Electrical stimulation signal control method and device, nerve stimulation equipment, readable storage medium and program product

Abstract

The invention relates to an electrical stimulation signal control method and device, nerve stimulation equipment, a readable storage medium and a program product. The method comprises the following steps: acquiring a respiratory signal and an electromyographic signal; frequency characteristics of the electromyographic signals are extracted, and respiratory frequency characteristics are obtained through calculation; based on the respiratory frequency characteristics, extracting a target respiratory signal in the respiratory signals; and adjusting parameter information of the target electrical stimulation signal based on the target respiration signal. By adopting the method, the objectivity and adaptability of the electrical stimulation parameters can be improved.

Description

Technical Field

[0001] This application relates to the field of medical device technology, and in particular to an electrical stimulation signal control method, device, nerve stimulation device, readable storage medium, and program product. Background Technology

[0002] Neurostimulation devices are a crucial aspect of current medical technology, with wide-ranging applications. However, current techniques mostly follow preset programs, applying electrical stimulation with fixed parameters. This static stimulation mode has two drawbacks: firstly, it is difficult to adapt to the differences in physiological functions among individuals, leading to varying stimulation effects; secondly, it lacks adaptability to the dynamic changes in the same subject's physical and mental state, resulting in a decline in efficacy over long-term use. Traditional lingual nerve stimulators can be broadly divided into open-loop and closed-loop stimulators. Open-loop stimulators operate by following a pre-set stimulation program or cycle, applying stimulation with fixed parameters. A single biosensor, such as a respiratory sensor or electromyography sensor, can be integrated to collect and analyze respiratory signals, and then perform periodic stimulation based on these signals. The advantage of this type of stimulator lies in its simple control logic and high system reliability. However, its limitations are also obvious: the stimulation effect exhibits significant individual differences, and for the same subject, the continuous effectiveness of the parameters is poor, easily decreasing over time. While closed-loop control technology has made progress, it also has deep-seated drawbacks. The most prominent is its high dependence on a single biosensor. For example, if the system relies solely on signals from a respiratory sensor or electromyography (EMG) sensor, the overall system performance will be severely affected if the sensor's performance is limited, the signal is interfered with, or the physiological state it reflects is insufficient to fully characterize the stimulation effect. This dependence on a single sensor significantly reduces the robustness and generalization ability of the closed-loop system. A more critical and deeper drawback lies in the complex and ambiguous conversion algorithm problem between single biosensor signals and stimulation effects. The signals obtained by the sensors are often indirect reflections of the subject's physiological activities, and their characteristics lack a direct and clear causal relationship with the actual desired electrical stimulation effect. Therefore, to convert these indirect signals into effective and accurate stimulation parameters, highly complex conversion algorithms are necessary. Designing such algorithms is not only time-consuming and labor-intensive but also requires extensive experimental data to calibrate their accuracy. However, it is precisely because of the ambiguity of the correlation between signals and effects that even with the most ingenious design, such complex algorithms are difficult to achieve precise mapping and synchronous control of stimulus parameters in practical applications, resulting in poor stimulus effectiveness and synchronization accuracy. Summary of the Invention

[0003] Therefore, it is necessary to provide an electrical stimulation signal control method, device, neurostimulation device, readable storage medium, and program product that can improve the targeting and adaptability of electrical stimulation parameters in response to the above-mentioned technical problems.

[0004] In a first aspect, the present application provides an electrical stimulation signal control method, comprising:

[0005] obtaining a respiration signal and an electromyography signal;

[0006] extracting a frequency feature of the electromyography signal, and calculating a respiration frequency feature;

[0007] based on the respiration frequency feature, extracting a target respiration signal from the respiration signal;

[0008] based on the target respiration signal, adjusting parameter information of a target electrical stimulation signal.

[0009] In one embodiment, the step of extracting a frequency feature of the electromyography signal, and calculating a respiration frequency feature specifically comprises:

[0010] obtaining an interval between adjacent wave crests in an envelope of the electromyography signal;

[0011] obtaining a sampling frequency of the electromyography signal, and based on the interval and the sampling frequency, extracting a frequency feature of the electromyography signal;

[0012] based on the frequency feature of the electromyography signal, calculating a respiration frequency feature.

[0013] In one embodiment, the step of based on the interval and the sampling frequency, extracting a frequency feature of the electromyography signal specifically comprises:

[0014] substituting the interval and the sampling frequency into a first formula to calculate the frequency feature of the electromyography signal, the first formula comprising:

[0015] ;

[0016] wherein, is the frequency feature of the electromyography signal, is the interval between adjacent wave crests in the envelope of the electromyography signal, is the sampling frequency of the electromyography signal.

[0017] In one embodiment, the step of based on the target respiration signal, adjusting parameter information of a target electrical stimulation signal specifically comprises:

[0018] obtaining an expiration point and an inspiration point in the target respiration signal;

[0019] based on the expiration point and the inspiration point, adjusting parameter information of a target electrical stimulation signal.

[0020] In one of the embodiments, the step of adjusting the parameter information of the target electrical stimulation signal based on the expiration point and the inspiration point specifically comprises:

[0021] adjusting the timing information of the target electrical stimulation signal based on the expiration point and the inspiration point.

[0022] In one of the embodiments, the step of extracting the target respiratory signal from the respiratory signal based on the respiratory frequency feature specifically comprises:

[0023] performing a polynomial least square fitting on the respiratory signal to obtain a fitted respiratory signal;

[0024] performing a band-pass filtering on the fitted respiratory signal based on the respiratory frequency feature to obtain the target respiratory signal.

[0025] In one of the embodiments, the step of performing a polynomial least square fitting on the respiratory signal specifically comprises:

[0026] performing a polynomial least square fitting on the respiratory signal with the optimization objective of minimizing the residual sum of squares, and the calculation formula of the residual sum of squares comprises:

[0027] ;

[0028] wherein, is the residual sum of squares, is the ordinate value of the respiratory signal at the abscissa , is the fitted value at the abscissa , , , , and are constants.

[0029] In one of the embodiments, the step of performing a band-pass filtering on the fitted respiratory signal based on the respiratory frequency feature specifically comprises:

[0030] performing the filtering on the fitted respiratory signal by using a band-pass filter allowing a frequency range of 0.8 times the respiratory frequency feature to 1.2 times the respiratory frequency feature.

[0031] In one of the embodiments, after the respiratory signal and the electromyography signal are acquired, the following steps are included:

[0032] extracting a target electromyography signal from the electromyography signal based on the respiratory signal;

[0033] adjusting the parameter information of the target electrical stimulation signal based on the target electromyography signal.

[0034] In one embodiment, the method further comprises, after obtaining the respiration signal and the electromyography signal:

[0035] extracting a target electromyography signal from the electromyography signal based on the respiration signal;

[0036] adjusting parameter information of a target electrical stimulation signal based on the target electromyography signal.

[0037] In a second aspect, the present application provides an electrical stimulation signal control device, comprising:

[0038] a signal obtaining module configured to obtain a respiration signal and an electromyography signal;

[0039] a feature extracting module configured to extract a frequency feature of the electromyography signal and calculate a respiration frequency feature;

[0040] a signal extracting module configured to extract a target respiration signal from the respiration signal based on the respiration frequency feature;

[0041] a parameter adjusting module configured to adjust parameter information of a target electrical stimulation signal based on the target respiration signal.

[0042] In a third aspect, the present application provides a neural stimulation device, comprising a respiration signal collecting device, an electromyography signal collecting device, an electrical stimulation signal generating device, a memory and a processor;

[0043] the respiration signal collecting device is configured to collect a respiration signal;

[0044] the electromyography signal collecting device is configured to collect an electromyography signal;

[0045] the electrical stimulation signal generating device is configured to generate a target electrical stimulation signal;

[0046] the memory stores a program, and the processor implements the steps of the method of any one of the above aspects when executing the program.

[0047] In a fourth aspect, the present application provides a readable storage medium, which stores a program, and the program implements the steps of the method of any one of the above aspects when executed by a processor.

[0048] In a fifth aspect, the present application provides a program product, comprising a program, and the program implements the steps of the method of any one of the above aspects when executed by a processor.

[0049] The above electric stimulation signal control method, device, neural stimulation apparatus, readable storage medium and program product can obtain multi-dimensional sensor signals (respiration signals and electromyography signals), extract a signal component in the respiration signal that matches the respiration rhythm contained in the electromyography signal, make the signal component more pure and accurate, and use the signal component as a basis for adjusting the electric stimulation parameters, so that dynamic matching between the electric stimulation parameters and the individual state can be achieved, and the target and adaptability of the electric stimulation parameters are improved. BRIEF DESCRIPTION OF DRAWINGS

[0050] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related art, the drawings needed to be used in the description of the embodiments of the present application or the related art will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other related drawings can be obtained by those skilled in the art without creative effort on the basis of these drawings.

[0051] Figure 1 It is an internal structure diagram of the neural stimulation apparatus in one embodiment.

[0052] Figure 2 It is a flowchart of the electric stimulation signal control method in one embodiment.

[0053] Figure 3 It is a flowchart of step 102 in one embodiment.

[0054] Figure 4 It is a flowchart of step 104 in one embodiment.

[0055] Figure 5 It is a flowchart of step 103 in one embodiment.

[0056] Figure 6 It is a flowchart of the electric stimulation signal control method in another embodiment.

[0057] Figure 7 It is a flowchart of the electric stimulation signal control method in still another embodiment.

[0058] Figure 8 It is a structural block diagram of the electric stimulation signal control device in one embodiment. DETAILED DESCRIPTION

[0059] In order to make the purposes, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and not to limit the present application.

[0060] It should be noted that the terms "first", "second", etc. used in the present application can be used to describe various elements, but these elements are not limited by these terms. These terms are only used to distinguish the first element from the second element. The terms "include" and "have" and any variations thereof used in the present application are intended to cover non-exclusive inclusion. The term "a plurality of" used in the present application refers to two or more. The term "and / or" used in the present application refers to one of the options or any combination of the options.

[0061] In the related art, a single sensor signal (e.g., a respiration signal, an electromyography signal) is usually relied on as a control basis. Since the characteristics of the single source signal are susceptible to interference, it is usually necessary to map to the ideal stimulation parameters through a complex conversion algorithm, and the correlation between the signal characteristics and the stimulation effect is not clear enough.

[0062] Based on this, the embodiment of the present application provides a neural stimulation device. As shown in the figure, the neural stimulation device 00 includes a respiration signal acquisition device 01, an electromyography signal acquisition device 02, an electrical stimulation signal generation device 03, and a processor 04. Wherein: Figure 1

[0063] The respiration signal acquisition device 01 is used to acquire a respiration signal.

[0064] The electromyography signal acquisition device 02 is used to acquire an electromyography signal.

[0065] The electrical stimulation signal generation device 03 is used to generate a target electrical stimulation signal.

[0066] The processor 04 is used to acquire the respiration signal and the electromyography signal; extract the frequency characteristics of the electromyography signal, and calculate the respiration frequency characteristics; extract a target respiration signal in the respiration signal based on the respiration frequency characteristics; and adjust the parameter information of the target electrical stimulation signal based on the target respiration signal.

[0067] Further, the processor 04 can send the adjusted parameter information to the electrical stimulation signal generation device 03, so that the electrical stimulation signal generation device 03 generates the target electrical stimulation signal according to the parameter information.

[0068] ​Specifically, the processor 04 can be communicatively connected with the respiratory signal acquisition device 01, the electromyography signal acquisition device 02, and the electrical stimulation signal generation device 03. The respiratory signal acquisition device 01 can be integrated in the extracorporeal part of the neural stimulation apparatus 00, for acquiring data of the frequency, amplitude, airflow, etc. of respiration and converting into digital signals transmitted to the processor 04. The electromyography signal acquisition device 02 can be integrated in the extracorporeal part of the neural stimulation apparatus 00, for acquiring data of the activity of the muscle related to respiratory activity and converting into digital signals transmitted to the processor 04. The electrical stimulation signal generation device 03 can be integrated in the extracorporeal part of the neural stimulation apparatus 00, for generating electrical stimulation pulse signals and transmitting to the intracorporeal electrode part through wireless energy transmission.

[0069] In one exemplary embodiment, as shown in Figure 2 , an electrical stimulation signal control method is provided, which is applied to the processor 04 in Figure 1 for illustration, including the following steps 101 to 104. Among them:

[0070] Step 101, obtaining respiratory signals and electromyography signals.

[0071] Among them, the respiratory signals can be the original respiratory signals of the target object collected by the respiratory signal acquisition device. The electromyography signals can be the electrical activity signals of the muscle related to respiratory activity of the target object collected by the electromyography signal acquisition device.

[0072] Step 102, extracting the frequency feature of the electromyography signals and calculating the respiratory frequency feature.

[0073] Among them, the respiratory frequency feature is a feature for characterizing the respiratory rhythm of the target object, and its unit can be hertz or times per minute. It can be understood that the muscle activity accompanying the respiratory process will produce a rhythmic feature in the electromyography signals that is synchronized with the respiratory cycle, so the frequency feature of the electromyography signals has relevance with the respiratory frequency.

[0074] Exemplarily, the processor 04 can first pre-process the electromyography signals, and then extract the frequency feature of the electromyography signals. The pre-processing method of the electromyography signals includes but is not limited to band-pass filtering of the electromyography signals to retain the effective frequency band of the electromyography signals, rectification and smoothing processing of the electromyography signals to highlight the amplitude variation trend of the electromyography signals. The method of extracting the frequency feature of the electromyography signals can include spectral analysis method. Specifically, the electromyography signals can be subjected to fast Fourier transform or power spectral density estimation to obtain the frequency spectrum of the electromyography signals; within a pre-set respiratory frequency range (e.g. natural respiratory frequency range), the main peak or energy concentrated frequency band in the frequency spectrum is identified, and the center frequency thereof is determined as the frequency feature of the electromyography signals.

[0075] Step 103, based on the breathing frequency feature, extracting a target breathing signal from the breathing signal.

[0076] The target breathing signal refers to a signal component extracted from the original breathing signal and related to the current breathing rhythm of the target subject. It can be understood that this extraction process eliminates signal components that do not match the breathing frequency feature, thereby obtaining a target breathing signal that is purer than the original breathing signal.

[0077] For example, the processor 04 can obtain the target breathing signal by retaining signal components in the breathing signal that have a frequency close to the breathing frequency feature.

[0078] Step 104, based on the target breathing signal, adjusting the parameter information of the target electrical stimulation signal.

[0079] The target electrical stimulation signal can be used to remind the target subject. The parameter information of the target electrical stimulation signal includes but is not limited to the timing of the electrical stimulation (e.g., start time, duration, stop time, etc.), intensity (e.g., current or voltage amplitude, etc.), frequency (e.g., pulse repetition frequency, etc.), and waveform.

[0080] In the above electrical stimulation signal control method, by obtaining multi-dimensional sensor signals (breathing signal and electromyography signal), according to the breathing rhythm contained in the electromyography signal, the signal component in the breathing signal that matches it is extracted, making it purer and more accurate, and used as the basis for adjusting the electrical stimulation parameters, which can realize the dynamic matching of electrical stimulation parameters and individual state, and improve the target and adaptability of electrical stimulation parameters. For example, the purer and more accurate breathing signal extracted can be used to extract effective muscle movement data from the electromyography signal, further analyze and determine the neural electric field distribution and muscle movement effect, and adjust the electrical stimulation parameters accordingly.

[0081] In one exemplary embodiment, as shown in Figure 3 The above-mentioned step 102 includes:

[0082] Step 201, obtaining the interval between adjacent peaks in the envelope of the electromyography signal.

[0083] Step 202, obtaining the sampling frequency of the electromyography signal, and based on the interval and the sampling frequency, extracting the frequency feature of the electromyography signal.

[0084] The frequency feature of the electromyography signal can be negatively correlated with the interval between adjacent peaks in the envelope of the electromyography signal, and positively correlated with the sampling frequency of the electromyography signal.

[0085] Step 203, calculating the breathing frequency feature according to the frequency feature of the electromyography signal.

[0086] Exemplarily, the frequency feature of the myoelectric signal can be directly taken as the respiration frequency feature, or a plurality of frequency features calculated continuously are smoothed and then taken as the respiration frequency feature.

[0087] Optionally, in the step of extracting the frequency feature of the myoelectric signal based on the interval and the sampling frequency, the step specifically comprises:

[0088] The interval and the sampling frequency are substituted into the first formula to calculate the frequency feature of the myoelectric signal, and the first formula comprises:

[0089] (1)

[0090] Wherein, is the frequency feature of the myoelectric signal. may be used to represent the basic frequency (i.e. the number of cycles per second) extracted from the myoelectric signal and synchronized with the respiration rhythm, with the unit of hertz. may be used to represent the time interval between adjacent wave crests, with the unit of second. is the interval between adjacent wave crests in the envelope of the myoelectric signal. may be used to represent the number of sampling points between two adjacent wave crests. is the sampling frequency of the myoelectric signal. may represent the number of myoelectric signal samples collected per second, with the unit of hertz.

[0091] In the embodiment, by calculating the interval between adjacent wave crests in the envelope of the myoelectric signal and combining the sampling frequency, the direct and efficient extraction of the respiration frequency feature is realized, the calculation efficiency and real-time performance are improved, and the response speed of the electric stimulation parameter adjustment is further improved.

[0092] In an exemplary embodiment, as shown in Figure 4 the step 104 comprises:

[0093] Step 401, obtaining an exhalation point and an inhalation point in the target respiration signal.

[0094] Step 402, adjusting the parameter information of the target electric stimulation signal based on the exhalation point and the inhalation point.

[0095] Wherein, the exhalation point refers to a feature point in the target respiration signal representing the beginning of the exhalation phase or the peak exhalation, which can correspond to the inflection point or local maximum point where the signal waveform changes from rising to falling. The inhalation point refers to a feature point in the target respiration signal representing the beginning of the inhalation phase or the peak inhalation, which can correspond to the inflection point or local minimum point where the signal waveform changes from falling to rising. The exhalation point and the inhalation point can be used to represent the respiration rhythm of the target object. For example, based on the exhalation point and the inhalation point, the respiration phase of the target object can be determined.

[0096] Optionally, the step of adjusting the parameter information of the target electrical stimulation signal based on the expiration point and the inspiration point specifically comprises:

[0097] The timing information of the target electrical stimulation signal is adjusted based on the expiration point and the inspiration point. For example, a start signal of the target electrical stimulation signal can be generated in response to the acquisition of the inspiration point. A stop signal of the target electrical stimulation signal can be generated in response to the acquisition of the expiration point.

[0098] In this embodiment, by positioning the expiration point and the inspiration point in the target respiratory signal, the precise synchronization of the electrical stimulation parameters and the respiratory phase is achieved, so that the electrical stimulation parameters can be adjusted at the accurate time, and the timing accuracy and state correlation of the electrical stimulation parameters are improved.

[0099] In one exemplary embodiment, as shown in Figure 5 The step 103 comprises:

[0100] In step 301, the respiratory signal is subjected to polynomial least squares fitting to obtain a fitted respiratory signal.

[0101] Exemplarily, the respiratory signal can be subjected to polynomial least squares fitting with the weighted sum minimum as the optimization target. Optionally, the respiratory signal can be subjected to moving average filtering with a fixed-width sliding window to obtain the fitted respiratory signal.

[0102] In step 302, the fitted respiratory signal is subjected to band-pass filtering processing based on the respiratory frequency feature to obtain a target respiratory signal.

[0103] Exemplarily, a preset allowable deviation can be added or subtracted from the respiratory frequency feature to obtain an allowed passing frequency range; the fitted respiratory signal is subjected to band-pass filtering processing based on the allowed passing frequency range to obtain the target respiratory signal.

[0104] Optionally, the step of subjecting the respiratory signal to polynomial least squares fitting specifically comprises:

[0105] The respiratory signal is subjected to polynomial least squares fitting with the least residual sum of squares as the optimization target. The calculation formula of the residual sum of squares comprises:

[0106] (2)

[0107] wherein, is the residual sum of squares, is the ordinate value of the respiratory signal at the abscissa is the fitted value at the abscissa is the fitted value at the abscissa is the fitted value at the abscissa , , , and is constant. In this way, the elimination of non-linear baseline drift and high-frequency noise can be achieved.

[0108] Optionally, in the step of performing band-pass filtering on the fitted respiratory signal based on the respiratory frequency feature, the step specifically comprises:

[0109] The fitted respiratory signal is filtered by a band-pass filter allowing a pass frequency range of 0.8 times the respiratory frequency feature to 1.2 times the respiratory frequency feature. For example, the pass frequency range is 0.8 Hz to 1.2 Hz when the respiratory frequency feature is 1 Hz. The calculation formula can include:

[0110] (3)

[0111] wherein, is a lower limit cutoff frequency of the pass frequency range, is an upper limit cutoff frequency of the pass frequency range, is a frequency feature of the myoelectric signal (i.e. respiratory frequency feature). It can be understood that the lower limit cutoff frequency is obtained by multiplying the respiratory frequency feature by a proportionality coefficient less than 1 (0.8 or other), and the upper limit cutoff frequency is obtained by multiplying the respiratory frequency feature by a proportionality coefficient greater than 1 (1.2 or other). In this way, the pass frequency range can be dynamically adjusted following the fast and slow dynamics of the respiratory frequency: when the respiratory frequency is faster, the passband range is correspondingly widened; when the respiratory frequency is slower, the passband range is correspondingly narrowed, improving the accuracy of filtering and signal quality.

[0112] In the embodiment, the preliminary denoising of the respiratory signal is achieved by polynomial least squares fitting, and then the purity of the respiratory signal is further improved by band-pass filtering centered on the respiratory frequency feature.

[0113] In an exemplary embodiment, as shown in Figure 6 the above-mentioned electrical stimulation signal control method further comprises:

[0114] Step 501: based on the respiratory signal, extracting a target myoelectric signal from the myoelectric signal.

[0115] Exemplarily, the respiratory signal and the myoelectric signal can be fused and filtered to extract a target myoelectric signal which is purer than the original myoelectric signal.

[0116] Step 502: based on the target myoelectric signal, adjusting the parameter information of the target electrical stimulation signal.

[0117] Optionally, in the step of adjusting the parameter information of the target electrical stimulation signal based on the target respiratory signal, the step specifically comprises:

[0118] Based on the target respiration signal and the target electromyography signal, the parameter information of the target electrical stimulation signal is adjusted.

[0119] In this embodiment, by extracting the pure target electromyography signal according to the respiration signal, the muscle activity interference irrelevant to the respiratory activity in the electromyography signal can be eliminated, and the control reliability is further improved.

[0120] In one exemplary embodiment, as shown in Figure 7 The above-mentioned electrical stimulation signal control method further comprises:

[0121] Step 601, obtaining the feedback signal of the target electrical stimulation signal.

[0122] The feedback signal includes but is not limited to sensor signal, sound signal.

[0123] Step 602, based on the feedback signal and the preset target feedback signal, adjusting the parameter information of the target electrical stimulation signal.

[0124] Exemplarily, the processor can adjust the parameter information of the target electrical stimulation signal when the deviation between the feedback signal and the preset target feedback signal is greater than the preset deviation.

[0125] Further, after adjusting the parameter information of the target electrical stimulation signal based on the feedback signal and the preset target feedback signal, the processor can execute the above-mentioned steps 101 to 104 again until the deviation between the feedback signal and the preset target feedback signal is less than the preset deviation.

[0126] In this embodiment, by adjusting the electrical stimulation parameter in a closed-loop control manner, individualized adaptive optimization is achieved.

[0127] In summary, in the above-mentioned electrical stimulation signal control method, by obtaining multi-dimensional sensor signals (respiration signal and electromyography signal), extracting the signal component in the respiration signal matched with the respiratory rhythm contained in the electromyography signal, making it more pure and accurate, and taking it as the basis for adjusting the electrical stimulation parameter, the dynamic matching of the electrical stimulation parameter and the individual state can be achieved, and the target and adaptability of the electrical stimulation parameter are improved. For details, please refer to Figure 1, the respiratory sensor (respiratory signal acquisition device 01) detects the respiratory signal and transmits to the processor 04, the electromyographic sensor (electromyographic signal acquisition device 02) detects the electromyographic signal and transmits to the processor 04, the processor 04 extracts the frequency characteristics of the electromyographic signal, and calculates the respiratory frequency characteristics (the way can be adopted to obtain the interval between adjacent wave crests in the envelope of the electromyographic signal; obtain the sampling frequency of the electromyographic signal, and based on the interval and the sampling frequency, extract the frequency characteristics of the electromyographic signal); based on the respiratory frequency characteristics, extract the target respiratory signal in the respiratory signal (the way can be adopted to perform polynomial least squares fitting on the respiratory signal to obtain a fitted respiratory signal; based on the respiratory frequency characteristics, perform band-pass filtering on the fitted respiratory signal, and the pass-through frequency range of the band-pass filtering can be 0.8 times the respiratory frequency characteristics to 1.2 times the respiratory frequency characteristics). Since the signal components irrelevant to the respiratory rhythm are eliminated, the processor 04 obtains a more pure and accurate respiratory signal. Further, the processor 04 matches the electrical stimulation parameters based on the extracted more pure and accurate respiratory signal (the way can be adopted to obtain the expiration point and the inspiration point in the target respiratory signal; based on the expiration point and the inspiration point, adjust the parameter information of the target electrical stimulation signal), and transmits to the electrical stimulation signal generation device 03, so that the electrical stimulation signal generation device 03 can generate more accurate electrical stimulation signals. At the same time, the feedback regulation system can be added according to the changes of the sensor signals collected before and after stimulation in a closed-loop control manner, so that the target biological sensor signal and the expected biological sensor signal are consistent to complete the closed-loop control, so that the individual difference of the stimulation effect of the device is small, and the parameter time persistence of the same individual is good.

[0128] It should be understood that, although each step in the flowchart involved in each embodiment as described above is displayed in sequence according to the arrow, these steps are not necessarily executed in sequence according to the arrow. Unless otherwise specified herein, the execution of these steps is not strictly limited in sequence, and these steps can be executed in other sequences. Moreover, at least part of the steps in the flowchart involved in each embodiment as described above can include multiple steps or stages, which are not necessarily executed at the same time, but can be executed at different times, and the execution sequence of these steps or stages is not necessarily sequential, but can be executed alternately or alternately with at least part of other steps or steps or stages in other steps. It can be understood that the steps in different embodiments can be freely combined as needed, and various non-contradictory schemes formed by combination are within the scope of protection of the present application.

[0129] Based on the same inventive concept, the embodiments of the present application also provide an electrical stimulation signal control device for implementing the above-mentioned electrical stimulation signal control method. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme described in the above method, and therefore the specific limitations in one or more electrical stimulation signal control device embodiments provided below can refer to the limitations of the electrical stimulation signal control method described above, which will not be described here again.

[0130] In one exemplary embodiment, as shown in Figure 8 An electrical stimulation signal control device 10 is provided, comprising a signal acquisition module 11, a feature extraction module 12, a signal extraction module 13 and a parameter adjustment module 14, wherein:

[0131] The signal acquisition module 11 is configured to acquire a respiratory signal and an electromyography signal.

[0132] The feature extraction module 12 is configured to extract a frequency feature of the electromyography signal and calculate a respiratory frequency feature.

[0133] The signal extraction module 13 is configured to extract a target respiratory signal from the respiratory signal based on the respiratory frequency feature.

[0134] The parameter adjustment module 14 is configured to adjust parameter information of a target electrical stimulation signal based on the target respiratory signal.

[0135] In one embodiment, the above-mentioned feature extraction module 12 comprises:

[0136] A peak interval acquisition sub-module configured to acquire an interval between adjacent peaks in an envelope of the electromyography signal.

[0137] A feature extraction sub-module configured to acquire a sampling frequency of the electromyography signal and extract a frequency feature of the electromyography signal based on the interval and the sampling frequency.

[0138] A feature calculation sub-module configured to calculate the respiratory frequency feature according to the frequency feature of the electromyography signal.

[0139] In one embodiment, the above-mentioned feature extraction sub-module is further configured to:

[0140] Substitute the interval and the sampling frequency into a first formula to calculate the frequency feature of the electromyography signal, the first formula comprising:

[0141] ;

[0142] wherein, is the frequency feature of the electromyography signal, is the interval between adjacent peaks in the envelope of the electromyography signal, is the sampling frequency of the electromyography signal.

[0143] In an embodiment, the parameter adjustment module 14 comprises:

[0144] a breathing point determination sub-module, configured to obtain an expiration point and an inspiration point in the target breathing signal.

[0145] a parameter adjustment sub-module, configured to adjust parameter information of the target electrical stimulation signal based on the expiration point and the inspiration point.

[0146] In an embodiment, the parameter adjustment sub-module is further configured to:

[0147] adjust timing information of the target electrical stimulation signal based on the expiration point and the inspiration point.

[0148] In an embodiment, the signal extraction module 13 comprises:

[0149] a signal fitting sub-module, configured to perform a polynomial least square fitting on the breathing signal to obtain a fitted breathing signal.

[0150] a filtering processing sub-module, configured to perform a band-pass filtering processing on the fitted breathing signal based on the breathing frequency feature to obtain the target breathing signal.

[0151] In an embodiment, the signal fitting sub-module is further configured to:

[0152] perform the polynomial least square fitting on the breathing signal with a least residual sum of squares as an optimization objective, and a calculation formula of the residual sum of squares comprises:

[0153] ;

[0154] wherein, is the residual sum of squares, is a vertical coordinate value of the breathing signal at a horizontal coordinate is a fitted value at the horizontal coordinate is a constant. , 、 、 and is a constant.

[0155] In an embodiment, the filtering processing sub-module is further configured to:

[0156] perform the filtering on the fitted breathing signal by using a band-pass filter allowing a frequency range of 0.8 times the breathing frequency feature to 1.2 times the breathing frequency feature to pass.

[0157] In an embodiment, the signal extraction module 13 is further configured to:

[0158] ​Based on the respiratory signal, a target electromyography signal is extracted from the electromyography signal.

[0159] The parameter adjustment module 14 is further configured to:

[0160] Based on the target electromyography signal, adjust the parameter information of the target electrical stimulation signal.

[0161] In one embodiment, the parameter adjustment module 14 is further configured to:

[0162] Obtain a feedback signal of the target electrical stimulation signal;

[0163] Based on the feedback signal and a preset target feedback signal, adjust the parameter information of the target electrical stimulation signal.

[0164] Each module in the electrical stimulation signal control device can be implemented by software, hardware, or a combination thereof, in whole or in part. Each module can be embedded in or independent of a processor in a computer device in hardware form, or stored in a memory in a computer device in software form, so as to be called and executed by a processor to perform the operations corresponding to each module.

[0165] In one exemplary embodiment, a neural stimulation device is provided, comprising a respiratory signal acquisition device, an electromyography signal acquisition device, an electrical stimulation signal generation device, a memory, and a processor;

[0166] The respiratory signal acquisition device is configured to acquire a respiratory signal;

[0167] The electromyography signal acquisition device is configured to acquire an electromyography signal;

[0168] The electrical stimulation signal generation device is configured to generate a target electrical stimulation signal;

[0169] The memory stores a program, and the processor implements the steps in each method embodiment described above when executing the program.

[0170] Specifically, please continue to refer to Figure 1, the respiratory sensor (respiratory signal acquisition device 01) detects the respiratory signal and transmits to the processor 04, the electromyographic sensor (electromyographic signal acquisition device 02) detects the electromyographic signal and transmits to the processor 04, the processor 04 extracts the frequency characteristics of the electromyographic signal, and calculates the respiratory frequency characteristics (the way can be adopted to obtain the interval between adjacent wave crests in the envelope of the electromyographic signal; obtain the sampling frequency of the electromyographic signal, and based on the interval and the sampling frequency, extract the frequency characteristics of the electromyographic signal); based on the respiratory frequency characteristics, extract the target respiratory signal in the respiratory signal (the way can be adopted to perform polynomial least squares fitting on the respiratory signal to obtain a fitted respiratory signal; based on the respiratory frequency characteristics, perform band-pass filtering on the fitted respiratory signal, and the pass-through frequency range of the band-pass filtering can be 0.8 times the respiratory frequency characteristics to 1.2 times the respiratory frequency characteristics). Since the signal components irrelevant to the respiratory rhythm are eliminated, the processor 04 obtains a more pure and accurate respiratory signal. Further, the processor 04 matches the electrical stimulation parameters based on the extracted more pure and accurate respiratory signal (the way can be adopted to obtain the expiration point and the inspiration point in the target respiratory signal; based on the expiration point and the inspiration point, adjust the parameter information of the target electrical stimulation signal), and transmits to the electrical stimulation signal generation device 03, so that the electrical stimulation signal generation device 03 can generate more accurate electrical stimulation signals. At the same time, the feedback regulation system can be added according to the changes of the sensor signals collected before and after stimulation in a closed-loop control manner, so that the target biological sensor signal and the expected biological sensor signal are consistent to complete the closed-loop control, so that the individual difference of the stimulation effect of the device is small, and the parameter time persistence of the same individual is good. The neural stimulation device 00 can be a lingual nerve stimulator.

[0171] Those skilled in the art can understand that, Figure 1 The structure shown in the figure is only a block diagram of part of the structure related to the scheme of the present application, and does not constitute a limitation on the neural stimulation device to which the scheme of the present application is applied. The specific neural stimulation device can include more or fewer components than those shown in the figure, or combine certain components, or have a different component arrangement.

[0172] In one embodiment, a readable storage medium is provided, and a program is stored on the readable storage medium. The program is executed by a processor to implement the steps in each of the method embodiments described above.

[0173] In one embodiment, a program product is provided, and the program product includes a program. The program is executed by a processor to implement the steps in each of the method embodiments described above.

[0174] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by programs instructing related hardware, and the programs can be stored in a non-volatile readable storage medium. When the programs are executed, the processes of the above-mentioned embodiment methods can be included. Any reference to memory, database or other medium used in each embodiment provided by the present application can include at least one of non-volatile memory and volatile memory. The non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical storage, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. The volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration but not limitation, the RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The database involved in each embodiment provided by the present application can include at least one of a relational database and a non-relational database. The non-relational database can include a distributed database based on a block chain, etc., and is not limited thereto. The processor involved in each embodiment provided by the present application can be a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, an artificial intelligence (AI) processor, etc., and is not limited thereto.

[0175] Each technical feature of the above embodiments can be combined arbitrarily. In order to make the description simple, all possible combinations of each technical feature in the above embodiments are not described, however, as long as the combination of the technical features does not exist, it should be considered as the range disclosed by the present application.

[0176] The above-described embodiments are merely illustrative of several embodiments of the present application, and the description is relatively specific and detailed, but should not be understood as a limitation on the scope of the patent. It should be noted that for those skilled in the art, without departing from the concept of the present application, a number of modifications and improvements can be made, which are all within the scope of the present application. Therefore, the scope of protection of the present application should be subject to the appended claims.

Claims

1. A method of controlling an electrical stimulation signal, characterized by, The method comprises: obtaining a respiratory signal and an electromyography signal; extracting a frequency feature of the electromyography signal and calculating a respiratory frequency feature; based on the respiratory frequency feature, extracting a target respiratory signal from the respiratory signal; based on the target respiratory signal, adjusting parameter information of a target electrical stimulation signal.

2. The method of claim 1, wherein, In the step of extracting the frequency feature of the electromyography signal and calculating the respiratory frequency feature, specifically comprising: obtaining the interval between adjacent peaks in the envelope of the electromyography signal; obtaining the sampling frequency of the electromyography signal, and based on the interval and the sampling frequency, extracting the frequency feature of the electromyography signal; based on the frequency feature of the electromyography signal, calculating the respiratory frequency feature.

3. The method of claim 2, wherein, In the step of extracting the frequency feature of the electromyography signal based on the interval and the sampling frequency, specifically comprising: substituting the interval and the sampling frequency into a first formula to calculate the frequency feature of the electromyography signal, the first formula comprising: ； wherein is a frequency characteristic of the myoelectric signal, is an interval between adjacent peaks in an envelope of the myoelectric signal, is a sampling frequency of the myoelectric signal.

4. The method of claim 1, wherein, In the step of adjusting the parameter information of the target electrical stimulation signal based on the target respiratory signal, specifically comprising: obtaining an expiration point and an inspiration point in the target respiratory signal; based on the expiration point and the inspiration point, adjusting the parameter information of the target electrical stimulation signal.

5. The method of claim 4, wherein, In the step of adjusting the parameter information of the target electrical stimulation signal based on the expiration point and the inspiration point, specifically comprising: based on the expiration point and the inspiration point, adjusting the timing information of the target electrical stimulation signal.

6. The method of claim 1, wherein, In the step of extracting the target respiratory signal from the respiratory signal based on the respiratory frequency feature, specifically comprising: performing a polynomial least squares fitting on the respiratory signal to obtain a fitted respiratory signal; based on the respiratory frequency feature, performing a band-pass filtering on the fitted respiratory signal to obtain the target respiratory signal.

7. The method of claim 6, wherein, In the step of performing the polynomial least squares fitting on the respiratory signal, specifically comprising: performing the polynomial least squares fitting on the respiratory signal with the minimum residual sum of squares as the optimization objective, the calculation formula of the residual sum of squares comprising: ； wherein is a residual sum of squares, is a vertical coordinate value of the respiration signal at a horizontal coordinate , is a fitted value at a horizontal coordinate , , , , and are constants.

8. The method of claim 6, wherein, In the step of performing the band-pass filtering on the fitted respiratory signal based on the respiratory frequency feature, specifically comprising: using a band-pass filter allowing a pass frequency range of 0.8 times the respiratory frequency feature to 1.2 times the respiratory frequency feature to filter the fitted respiratory signal.

9. The method of claim 1, wherein, After the step of obtaining the respiratory signal and the electromyography signal, comprising: based on the respiratory signal, extracting a target electromyography signal from the electromyography signal; based on the target electromyography signal, adjusting the parameter information of the target electrical stimulation signal.

10. The method of claim 1, wherein, The method further comprises: obtaining a feedback signal of the target electrical stimulation signal; based on the feedback signal and a preset target feedback signal, adjusting the parameter information of the target electrical stimulation signal.

11. An electrical stimulation signal control device, characterized by The device comprises: a signal obtaining module for obtaining a respiratory signal and an electromyography signal; a feature extracting module for extracting a frequency feature of the electromyography signal and calculating a respiratory frequency feature; a signal extracting module for extracting a target respiratory signal from the respiratory signal based on the respiratory frequency feature; a parameter adjusting module for adjusting parameter information of a target electrical stimulation signal based on the target respiratory signal.

12. A nerve stimulation device, characterized by The device comprises a respiration signal acquisition device, an electromyography signal acquisition device, an electrical stimulation signal generation device, a memory and a processor. The respiration signal acquisition device is configured to acquire a respiration signal. The electromyography signal acquisition device is configured to acquire an electromyography signal. The electrical stimulation signal generation device is configured to generate a target electrical stimulation signal. The memory stores a program, and the processor executes the program to implement the steps of the method in any one of claims 1 to 10.

13. A readable storage medium, having stored thereon a program, wherein the program is configured to cause a processor to perform the method according to any one of claims 1-12. The program, when executed by the processor, implements the steps of the method in any one of claims 1 to 10.

14. A program product comprising a program, characterized in that The program, when executed by the processor, implements the steps of the method in any one of claims 1 to 10.

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