considers all relevant stimulus factors and improves the accuracy of predicting treatment effects; customized treatment: identifying key stimulus factors and providing customized treatment parameters for each user; determining weight vectors and membership vectors: quantifying the influence: quantifying the influence of different stimulus factors through weights and membership, making the model adjustment more accurate. Increase precision; Prioritization: clarify which factors are more important, so as to give them higher priority when optimizing the model; Perform fuzzy transformation: Handle uncertainty: Fuzzy logic can handle uncertainty and ambiguity in data and improve the robustness of the model; Flexibility: Allow the model to make effective decisions when faced with imprecise or partial information; Adaptability: The adaptive weight layer enables the model to adjust in real time based on feedback, improving the accuracy of current control; Feature extraction and distance calculation: Key information identification: Extracting key features from breathing data helps the model better understand the user's breathing pattern; Dynamic tracking: Calculating the distance between feature vectors enables the model to track dynamic changes in user status.

[0060] Furthermore, the real-time electrical stimulation current data includes the stimulation intensity, pulse width, pulse frequency and stimulation duration of the current; according to the distance between the initial breathing feature vector and the real-time stimulated breathing feature vector, the initial muscle contraction signal, the real-time stimulated muscle contraction signal, the initial breathing feature vector, the real-time stimulated breathing feature vector and the real-time electrical stimulation current data, the constructed deep neural network is optimized and trained until the loss function converges, including:

[0061] Construct a time series sample vector based on the distance between the initial breathing feature vector and the real-time stimulated breathing feature vector, the initial breathing feature vector, and the real-time stimulated breathing feature vector:

[0062] Input the time series sample vector into the input layer of the deep neural network to obtain the respiratory time series output data set output by the first fully connected layer;

[0063] Based on the time series sample vector, the initial muscle contraction signal and the real-time stimulated muscle contraction signal are subjected to non-negative matrix decomposition to obtain a signal non-negative matrix decomposition set;

[0064] Perform window processing on the signal non-negative matrix decomposition set to obtain time domain samples; perform short-time Fourier transform on the time domain samples to obtain frequency domain samples;

[0065] quantifying a first correlation between the frequency domain samples and the real-time electrical stimulation current data by means of information entropy and mutual information, and determining a first adaptive loss function according to a first adaptive adjustment function preset in the adaptive weight layer;

[0066]

[0067] in, is the first adaptive loss function; U is the number of adaptive weight layers; u is the number of adaptive weight layers; Strat1 u is the first strategy of the u-th adaptive weight layer; Ajust1() is the first adaptive adjustment function; α1 is the regularization parameter; e is a natural constant; relev1 is the first correlation quantity;

[0068] quantifying a second correlation between the frequency domain samples and the respiratory timing output data set by means of information entropy and mutual information, and determining a second adaptive loss function according to a second adaptive adjustment function preset in the adaptive weight layer;

[0069] A loss function is constructed according to the first adaptive loss function and the second adaptive loss function, and the deep neural network is optimized and trained until the loss function converges.

[0070] Based on the embodiments provided in the present application, the following beneficial effects are achieved: constructing a time series sample vector: by combining the initial and real-time breathing and muscle contraction signals, a comprehensive time series sample vector can be created, which helps the deep neural network capture the dynamic changes in the user's physiological state; inputting the deep neural network: inputting the time series sample vector into the network enables the network to receive rich physiological state information starting from the input layer, laying the foundation for subsequent feature learning and state prediction; output of the first fully connected layer: the breathing time series output data set output by the first fully connected layer can provide the network with preliminary breathing pattern features, which is helpful for subsequent analysis and processing; non-negative matrix decomposition: performing non-negative matrix decomposition on the initial and real-time stimulation muscle contraction signals can extract the key features of muscle activity, which is helpful for understanding the intrinsic structure of muscle activity; window processing and short-time Fourier transform: through window processing and short-time Fourier transform, time domain samples can be converted into frequency domain samples. , which helps to analyze the frequency characteristics of muscle activity and provide a basis for adjusting the frequency of electrical stimulation; information entropy and mutual information quantification: using information entropy and mutual information to quantify the correlation between frequency domain samples and electrical stimulation current data, the relationship between different physiological signals and electrical stimulation effects can be evaluated, providing guidance for optimizing electrical stimulation parameters; determination of adaptive loss function: the first adaptive loss function and the second adaptive loss function determined according to the first correlation quantity enable the model training process to automatically adjust the sensitivity to outliers and improve the robustness of the model; optimization training of deep neural networks: by combining the first and second adaptive loss functions for network optimization training, it can be ensured that the network continuously adjusts itself during the training process to adapt to changes in the user's physiological state until the loss function converges; real-time current control: real-time control of the electrical stimulation current, so that the electrical stimulation can be dynamically adjusted according to the user's real-time physiological state, improving the treatment effect and user comfort.

[0071] These steps together form a closed-loop adaptive current adjustment system, which can make intelligent adjustments based on the user's real-time physiological data to achieve personalized and optimized electrical stimulation therapy. In this way, the effectiveness and safety of electrical stimulation therapy can be significantly improved.

[0072] Further,

[0073] in, is the second adaptive loss function; U is the number of adaptive weight layers; u is the number of adaptive weight layers; Strat2 u is the second strategy of the u-th adaptive weight layer; Ajust2() is the second adaptive adjustment function; α2 is the regularization parameter; e is a natural constant; relev2 is the second related quantity.

[0074] Furthermore, the distance between sample points is calculated pairwise based on the following formula:

[0075]

[0076] Where distan(X,Y) represents the distance between the initial respiratory feature vector X and the real-time stimulated respiratory feature vector Y; m is the dimension of the initial respiratory feature vector; n is the dimension of the real-time stimulated respiratory feature vector; x i represents the coordinate value of the initial respiratory feature vector X in the dimension numbered i; y j is the coordinate value of the real-time stimulation breathing feature vector Y in the dimension numbered j; t is a controllable variable parameter.

[0077] Furthermore, the body parameter information includes age, joint mobility, muscle atrophy, spasticity parameters and lung function parameters; wherein the lung function parameters include vital capacity, forced vital capacity and forced expiratory volume in one second.

[0078] Furthermore, the treatment terminal includes multiple treatment electrodes; the multiple treatment electrodes include electrodes attached to the posterior edges of the left and right sternocleidomastoid muscles, the left and right subclavian pectoralis major muscles, the oblique abdominal muscles, the rectus abdominis muscles, the quadriceps femoris of both lower limbs, the tibialis anterior muscles, the common peroneal nerve, the tibialis anterior muscles, the peroneus longus and brevis muscles, the quadriceps femoris, the hip abductor muscles, and the wrist and finger extensor muscles.

[0079] Alternatively, if Figure 2 As shown, the present application provides a respiratory nerve muscle stimulation control system, including a user stimulation parameter determination unit 201, an initial muscle contraction filter signal determination unit 202 and an electrical stimulation current real-time control unit 203; wherein,

[0080] The user stimulation parameter determination unit 201 is used to collect the user's body parameter information; determine the user's stimulation parameters according to the body parameter information and the parameters stored in the database;

[0081] The initial muscle contraction filter signal determination unit 202 is used to process the initial muscle contraction signal obtained from the treatment terminal through the signal processing module before electrically stimulating the user's body to obtain an initial muscle contraction filter signal; wherein the initial muscle contraction signal is sensed by the treatment terminal attached to the user's body;

[0082] The electrical stimulation current real-time control unit 203 is used to control the electrical stimulation current generated by the treatment terminal arranged on the user's body in real time according to the user's stimulation parameters, initial muscle contraction filter signal, real-time muscle contraction filter signal and real-time breathing conditions through an adaptive current adjustment model optimized by training; wherein the real-time breathing conditions include the user's inhalation time and breathing frequency; the electrical stimulation current acts on the user's nerves or muscles, the muscles include single muscle groups or multiple muscle groups, and the nerves include phrenic nerves.

[0083] Furthermore, the body parameter information includes age, joint mobility, muscle atrophy, spasticity parameters and lung function parameters; wherein the lung function parameters include vital capacity, forced vital capacity and forced expiratory volume in one second.

[0084] Furthermore, the treatment terminal includes multiple treatment electrodes; the multiple treatment electrodes include electrodes attached to the posterior edges of the left and right sternocleidomastoid muscles, the left and right subclavian pectoralis major muscles, the oblique abdominal muscles, the rectus abdominis muscles, the quadriceps femoris of both lower limbs, the tibialis anterior muscles, the common peroneal nerve, the tibialis anterior muscles, the peroneus longus and brevis muscles, the quadriceps femoris, the hip abductor muscles, and the wrist and finger extensor muscles.

[0085] The above are only preferred embodiments of the present invention, and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A respiratory nerve muscle stimulation control method, characterized in that: include: Collecting the user's body parameter information; determining the stimulation parameters of the user according to the body parameter information and the parameters stored in the database; Before electrically stimulating the user's body, the initial muscle contraction signal obtained from the treatment terminal is processed by a signal processing module to obtain an initial muscle contraction filter signal; wherein the initial muscle contraction signal is sensed by the treatment terminal attached to the user's body; According to the stimulation parameters of the user, the initial muscle contraction filter signal, the real-time muscle contraction filter signal and the real-time breathing condition, the current of the electrical stimulation generated by the treatment terminal disposed on the user's body is controlled in real time by the adaptive current adjustment model completed by training optimization; Among them, the real-time breathing condition includes the user's inhalation time and breathing frequency; the electrical stimulation current acts on the user's nerves or muscles, the muscles include single muscle groups or multiple muscle groups, and the nerves include phrenic nerves.

2. The respiratory nerve muscle stimulation control method according to claim 1, characterized in that: The adaptive current regulation model is constructed and trained and optimized based on the following methods: Acquire historical user stimulation data, historical muscle contraction signals, historical respiration data, and real-time electrical stimulation current data; wherein the historical muscle contraction signals include initial muscle contraction signals and real-time stimulated muscle contraction signals; the historical respiration data include initial respiration data and real-time stimulated respiration data; Determine a set of stimulus factors of the historical user stimulus data; Determining a weight vector and a membership vector of each stimulus factor in the stimulus factor set; Performing fuzzy transformation on the weight vector and the membership vector of each stimulus factor to obtain a first fuzzy evaluation matrix; Performing fuzzy operation on the first fuzzy evaluation matrix and the weight vectors of each stimulus factor and normalizing the result to obtain a second fuzzy evaluation matrix; A deep neural network is constructed according to the second fuzzy evaluation matrix, including an input layer, a basic network, and an adaptive weight layer; the basic network includes six convolutional layers, three fully connected layers, and a pooling layer; wherein the convolutional layer is connected to the pooling layer, and the pooling layer is used to aggregate adjacent region values; the three fully connected layers include a first fully connected layer, a second fully connected layer, and a third fully connected layer; the adaptive weight layer is used to complete the adaptive adjustment of current parameters through back propagation; Performing feature extraction on the initial breathing data and the real-time stimulated breathing data to obtain an initial breathing feature vector and a real-time stimulated breathing feature vector; Calculating the distance between the initial breathing feature vector and the real-time stimulated breathing feature vector; According to the distance between the initial breathing feature vector and the real-time stimulated breathing feature vector, the initial muscle contraction signal, the real-time stimulated muscle contraction signal, the initial breathing feature vector, the real-time stimulated breathing feature vector and the real-time electrical stimulation current data, the constructed deep neural network is optimized and trained until the loss function converges.

3. The respiratory nerve muscle stimulation control method according to claim 2, characterized in that: The real-time electrical stimulation current data includes the stimulation intensity, pulse width, pulse frequency and stimulation duration of the current; the constructed deep neural network is optimized and trained according to the distance between the initial breathing feature vector and the real-time stimulated breathing feature vector, the initial muscle contraction signal, the real-time stimulated muscle contraction signal, the initial breathing feature vector, the real-time stimulated breathing feature vector and the real-time electrical stimulation current data until the loss function converges, including: A time series sample vector is constructed according to the distance between the initial breathing feature vector and the real-time stimulated breathing feature vector, the initial breathing feature vector, and the real-time stimulated breathing feature vector: The time series sample vector is input into the input layer of the deep neural network to obtain the respiratory time series output data set output by the first fully connected layer.

4. The respiratory nerve muscle stimulation control method according to claim 3, characterized in that: Based on the time series sample vector, non-negative matrix decomposition is performed on the initial muscle contraction signal and the real-time stimulated muscle contraction signal to obtain a signal non-negative matrix decomposition set; Performing window processing on the signal non-negative matrix decomposition set to obtain time domain samples; and performing short-time Fourier transform on the time domain samples to obtain frequency domain samples.

5. The respiratory nerve muscle stimulation control method according to claim 4, characterized in that: quantifying a first correlation between the frequency domain samples and the real-time electrical stimulation current data by means of information entropy and mutual information, and determining a first adaptive loss function according to a first adaptive adjustment function preset in the adaptive weight layer; quantifying a second correlation between the frequency domain samples and the respiratory timing output data set by means of information entropy and mutual information, and determining a second adaptive loss function according to a second adaptive adjustment function preset in the adaptive weight layer; The loss function is constructed according to the first adaptive loss function and the second adaptive loss function, and the deep neural network is optimized and trained until the loss function converges.

6. The respiratory nerve muscle stimulation control method according to claim 1, characterized in that: The body parameter information includes age, joint range of motion, muscle atrophy, spasticity parameters and lung function parameters; wherein the lung function parameters include vital capacity, forced vital capacity and forced expiratory volume in one second.

7. The respiratory nerve muscle stimulation control method according to claim 1, characterized in that: The treatment terminal includes multiple treatment electrodes; the multiple treatment electrodes include electrodes attached to the posterior edges of the left and right sternocleidomastoid muscles, the left and right subclavian pectoralis major muscles, the oblique abdominal muscles, the rectus abdominis muscles, the quadriceps femoris muscles of both lower limbs, the tibialis anterior muscles, the common peroneal nerve, the tibialis anterior muscles, the peroneus longus and brevis muscles, the quadriceps femoris muscles, the hip abductor muscles, and the wrist and finger extensor muscles.

8. A respiratory nerve muscle stimulation control system, the system implementing the method according to claims 1-7, characterized in that: It includes a user stimulation parameter determination unit, an initial muscle contraction filter signal determination unit and an electrical stimulation current real-time control unit; wherein, The user stimulation parameter determination unit is used to collect the user's body parameter information; and determine the user's stimulation parameters according to the body parameter information and the parameters stored in the database; The initial muscle contraction filter signal determination unit is used to process the initial muscle contraction signal obtained from the treatment terminal through the signal processing module before electrically stimulating the user's body to obtain an initial muscle contraction filter signal; wherein the initial muscle contraction signal is sensed by the treatment terminal attached to the user's body; The electrical stimulation current real-time control unit is used to control the electrical stimulation current generated by the treatment terminal arranged on the user's body in real time according to the user's stimulation parameters, the initial muscle contraction filter signal, the real-time muscle contraction filter signal and the real-time breathing condition through an adaptive current adjustment model that has been trained and optimized; wherein the real-time breathing condition includes the user's inhalation time and breathing frequency; the electrical stimulation current acts on the user's nerves or muscles, the muscles include single muscle groups or multiple muscle groups, and the nerves include phrenic nerves.

9. The respiratory nerve muscle stimulation control system according to claim 8, characterized in that: The body parameter information includes age, joint range of motion, muscle atrophy, spasticity parameters and lung function parameters; wherein the lung function parameters include vital capacity, forced vital capacity and forced expiratory volume in one second.

10. The respiratory nerve muscle stimulation control system according to claim 8, characterized in that: The treatment terminal includes multiple treatment electrodes; the multiple treatment electrodes include electrodes attached to the posterior edges of the left and right sternocleidomastoid muscles, the left and right subclavian pectoralis major muscles, the oblique abdominal muscles, the rectus abdominis muscles, the quadriceps femoris muscles of both lower limbs, the tibialis anterior muscles, the common peroneal nerve, the tibialis anterior muscles, the peroneus longus and brevis muscles, the quadriceps femoris muscles, the hip abductor muscles, and the wrist and finger extensor muscles.

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