Respiratory nerve muscle stimulation method and system

Abstract

The invention relates to the field of medical instruments, and discloses a respiratory neuromuscular stimulation method and system, and the method comprises the steps: obtaining an action signal generated by the abdomen or chest of a patient along with the respiratory action through a three-axis sensor; the collected signals are analyzed to determine an optimal data channel, and an oscillogram is generated; determining a breathing threshold based on the wave crest and the wave trough; analyzing the breathing signal of the patient based on the breathing threshold value, and judging the breathing action of the patient; controlling the output of an electrical stimulation signal according to the breathing action of the patient to electrically stimulate the patient; determining a second breathing threshold value based on the wave crest and the wave trough, and judging the effectiveness of the signal according to the second breathing threshold value. According to the invention, the three-axis sensing signal and the dynamic threshold judgment are fused, the real-time accurate judgment of the breathing action and the intelligent synchronous control of the electrical stimulation are realized, and the purpose of improving the treatment comfort and curative effect of the patient is achieved.

Description

Technical Field

[0001] The present invention relates to the field of medical devices, and in particular to a respiratory nerve muscle stimulation method and system. Background Art

[0002] The Respiratory Neuromuscular Stimulator is a therapeutic device that improves pulmonary ventilation and promotes respiratory recovery. Using electrophysiological techniques, it provides synergistic feedback electrical stimulation to the phrenic nerve and abdominal muscles via external electrodes, causing them to contract regularly, increasing tidal volume, promoting CO2 excretion within the alveoli, alleviating dyspnea symptoms, and enhancing respiratory muscle strength and endurance, thereby promoting respiratory recovery.

[0003] Traditional electrical stimulation control mainly trains the patient's breathing and relies on the patient's active cooperation. It usually collects respiratory signals and performs specific calculations to obtain respiratory parameters, such as the number of breaths or respiratory frequency. The respiratory parameters determine the electrical stimulation signal of a fixed period and output it to the patient, so that the patient breathes according to the rhythm of the stimulator. The patient must consciously keep in sync with the stimulator. When the breathing rhythm is not synchronized with the stimulator, it will cause confrontation. For example, when the patient inhales, the electrical stimulation of exhalation is output to the patient, which not only fails to have a rehabilitation effect, but will increase the burden on the patient.

[0004] At the same time, existing respiratory electrical stimulation devices mostly use preset time modes or manual triggering, which cannot dynamically adapt to the patient's actual respiratory rhythm, resulting in asynchrony between stimulation and breathing movements, which can easily cause discomfort or even respiratory disorders. Summary of the Invention

[0005] To solve the problem, the present invention provides a respiratory nerve muscle stimulation method, which realizes real-time and accurate judgment of respiratory movements and intelligent synchronous control of electrical stimulation by fusing three-axis sensor signals and dynamic thresholds, so as to achieve the purpose of improving patient treatment comfort and efficacy.

[0006] To achieve the above object, the present invention proposes a technical solution, a respiratory nerve muscle stimulation method, comprising the following steps: S1 uses a three-axis sensor to obtain the motion signal generated by the patient's abdomen or chest during breathing; S2 analyzes the patient's respiratory signal based on the abdominal or chest motion signal and determines the patient's respiratory movement; S3 controls the output of the electrical stimulation signal according to the patient's breathing action to perform electrical stimulation on the patient.

[0007] Specifically, in step S1, the three-axis sensor includes a three-axis gravity sensor and a three-axis acceleration sensor. A gyroscope sensor can also be used to collect the angle changes of the abdomen or chest when it moves with breathing movements. In addition, two of the three-axis gravity sensor, three-axis acceleration sensor, and gyroscope sensor can be used simultaneously to form a six-axis sensor to monitor abdominal or chest movement signals.

[0008] Furthermore, the judgment logic of the three-axis sensor monitoring is: S101 sets the range of the three-axis sensor; S102 collects motion data in the X, Y, and Z axes within a time period of T; S103 analyzes and calculates the collected data to determine the optimal data channel; S104 generates a waveform diagram based on the optimal data channel.

[0009] The acquisition time T is not limited. The longer the acquisition time T, the larger the amount of data collected and the higher the accuracy of data processing. However, the patient's respiratory frequency and amplitude change dynamically, and it is difficult to achieve dynamic changes in data processing over a long period of time. If the acquisition time T is short, the data accuracy will be reduced, but it will be easier to achieve real-time synchronization and adapt to real-time changes in the patient's respiratory status.

[0010] Preferably, the time T is 30 to 120 seconds.

[0011] Furthermore, the step S2 is specifically as follows: S201 collects all peak and trough data in the waveform into one data set; S202 performs rounding processing on the peaks and troughs in the data set to select the peaks and troughs with the most occurrences; S203: Based on the peaks and troughs that appear the most times, select the smallest peak and the smallest trough as the reference peak and reference trough; S204 sets a first inhalation threshold and a first exhalation threshold based on the reference peak and the reference trough; S205 sets the reference slope k0 based on the waveform diagram and calculates the slope k of the motion data obtained at time t. t ; S206 obtains the patient's breathing signal according to the first inhalation threshold, the first exhalation threshold and the reference slope k0; S207 is based on the respiratory signal and the slope k of the motion data at time t t Determine the patient's breathing movements.

[0012] Furthermore, in step S202, the peak and trough data are rounded down, specifically: Where: ƒ′ is the peak or trough value after rounding; ƒ is the peak or trough value in the data set; N is the number of digits to be rounded, which can be 10 or 100.

[0013] In another processing scheme, the peak and trough data rounding process can also be rounded up, specifically: Where: ƒ′ is the peak or trough value after rounding; ƒ is the peak or trough value in the data set; N is the number of digits to be rounded, which can be 10 or 100.

[0014] Furthermore, the first exhalation threshold is set to: Where: 呼 is the first exhalation threshold; ƒ 峰 is the reference peak; 谷 is the reference trough; a is the setting coefficient; The first inhalation threshold is set as: Where: 吸 is the first inspiratory threshold; ƒ 峰 is the reference peak; 谷 is the reference trough; a is the setting coefficient.

[0015] Specifically, in the first inhalation threshold and the first exhalation threshold, the coefficient a ranges from 0.05 to 0.3.

[0016] Preferably, the coefficient a is 0.2.

[0017] Furthermore, in step S205, the slope k of the motion data at time t t The slope k at time t is calculated by the obtained abdominal or chest motion data. Specifically, n abdominal or chest motion data are uploaded every t1 time period, and the slope k at time t is calculated using the abdominal or chest motion data of the latest m t1 time periods each time. t ; The reference slope k0 ranges from 0.2 to 2.

[0018] Among them: the time period t1 is not limited and can be 50ms, 100ms, 120ms or other time periods, as long as it is a given smaller time change; the number n of abdominal or chest motion data uploaded in the t1 time period is not limited and can be 3, 4, 5 or other numbers. In the most recent m t1 time periods, m represents the number of consecutive t1 time periods. The specific value is not limited and can be 1, 2, 3, 4 or other numbers.

[0019] Preferably, the t1 time period is 60ms to 120ms, n of the abdominal or chest motion data to be uploaded is 5, and the data of the latest 3 t1 time periods are used for the latest m t1 time periods.

[0020] Furthermore, in a shorter period of time, the slope k t The calculation is approximately linear, specifically: Where: k t is the slope of the current collected data; dg is the change in the motion signal collected during the time period t; dt is the time change.

[0021] Furthermore, in step S205, the respiratory signal includes: Inspiratory start signal, when the slope of the collected data k t When the slope is greater than the reference slope k0 and the abdominal or chest motion data is equal to the first inhalation threshold, it is determined to be an inhalation start signal; Inspiratory termination signal, when the slope of the collected data k t When the slope is greater than the reference slope k0 and the abdominal or chest motion data reaches a peak, it is determined as an inhalation termination signal; Exhalation start signal, when the slope of the collected data k t When the slope is less than the reference slope - k0 and the abdominal or chest motion data is equal to the first exhalation threshold, it is determined to be an inhalation start signal; Exhalation termination signal, when the slope of the collected data k t When the slope is less than the reference slope - k0 and the abdominal or chest motion data reaches a trough, it is determined as an inhalation termination signal.

[0022] Furthermore, the step S207 is specifically as follows: the period between the collected inhalation start signal and the collected inhalation end signal is the inhalation action; the period between the collected exhalation start signal and the collected exhalation end signal is the exhalation action; and the period between two adjacent inhalation start signals is a respiratory cycle.

[0023] Furthermore, each respiratory cycle of the patient is recorded and compared with a preset standard respiratory cycle to analyze the patient's respiratory status, which includes: tachypnea, normopnea, bradypnea, etc.

[0024] Furthermore, the value of the reference slope k0 is automatically adjusted based on the respiratory state; When the respiratory state is judged as tachypnea, the value of k0 is set according to the standard deviation between the monitored average respiratory cycle and the preset standard respiratory cycle. The larger the standard deviation, the larger the value of k0. The maximum value is k0=2. When the respiratory state is determined to be bradypnea, the value of k0 is set according to the standard deviation between the monitored average respiratory cycle and the preset standard respiratory cycle. The larger the standard deviation, the smaller the value of k0. The minimum value is k0=0.2. Furthermore, the step S2 further includes determining the validity of the respiratory signal, wherein the determination principle is as follows: setting a second exhalation threshold and a second inhalation threshold; When the slope k of the collected data t When the slope is greater than the reference slope k0 and the amplitude is greater than the second exhalation threshold within time t, it is determined to be an invalid signal; When the slope k of the collected data t When the slope is greater than the reference slope k0 and the amplitude is greater than the first inhalation threshold and less than the second exhalation threshold within time t, it is determined to be a valid signal; When the slope k of the collected data t When the slope is less than the reference slope - k0 and the amplitude is less than the second inhalation threshold within time t, it is determined to be an invalid signal; When the slope k of the collected data t When the slope is less than the reference slope - k0 and the amplitude is greater than the first exhalation threshold and less than the second inhalation threshold within time t, it is determined to be a valid signal.

[0025] Furthermore, the second exhalation threshold value is expressed as: in: is the second exhalation threshold; 峰 is the reference peak; 谷 is the reference trough; β is the setting coefficient; The second inhalation threshold expression is: in: is the second inspiratory threshold; ƒ 峰 is the reference peak; 谷 is the reference trough; β is the setting coefficient.

[0026] Specifically, in the second exhalation threshold and the second inhalation threshold, the coefficient β is set to a value of 0.4-1.

[0027] Preferably, the coefficient β is set to a value of 0.5.

[0028] Furthermore, in step S3, the logic for controlling the output of the electrical stimulation signal is specifically as follows: When receiving the inspiration start signal, it sends out the phrenic nerve electrical stimulation signal to output electrical stimulation to the phrenic nerve; When receiving the inhalation termination signal, a stop electrical stimulation signal is sent to stop outputting electrical stimulation; When receiving the exhalation start signal, it sends out an abdominal muscle electrical stimulation signal to output electrical stimulation to the abdominal muscles; When the exhalation termination signal is received, a stop electrical stimulation signal is sent to stop outputting electrical stimulation.

[0029] Apply electrical stimulation to the phrenic nerve to stimulate the diaphragm to contract and assist in inspiration; Electrical stimulation is applied to the abdominal muscles to stimulate their contraction, increase abdominal pressure, reposition the diaphragm, and assist exhalation.

[0030] Furthermore, the abdominal muscles include the rectus abdominis and the lower abdominal muscles. When the abdominal muscle electrical stimulation signal is sent, electrical stimulation is output to either the rectus abdominis or the lower abdominal muscles, or electrical stimulation is output to both the rectus abdominis and the lower abdominal muscles at the same time.

[0031] Furthermore, the first inhalation threshold, the first exhalation threshold, the second inhalation threshold, and the second exhalation threshold are all dynamic thresholds, specifically: continuously monitoring the patient's abdominal or chest movement data, performing a threshold calculation on the data collected within each time T, and performing respiratory signal judgment according to the previously calculated threshold within the subsequent time T. At the same time, the collected abdominal or chest movement data is uploaded as a new data set and the threshold is recalculated, and the respiratory signal is judged according to the recalculated threshold within the next time T.

[0032] Also disclosed is a respiratory nerve muscle stimulation system for implementing the above-mentioned respiratory nerve muscle stimulation method, comprising: Three-axis sensor: used to monitor the patient's chest or abdomen motion signals accompanying breathing movements and upload the collected data to the signal acquisition unit; Signal acquisition unit: collects the patient's respiratory data through a multi-parameter sensor, and uploads the collected signals to the data processing unit after aggregation; Data processing unit: receives the motion signal data uploaded by the signal acquisition unit, processes and analyzes the received data, and transmits the analysis results to the control unit; Control unit: controls the stimulation electrodes to output electrical stimulation according to the analysis results sent by the data processing unit; Stimulation electrode: receives the electrical stimulation signal from the control unit and performs electrical stimulation therapy on the patient.

[0033] Beneficial effects of the present invention: 1. By analyzing and comparing the signals of the three data channels of the three-axis sensor, only the optimal data channel is used as the effective signal for analysis, which can avoid the mutual interference of multiple signals, reduce the simplicity of signal judgment, and speed up the response speed; 2. By integrating triaxial sensor signals and dynamic threshold judgment, it achieves real-time and accurate judgment of respiratory movements and intelligent synchronous control of electrical stimulation, thereby improving patient comfort and therapeutic efficacy; 3. By setting the second respiratory threshold to determine the validity of the signal, the interference caused by the patient's coughing, talking, shaking and other non-respiratory movements during breathing can be accurately determined, thereby improving anti-interference performance.

[0034] Other beneficial effects are described in the detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. The drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. For those skilled in the art, other drawings can be obtained based on the drawings without paying any creative work.

[0036] Figure 1 A flow chart of a method for stimulating respiratory nerve muscles according to the present invention; Figure 2 This is a comparison waveform diagram of the data collected by the three-axis sensor X, Y, and Z axes of the present invention; Figure 3 This is a flow chart of an embodiment of the present invention for analyzing collected abdominal or chest motion signals; Figure 4 An embodiment of the present invention for generating a waveform diagram for monitoring data of a three-axis sensor; Figure 5 Schematic diagram of the respiratory neuromuscular stimulation system of the present invention; Figure 6 This is a schematic diagram of the structure of the respiratory neuromuscular stimulation system of the present invention.

[0037] Among them: 1. Treatment host; 11. Data processing unit; 12. Control unit; 2. Signal acquisition module; 3. Three-axis sensor; 4. Stimulation electrode; 41. Phrenic nerve electrode; 42. Abdominal muscle electrode. DETAILED DESCRIPTION

[0038] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0039] Reference Figure 1 As shown, an embodiment of a respiratory nerve muscle stimulation method includes the following steps: S1 uses a three-axis sensor to obtain the motion signal generated by the patient's abdomen or chest during breathing; S2 analyzes the patient's respiratory signal based on the abdominal or chest motion signal and determines the patient's respiratory movement; S3 controls the output of the electrical stimulation signal according to the patient's breathing action to perform electrical stimulation on the patient.

[0040] In this embodiment, the three-axis sensor uses a three-axis acceleration sensor, model: LIS3DH, manufacturer: STMicroelectronics (STMicroelectronics, referred to as ST), the monitoring judgment logic is: S101 sets the range of the three-axis sensor; S102 collects motion data in the X, Y, and Z axes within a time period of T; S103 analyzes and calculates the collected data to determine the optimal data channel; S104 generates a waveform diagram based on the optimal data channel.

[0041] It is known that a three-axis gravity sensor, a gyroscope sensor, or any two Chuanganqi sensors can be combined into a six-axis sensor for monitoring.

[0042] In this embodiment, the measuring range of the three-axis acceleration sensor is set to ±2g. Of course, in other embodiments, it can also be set to ±3g, ±4g, etc.

[0043] In this embodiment, the acquisition time T is set to 30s.

[0044] It is known that the acquisition time T can also be 15 seconds, 60 seconds, 90 seconds, or other acquisition times. The acquisition time T can be selected according to the accuracy of the sensor used.

[0045] According to clinical practice and the "Terminology of Respiratory Diseases", the normal respiratory cycle time can be set to 3 seconds. The longer the acquisition time T, the larger the amount of data collected, and the higher the accuracy of data processing. However, the patient's respiratory frequency and amplitude change dynamically, and it is difficult to achieve dynamic changes in data processing over a long period of time; if the acquisition time T is short, the amount of data collected is small, and the data accuracy will be reduced, but it is easier to achieve real-time synchronization, and real-time adjustment can be made according to the patient's respiratory status, resulting in better human-machine synchronization.

[0046] Reference Figure 2 As shown, the three-axis sensor collects the motion signals of the abdomen or chest accompanying the breathing movement within the time T, and forms three groups of motion signals after filtering and amplifying the collected signals. The group of signals with the least interference and the best reflection of the breathing movement is regarded as the valid signal, and the corresponding data channel is determined as the optimal data channel. In this embodiment, the Z-axis data is superior to the X-axis and Y-axis data, and the Z-axis data is determined to be the optimal channel. A waveform is generated based on the Z-axis data, and the X-axis and Y-axis data are regarded as invalid data.

[0047] It can be known that the filtering method can use Kalman filtering, wavelet filtering, low-pass filtering, or other filtering methods to filter out cluttered signals from the collected data and obtain effective signals; signal amplification is performed by amplifying the collected action signals through an amplification circuit.

[0048] Reference Figure 3 As shown in FIG, the specific process of analyzing the collected abdominal or chest motion signal is as follows: S201 collects all peak and trough data in the waveform into one data set; S202 performs rounding processing on the peaks and troughs in the data set to select the peaks and troughs with the most occurrences; S203: Based on the peaks and troughs that appear the most times, select the smallest peak and the smallest trough as the reference peak and reference trough; S204 sets a first inhalation threshold and a first exhalation threshold based on the reference peak and the reference trough; S205 sets the reference slope k0 based on the waveform diagram and calculates the slope k of the motion data obtained at time t. t ; S206 obtains the patient's breathing signal according to the first inhalation threshold, the first exhalation threshold and the reference slope k0; S207 is based on the respiratory signal and the slope k of the motion data at time t t Determine the patient's breathing movements.

[0049] In this embodiment, all peak and trough data collected within the time T are processed by rounding down, and then filtered. The rounding down is specifically as follows: Where: ƒ′ is the peak or trough value after rounding; ƒ is the peak or trough value in the data set; N is the rounding digit 100; All peaks and troughs are rounded to the nearest hundred and then screened to select the peaks and troughs that appear the most times within time T. The monitoring data of the screened peaks and troughs before rounding are used to establish a sub-dataset. From the sub-dataset, the minimum peak is selected as the benchmark peak, and the minimum trough is selected as the benchmark trough.

[0050] In other embodiments, the rounding digit N may also be 10, and all peaks and troughs in the data set are rounded to the nearest ten before screening.

[0051] It is known that the peak and trough data can also be rounded up, specifically: Where: ƒ′ is the peak or trough value after rounding; ƒ is the peak or trough value in the data set; N is the rounding digit 100.

[0052] There is no essential difference between rounding down and rounding up, and the peaks and troughs screened out are the same.

[0053] In this embodiment, the first exhalation threshold is set to: Where: 呼 is the first exhalation threshold; ƒ 峰 is the reference peak; 谷 is the reference trough; a is the setting coefficient; The first inhalation threshold is set as: Where: 吸 is the first inspiratory threshold; ƒ 峰 is the reference peak; 谷 is the reference trough; a is the setting coefficient.

[0054] like Figure 4 In one embodiment shown, the coefficient a in the first inhalation threshold and the first exhalation threshold is 0.2; Filter out the benchmark peak 峰 is 528; filter out the benchmark trough ƒ 谷 is 273; the first exhalation threshold can be calculated as λ 呼 =477; the first inhalation threshold is λ 吸 =324.

[0055] It can be known that the coefficient a is not limited to 0.2, and can be set to 0.05, 0.1, 0.3 or other values ​​according to the sensor accuracy and control requirements. The smaller the coefficient a, the higher the requirements for sensor accuracy and sensitivity, the better the synchronization of electrical stimulation applied to the patient, the longer the electrical stimulation time in a single breathing cycle, and the better the treatment effect.

[0056] In a further embodiment, the slope k of the motion data at time t t The slope k at time t is calculated by the obtained abdominal or chest motion data. Specifically, n abdominal or chest motion data are uploaded every t1 time period, and the slope k at time t is calculated using the abdominal or chest motion data of the latest m t1 time periods each time. t ; The reference slope k0 ranges from 0.2 to 2.

[0057] Among them: the time period t1 is not limited and can be 50ms, 100ms, 120ms or other time periods, as long as it is a given smaller time change; the number n of abdominal or chest motion data uploaded in the t1 time period is not limited and can be 3, 4, 5 or other numbers. In the most recent m t1 time periods, m represents the number of consecutive t1 time periods. The specific value is not limited and can be 1, 2, 3, 4 or other numbers.

[0058] In this embodiment, the t1 time period is 100ms, that is, 5 abdominal or chest motion data are collected and uploaded every 100ms, and the slope k is calculated using the data collected in the last 200ms. t .

[0059] In a shorter time period, the slope k t The calculation is approximately linear, and the calculation formula is: Where: k t is the slope of the current collected data; dg is the change in the motion signal collected during the time period t; dt is the time change.

[0060] In a further embodiment, the breathing signal includes: Inspiratory start signal, when the slope of the collected data k t When the slope is greater than the reference slope k0 and the abdominal or chest motion data is equal to the first inhalation threshold, it is determined to be an inhalation start signal; Inspiratory termination signal, when the slope of the collected data k t When the slope is greater than the reference slope k0 and the abdominal or chest motion data reaches a peak, it is determined as an inhalation termination signal; Exhalation start signal, when the slope of the collected data k t When the slope is less than the reference slope - k0 and the abdominal or chest motion data is equal to the first exhalation threshold, it is determined to be an inhalation start signal; Exhalation termination signal, when the slope of the collected data k t When the slope is less than the reference slope - k0 and the abdominal or chest motion data reaches a trough, it is determined as an inhalation termination signal.

[0061] In a further embodiment, the period between the collected inhalation start signal and the collected inhalation end signal is the inhalation action; the period between the collected exhalation start signal and the collected exhalation end signal is the exhalation action; and the period between two adjacent inhalation start signals is a respiratory cycle.

[0062] In a further embodiment, each respiratory cycle of the patient is recorded and compared with a preset standard respiratory cycle to analyze the patient's respiratory status, which includes: tachypnea, normopnea, bradypnea, etc.

[0063] In a further embodiment, the value of the reference slope k0 is automatically adjusted based on the respiratory state; When the respiratory state is judged as tachypnea, the value of k0 is set according to the standard deviation between the monitored average respiratory cycle and the preset standard respiratory cycle. The larger the standard deviation, the larger the value of k0. The maximum value is k0=2. When the respiratory state is determined to be bradypnea, the value of k0 is set according to the standard deviation between the monitored average respiratory cycle and the preset standard respiratory cycle. The larger the standard deviation, the smaller the value of k0. The minimum value is k0=0.2. In a further embodiment, since patients often make movements other than breathing, such as coughing, talking, swallowing, shaking, and leaning sideways during treatment, these movements may interfere with the motion signals collected by the three-axis sensor, which is reflected in the waveform as abnormal fluctuations. Therefore, it is necessary to determine the validity of the collected data. The determination principle is as follows: setting a second exhalation threshold and a second inhalation threshold; When the slope k of the collected data t When the slope is greater than the reference slope k0 and the amplitude is greater than the second exhalation threshold within time t, it is determined to be an invalid signal; When the slope k of the collected data t When the slope is greater than the reference slope k0 and the amplitude is greater than the first inhalation threshold and less than the second exhalation threshold within time t, it is determined to be a valid signal; When the slope k of the collected data t When the slope is less than the reference slope - k0 and the amplitude is less than the second inhalation threshold within time t, it is determined to be an invalid signal; When the slope k of the collected data t When the slope is less than the reference slope - k0 and the amplitude is greater than the first exhalation threshold and less than the second inhalation threshold within time t, it is determined to be a valid signal.

[0064] In this embodiment, because talking, coughing, shaking, and other actions are accompanied by body surface movement, these movements are more intense than normal breathing. Within 0.02 to 0.2 respiratory cycles, or even shorter, an amplitude higher than normal breathing can be achieved. Based on this, time t is set to 200ms. In practice, this can negate the interference signals caused by actions other than breathing, and electrical stimulation therapy is not performed during these actions. It is understood that time t is not limited to 200ms. Depending on the sensitivity and accuracy of the sensor and the treatment scenario, time t can be set within 50ms to 300ms.

[0065] In this embodiment, the second exhalation threshold value is expressed as: in: is the second exhalation threshold;峰 is the reference peak; 谷 is the reference trough; β is the setting coefficient; The second inhalation threshold expression is: in: is the second inspiratory threshold; ƒ 峰 is the reference peak; 谷 is the reference trough; β is the setting coefficient.

[0066] It should be noted that the first exhalation threshold, the first inhalation threshold, the second exhalation threshold, and the second inhalation threshold are collectively referred to as the respiratory threshold, among which the first exhalation threshold and the first inhalation threshold are the first respiratory threshold, and the second exhalation threshold and the second inhalation threshold are the second respiratory threshold; among which the coefficient β in the expressions of the second exhalation threshold and the second inhalation threshold is set to a value of 0.4~1.

[0067] In this embodiment, the coefficient β is set to 0.5.

[0068] In a further embodiment, the logic for controlling the output of the electrical stimulation signal is specifically as follows: When receiving the inspiration start signal, it sends out the phrenic nerve electrical stimulation signal to output electrical stimulation to the phrenic nerve; When receiving the inhalation termination signal, a stop electrical stimulation signal is sent to stop outputting electrical stimulation; When receiving the exhalation start signal, it sends out an abdominal muscle electrical stimulation signal to output electrical stimulation to the abdominal muscles; When the exhalation termination signal is received, a stop electrical stimulation signal is sent to stop outputting electrical stimulation.

[0069] Apply electrical stimulation to the phrenic nerve to stimulate the diaphragm to contract and assist in inspiration; Electrical stimulation is applied to the abdominal muscles to stimulate their contraction, increase abdominal pressure, reposition the diaphragm, and assist exhalation.

[0070] In a further embodiment, the abdominal muscles include the rectus abdominis and the lower abdominal muscles, and when the abdominal muscle electrical stimulation signal is issued, electrical stimulation can be selectively output to either the rectus abdominis or the lower abdominal muscles; You can also choose to output electrical stimulation to the rectus abdominis and lower abdominal muscles at the same time.

[0071] In a further embodiment, the respiratory threshold is a dynamic threshold, specifically: continuously monitoring the patient's abdominal or chest movement data, performing a threshold calculation on the data collected within each time T, and performing respiratory signal judgment according to the previously calculated threshold within the subsequent time T. At the same time, the collected abdominal / chest movement data is uploaded as a new data set and the threshold is recalculated, and the respiratory signal is judged according to the recalculated threshold within the next time T.

[0072] like Figure 5 As shown, an embodiment of a respiratory nerve muscle stimulation system is used to implement the respiratory nerve muscle stimulation method of the present invention, comprising: Three-axis sensor 3: attached to the patient's chest or abdomen where the movement is obvious with breathing, used to monitor the patient's chest or abdomen movement signals with breathing and upload the collected data to the signal acquisition unit; Signal acquisition unit 2: collects the patient's respiratory data through a multi-parameter sensor, and uploads the collected signals to the data processing unit after aggregation Data processing unit 11: receives the motion signal data uploaded by the signal acquisition unit, processes and analyzes the received data, and transmits the analysis results to the control unit; Control unit 12: controls the stimulation electrodes to output electrical stimulation according to the analysis results sent by the data processing unit; Stimulation electrode 4: receives the electrical stimulation signal from the control unit and performs electrical stimulation treatment on the patient.

[0073] Reference Figure 6 As shown, the specific operation process is as follows: the patient lies flat on the treatment bed, the three-axis sensor 3 is attached to the patient's abdomen just above or on either side of the navel, the phrenic nerve electrode 41 of the physical therapy stimulation electrode 4 is attached to the corresponding treatment position of the phrenic nerve on the patient's body surface, and the abdominal muscle electrode 42 is attached to the corresponding treatment position of the abdominal muscle on the patient's body surface. The three-axis sensor 3 and the stimulation electrode 4 are both connected to the treatment host 1. The treatment host 1 is internally provided with a data processing unit 11 and a control unit 12. The control unit 12 is preset with a respiratory threshold judgment logic, a motion signal slope calculation logic, an electrical stimulation control logic, and a reference slope k0; After the treatment host 1 is turned on, the signal acquisition unit 2 first collects 30 seconds of motion signals generated by the patient's abdomen with breathing movements through the three-axis sensor 3, and uploads the signals to the data processing unit 11. The data processing unit 11 performs noise reduction and amplification processing on the received signals, and selects the best data channel from the three-axis data as valid data for analysis, such as Figure 2 As shown, the Z-axis data is selected as valid data, the X-axis and Y-axis data are selected as invalid data, and only the Z-axis data is analyzed; The data processing unit 11 generates a waveform graph from the collected 30 seconds of data, and classifies all peaks and troughs into a data set, rounds down these peaks and troughs, and then filters out the peaks and troughs that appear most frequently, and classifies these peaks and troughs into a sub-data set, from which the minimum peak and minimum trough are selected as reference peak and reference trough; analyzes the patient's respiratory cycle through the waveform graph, and compares it with a preset standard respiratory cycle to determine the patient's respiratory state, and obtains a first exhalation threshold, a first inhalation threshold, a second exhalation threshold, and a second inhalation threshold based on the patient's respiratory state, the reference peak, and the reference trough; 30 seconds after the start, the data collected by the signal acquisition unit 2 through the triaxial sensor 3 continues to be uploaded to the data processing unit 11, and the data processing unit 11 calculates the current slope k of the newly collected data. t , when k t > k0, and when the current signal value is equal to the first inspiratory threshold, it is determined that inspiration has begun. The data processing unit 11 transmits the determination result to the control unit 12. The control unit 12 issues a phrenic nerve electrical stimulation signal to control the phrenic nerve electrode 41 to discharge and electrically stimulate the phrenic nerve, thereby stimulating the phrenic nerve to drive the diaphragm to contract and assist the patient in inhaling. When the data processing unit 11 analyzes and collects a signal that reaches a peak value, it is determined to be an inspiration stop signal, and the control unit 12 sends a stop electrical stimulation signal to control the phrenic nerve electrode 41 to stop discharging; When k t < -k0, and the current signal value is equal to the first exhalation threshold, it is determined that exhalation has begun, and the control unit 12 sends an abdominal muscle electrical stimulation signal to control the abdominal muscle electrodes 42 to discharge and electrically stimulate the abdominal muscles, stimulating abdominal muscle contraction, compressing the abdominal cavity, helping the diaphragm to move closer to the chest, and assisting the patient in exhaling; When the data processing unit 11 analyzes and collects a signal that reaches a valley value, it is determined to be an exhalation stop signal, and the control unit 12 sends a stop electrical stimulation signal to control the abdominal muscle electrodes 42 to stop discharging; The data processing unit 11 collects the data collected by the signal collection unit 2 into a data set every 30 seconds, recalculates the respiratory threshold and overwrites the previous respiratory threshold, and the respiratory signal is judged based on the latest respiratory threshold.

[0074] The lower abdominal muscles mentioned in the present invention refer to the lower part of the abdominal muscles. More specifically, the stimulation position of the lower abdominal muscles is between the outer edge of the rectus abdominis and the inguinal ligament, or the intersection of the outer edge of the rectus abdominis and the midline of the abdomen. Of course, there are other stimulation points, which are not listed here one by one.

[0075] Although the preferred embodiments of the present invention have been described above in conjunction with the accompanying drawings, the present invention is not limited to the above-mentioned specific embodiments. The above-mentioned specific embodiments are merely illustrative and not restrictive. Under the guidance of the present invention, ordinary technicians in this field can also make many forms of specific changes without departing from the scope of protection of the invention and the claims. These all fall within the scope of protection of the present invention.

Claims

1. A respiratory nerve muscle stimulation method, characterized in that: The following steps are involved: S1 uses a three-axis sensor to obtain the motion signal generated by the patient's abdomen or chest during breathing; S2 analyzes the patient's respiratory signal based on the abdominal or chest motion signal and determines the patient's respiratory movement; S3 controls the output of the electrical stimulation signal according to the patient's breathing action to perform electrical stimulation on the patient.

2. The respiratory nerve muscle stimulation method according to claim 1, characterized in that: In step S1, the three-axis sensor includes a three-axis gravity sensor and a three-axis acceleration sensor, and the specific judgment logic is: S101 sets the range of the three-axis sensor; S102 collects motion data in the X, Y, and Z axes within a time period of T; S103 analyzes and calculates the collected data to determine the optimal data channel; S104 generates a waveform diagram based on the optimal data channel.

3. The respiratory nerve muscle stimulation method according to claim 2, characterized in that: The step S2 is specifically as follows: S201 collects all peak and trough data in the waveform into one data set; S202 performs rounding processing on the peaks and troughs in the data set to select the peaks and troughs with the most occurrences; S203: Based on the peaks and troughs that appear the most times, select the smallest peak and the smallest trough as the reference peak and reference trough; S204 sets a first inhalation threshold and a first exhalation threshold based on the reference peak and the reference trough; S205 sets the reference slope k0 based on the waveform diagram and calculates the slope k of the motion data obtained at time t. t ; S206 obtains the patient's breathing signal according to the first inhalation threshold, the first exhalation threshold and the reference slope k0; S207 is based on the respiratory signal and the slope k of the motion data at time t t Determine the patient's breathing movements.

4. The respiratory nerve muscle stimulation method according to claim 3, characterized in that: In step S202, the peaks and troughs are rounded down, specifically: Where: ƒ′ is the peak or trough value after rounding; ƒ is the peak or trough value in the data set; N is the number of digits to be rounded, which can be 10 or 100; Alternatively, the peaks and troughs can be rounded up, as follows: Where: ƒ′ is the peak or trough value after rounding; ƒ is the peak or trough value in the data set; N is the number of digits to be rounded, which can be 10 or 100.

5. The respiratory nerve muscle stimulation method according to claim 4, characterized in that: The first exhalation threshold is set as: Where: 呼 is the first exhalation threshold; ƒ 峰 is the reference peak; 谷 is the reference trough; a is the setting coefficient; The first inhalation threshold is set as: Where: 吸 is the first inspiratory threshold; ƒ 峰 is the reference peak; 谷 is the reference trough; a is the setting coefficient.

6. The respiratory nerve muscle stimulation method according to claim 5, characterized in that: In step S205, the reference slope k0 is set to 0.2~2; the motion data slope k t The slope k is calculated based on the obtained abdominal or chest motion data. Specifically, n abdominal or chest motion data are uploaded every t1 time period, and the slope k is calculated using the abdominal or chest motion data of the most recent m t1 time periods each time. t ; The slope k t The calculation formula is: Where: k t is the slope of the current collected data; dg is the change in the motion signal collected during the t1 period; dt is the time change.

7. The respiratory nerve muscle stimulation method according to claim 6, characterized in that: In step S206, the respiratory signal includes: Inspiratory start signal, when the slope of the collected data k t When the slope is greater than the reference slope k0 and the abdominal or chest motion data is equal to the first inhalation threshold, it is determined to be an inhalation start signal; Inspiratory termination signal, when the slope of the collected data k t When the slope is greater than the reference slope k0 and the abdominal or chest motion data reaches a peak, it is determined as an inhalation termination signal; Exhalation start signal, when the slope of the collected data k t When the slope is less than the reference slope - k0 and the abdominal or chest motion data is equal to the first exhalation threshold, it is determined to be an inhalation start signal; Exhalation termination signal, when the slope of the collected data k t When the slope is less than the reference slope - k0 and the abdominal or chest motion data reaches a trough, it is determined as an inhalation termination signal.

8. The respiratory nerve muscle stimulation method according to claim 7, characterized in that: The step S207 specifically includes: the period from the collected inhalation start signal to the collected inhalation end signal is the inhalation action; the period from the collected exhalation start signal to the collected exhalation end signal is the exhalation action; and the period between two adjacent inhalation start signals is a respiratory cycle.

9. The respiratory nerve muscle stimulation method according to claim 8, characterized in that: The step S2 also includes determining the validity of the respiratory signal, and the determination principle is: setting a second exhalation threshold and a second inhalation threshold; When the slope k of the collected data t When the slope is greater than the reference slope k0 and the amplitude is greater than the second exhalation threshold within time t, it is determined to be an invalid signal; When the slope k of the collected data t When the slope is greater than the reference slope k0 and the amplitude is greater than the first inhalation threshold and less than the second exhalation threshold within time t, it is determined to be a valid signal; When the slope k of the collected data t When the slope is less than the reference slope - k0 and the amplitude is less than the second inhalation threshold within time t, it is determined to be an invalid signal; When the slope k of the collected data t When the slope is less than the reference slope - k0 and the amplitude is greater than the first exhalation threshold and less than the second inhalation threshold within time t, it is determined to be a valid signal.

10. The respiratory nerve muscle stimulation method according to claim 9, characterized in that: The second exhalation threshold expression is: in: is the second exhalation threshold; 峰 is the reference peak; 谷 is the reference trough; β is the setting coefficient; The second inhalation threshold expression is: in: is the second inspiratory threshold; ƒ 峰 is the reference peak; 谷 is the reference trough; β is the setting coefficient.

11. The respiratory nerve muscle stimulation method according to claim 10, characterized in that: In step S3, the logic for controlling the output of the electrical stimulation signal is specifically as follows: When receiving the inspiration start signal, it sends out the phrenic nerve electrical stimulation signal to output electrical stimulation to the phrenic nerve; When receiving the inhalation termination signal, a stop electrical stimulation signal is sent to stop outputting electrical stimulation; When receiving the exhalation start signal, it sends out an abdominal muscle electrical stimulation signal to output electrical stimulation to the abdominal muscles; When the exhalation termination signal is received, a stop electrical stimulation signal is sent to stop outputting electrical stimulation.

12. The respiratory nerve muscle stimulation method according to claim 11, characterized in that: The abdominal muscles include the rectus abdominis and the lower abdominal muscles. When an abdominal muscle electrical stimulation signal is sent, electrical stimulation is output to either the rectus abdominis or the lower abdominal muscles, or electrical stimulation is output to both the rectus abdominis and the lower abdominal muscles at the same time.

13. The respiratory nerve muscle stimulation method according to any one of claims 4 to 12, characterized in that: The first inhalation threshold, the first exhalation threshold, the second inhalation threshold, and the second exhalation threshold are all dynamic thresholds, specifically: continuously monitoring the patient's abdominal or chest movement data, performing a threshold calculation on the data collected within each time T, and performing respiratory signal judgment according to the previously calculated threshold within the subsequent time T. At the same time, the collected abdominal or chest movement data is uploaded as a new data set and the threshold is recalculated, and the respiratory signal is judged according to the recalculated threshold within the next time T.

14. A respiratory nerve muscle stimulation system for implementing the respiratory nerve muscle stimulation method according to any one of claims 1 to 13, characterized in that: include: Three-axis sensor: used to monitor the patient's chest or abdomen motion signals accompanying breathing movements and upload the collected data to the signal acquisition unit; Signal acquisition unit: collects the patient's respiratory data through a multi-parameter sensor, and uploads the collected signals to the data processing unit after aggregation; Data processing unit: receives the motion signal data uploaded by the signal acquisition unit, processes and analyzes the received data, and transmits the analysis results to the control unit; Control unit: controls the stimulation electrodes to output electrical stimulation according to the analysis results sent by the data processing unit; Stimulation electrode: receives the electrical stimulation signal from the control unit and performs electrical stimulation on the patient.

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