Simulated bio-electricity signal generation method and device, equipment and storage medium

Abstract

The invention relates to the technical field of bio-electricity signal simulation, and provides a simulation bio-electricity signal generation method, device and equipment based on dynamic waveform regulation and control and a storage medium wherein the simulation bio-electricity signal generation method based on dynamic waveform regulation and control comprises the following steps: periodically collecting electric signal data of a target organism; processing the electric signal data, and extracting target feature information; according to the target feature information, selecting a preset type of waveform and setting an initial parameter of the preset type of waveform; dynamically adjusting parameters of the preset type of waveform according to the real-time signal change based on a feedback control algorithm; and according to the adjusted waveform parameter, obtaining a simulated bio-electricity signal. The simulated bio-electricity signals conforming to the individual characteristics are generated by adjusting the waveform parameters of the signals in real time, the difference between the simulated bio-electricity signals and the actual bio-electricity signals can be effectively reduced, and the application universality is improved.

Description

Technical Field

[0001] The present invention relates to the field of bioelectric signal simulation technology, and in particular to a method, device, equipment and storage medium for generating simulated bioelectric signals based on dynamic waveform regulation. Background Art

[0002] Simulating bioelectric signals (such as electromyographic signals, electrocardiographic signals, and electroencephalographic signals) is one of the key technologies in biomedical engineering and rehabilitation equipment. At present, the generation methods of simulated bioelectric signals mostly use preset waveform templates or simplified mathematical models to generate signals. This method can reflect the basic form and characteristics of bioelectric signals to a certain extent, but due to the complexity and individual differences of bioelectric signals, the above methods cannot provide sufficient flexibility to generate diversified signals for different individuals, and cannot adapt to the physiological characteristics of different individuals, resulting in significant deviations between the generated bioelectric signals and the actual bioelectric signals, affecting subsequent analysis and application. Summary of the invention

[0003] The embodiments of the present application provide a method, apparatus, device and storage medium for generating simulated bioelectric signals based on dynamic waveform control, which aims to generate simulated bioelectric signals that meet individual characteristics by adjusting the waveform parameters of the signal in real time, which can effectively reduce the difference between the simulated bioelectric signals and the actual bioelectric signals and improve the universality of the application.

[0004] In a first aspect, an embodiment of the present application provides a method for generating a simulated bioelectric signal based on dynamic waveform control, comprising:

[0005] Periodically collecting bioelectric signals of the target object;

[0006] Process the bioelectric signals and extract target feature information;

[0007] According to the target characteristic information, a preset type waveform is selected and initial parameters of the preset type waveform are set;

[0008] Based on the feedback control algorithm, the parameters of the preset type of waveform are dynamically adjusted according to the real-time changes of the bioelectric signal;

[0009] According to the adjusted waveform parameters, a simulated bioelectric signal is obtained.

[0010] In one embodiment, the bioelectric signal is processed to extract target feature information, including:

[0011] The bioelectric signals are processed to extract time domain features and frequency domain features respectively.

[0012] In one embodiment, the bioelectric signal is processed to extract time domain features and frequency domain features respectively, including:

[0013] Divide the bioelectric signal into multiple time windows;

[0014] Calculate the time domain eigenvalue of the corresponding bioelectric signal in each time window to obtain the time domain eigenvector;

[0015] In each time window, the time domain signal is converted into a frequency domain signal, the frequency domain eigenvalue is calculated, and the frequency domain eigenvector is obtained.

[0016] In one embodiment, selecting a preset type of waveform and setting initial parameters of the preset type of waveform according to target feature information includes:

[0017] Determine the dynamic characteristics of the bioelectric signal according to the time domain feature vector, and determine the frequency composition and spectrum distribution of the bioelectric signal according to the frequency domain feature vector;

[0018] According to the dynamic characteristics, frequency composition and spectrum distribution of the bioelectric signal, a matching preset type waveform is selected and initial parameters of the preset type waveform are set.

[0019] In one embodiment, based on a feedback control algorithm, the parameters of a preset type of waveform are dynamically adjusted according to real-time changes in the bioelectric signal, including:

[0020] Extract the time domain and frequency domain features of bioelectric signals in real time;

[0021] Compare the time domain characteristics and the frequency domain characteristics with the parameters of the preset type of waveform respectively, and determine the error according to the comparison results;

[0022] Analyze errors based on feedback control algorithms and determine control strategies;

[0023] Dynamically adjust the parameters of the preset type waveform according to the control strategy.

[0024] In one embodiment, analyzing the error based on a feedback control algorithm and determining a control strategy includes:

[0025] The error size is analyzed based on the feedback control algorithm, and the adjustment value of the waveform parameters that need to be adjusted is calculated.

[0026] In one embodiment, dynamically adjusting the parameters of a preset type of waveform according to a control strategy includes:

[0027] According to the calculated adjustment values ​​of the waveform parameters, the parameters of the preset type of waveform are dynamically adjusted.

[0028] In a second aspect, an embodiment of the present application provides a device for generating a simulated bioelectric signal based on dynamic waveform control, comprising:

[0029] A collection module, used for periodically collecting bioelectric signals of a target object;

[0030] An extraction module is used to process bioelectric signals and extract target feature information;

[0031] A selection module is used to select a preset type of waveform and set initial parameters of the preset type of waveform according to target feature information;

[0032] An adjustment module, for dynamically adjusting the parameters of a preset type of waveform according to real-time changes in bioelectric signals based on a feedback control algorithm;

[0033] The generating module is used to obtain the simulated bioelectric signal according to the adjusted waveform parameters.

[0034] In a third aspect, an embodiment of the present application provides an electronic device, including:

[0035] Memory and processing modules;

[0036] The memory is used to store computer programs;

[0037] The processing module is used to execute the computer program and implement the steps of the method for generating simulated bioelectric signals based on dynamic waveform regulation in the first aspect when executing the computer program.

[0038] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, which stores a computer program; when the computer program is executed by one or more processing modules, the one or more processing modules execute the steps of the method for generating simulated bioelectric signals based on dynamic waveform regulation as described in the first aspect above.

[0039] The embodiments of the present application provide a method, device, equipment and storage medium for generating a simulated bioelectric signal based on dynamic waveform regulation, wherein the method for generating a simulated bioelectric signal based on dynamic waveform regulation includes: periodically collecting the electrical signal data of the target organism; processing the electrical signal data to extract target feature information; selecting a preset type of waveform and setting the initial parameters of the preset type of waveform according to the target feature information; dynamically adjusting the parameters of the preset type of waveform according to real-time signal changes based on a feedback control algorithm; and obtaining a simulated bioelectric signal according to the adjusted waveform parameters. By adjusting the waveform parameters of the signal in real time to generate a simulated bioelectric signal that meets individual characteristics, the difference between the simulated bioelectric signal and the actual bioelectric signal can be effectively reduced, thereby improving the universality of the application. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying any creative work.

[0041] Figure 1 A schematic diagram of a flow chart of a method for generating a simulated bioelectric signal based on dynamic waveform control provided in an embodiment of the present application;

[0042] Figure 2 A schematic diagram of the structure of a simulated bioelectric signal generating device based on dynamic waveform control provided in an embodiment of the present application;

[0043] Figure 3 A schematic block diagram of a simulated bioelectric signal generation setting based on dynamic waveform regulation provided in an embodiment of the present application. DETAILED DESCRIPTION

[0044] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are 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 creative work are within the scope of protection of the present invention.

[0045] The flowcharts shown in the accompanying drawings are only examples and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may also be decomposed, combined or partially merged, so the actual execution order may change according to actual conditions.

[0046] It should also be understood that the terms used in this application specification are only for the purpose of describing specific embodiments and are not intended to limit the application. As used in this application specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0047] It should be further understood that the term “and / or” used in the specification and appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0048] The technical solution provided by this application will be described in detail below with reference to the accompanying drawings.

[0049] See also Figure 1 , Figure 1A flow chart of a method for generating a simulated bioelectric signal based on dynamic waveform regulation provided in an embodiment of the present application. The execution subject of the method for generating a simulated bioelectric signal based on dynamic waveform regulation is an electronic device that completes the entire task of generating, detecting, and adjusting the simulated bioelectric signal. The combination of hardware and / or software modules included in the electronic device can complete the steps of the method for generating a simulated bioelectric signal based on dynamic waveform regulation. Figure 1 It can be seen that the method for generating a simulated bioelectric signal based on dynamic waveform regulation provided in the embodiment of the present application includes the following steps:

[0050] S101: Periodically collect bioelectric signals of a target object.

[0051] Periodic collection of target object's bioelectric signals refers to the continuous recording of target object's (such as human muscle, nerve, heart, etc.) bioelectric signals at set time intervals (sampling period). Collecting these bioelectric signals can monitor changes in physiological state as a basis for real-time control or diagnosis. Typical applications include ECG, EEG, EMG and other signal monitoring.

[0052] S102: Process the bioelectric signal to extract target feature information.

[0053] Bioelectric signals (such as ECG, EEG, EMG, etc.) are electrical signals generated by human tissues and organs during physiological activities. These signals usually contain a lot of noise and redundant information, so signal processing is required to extract effective target feature information.

[0054] In one embodiment, the bioelectric signal is processed to extract target feature information, including: processing the bioelectric signal to extract time domain features and frequency domain features respectively.

[0055] The analysis of time domain features mainly focuses on the changes of bioelectric signals on the time axis, and directly uses the waveform of the original bioelectric signal for calculation. This method can capture information such as the trend, amplitude change, and peak value of the bioelectric signal. The time domain features mainly include: mean, variance and standard deviation, peak-to-peak value, root mean square value, and zero crossing rate. Among them, the mean is used to describe the average value of the bioelectric signal over a period of time and judge the overall trend of the bioelectric signal; the variance and standard deviation are used to measure the volatility and degree of change of the bioelectric signal; the peak-to-peak value is the difference between the maximum and minimum values ​​of the bioelectric signal, which is used to describe the amplitude range of the bioelectric signal; the root mean square value is used to evaluate the energy size of the bioelectric signal; the zero crossing rate is used to detect the frequency of changes in the bioelectric signal. These time domain features are suitable for analyzing low-frequency signals, detecting emergencies, and describing the energy and change trend of bioelectric signals.

[0056] Frequency domain features reveal the frequency components and periodic characteristics of bioelectric signals by converting them from the time domain to the frequency domain (usually using Fourier transform). This is very important for detecting periodic events and filtering out noise. This embodiment can obtain more comprehensive information by combining time domain features and frequency domain features to analyze bioelectric signals. For example, time domain features can directly reflect the fluctuation trend and energy size of bioelectric signals, while frequency domain features reveal the hidden periodic components and frequency anomalies of bioelectric signals.

[0057] In one embodiment, the bioelectric signal is processed to extract time domain features and frequency domain features respectively, including: dividing the bioelectric signal into multiple time windows; calculating the time domain feature value of the corresponding bioelectric signal in each time window to obtain a time domain feature vector; converting the time domain signal into a frequency domain signal in each time window, calculating the frequency domain feature value, and obtaining a frequency domain feature vector.

[0058] Bioelectric signals (such as electrocardiogram, electroencephalogram, and electromyography) are usually continuous and non-stationary, which means that the characteristics of bioelectric signals will change over time. In order to better extract these changing features, the time window division method is often used, that is, the bioelectric signal is divided into multiple small segments of fixed length, and the features are calculated separately on each segment. The time window refers to the length of each time window, such as 100 milliseconds, 500 milliseconds, or 1 second. In practical applications, in order to avoid the loss of important information, there is usually some overlap between adjacent time windows, such as 50 percent overlap. Assuming that the length of the bioelectric signal is N, the time window size is W, and the overlap length is O, the starting index of the time window is:

[0059] t i =i·(WO) (i=0, 1, 2,...)

[0060] The data in each time window is used to calculate time domain and frequency domain features separately.

[0061] The bioelectric signal in each time window is processed, and several time domain features are calculated to form a time domain feature vector. Common time domain features include: mean, standard deviation, peak-to-peak value, and root mean square value. All time domain feature values ​​are combined into a vector, for example, V1 = [mean, standard deviation, ..., root mean square value], and each time window will generate a corresponding time domain feature vector.

[0062] Fourier transform is applied to the bioelectric signal in each time window to obtain the spectrum of the bioelectric signal; frequency domain information such as main frequency, power spectrum density, frequency center, bandwidth, etc. can be extracted from the spectrum, and all frequency domain information is combined into a vector, for example, V2 = [main frequency, power spectrum density, frequency center, bandwidth...], and each time window will generate a corresponding frequency domain feature vector.

[0063] Among them, the main frequency is used to represent the frequency component with the highest energy in the spectrum, the power spectrum density is used to represent the energy distribution of each frequency component, the frequency center is used to represent the center of gravity of the spectrum, and the bandwidth is used to represent the frequency distribution range of the signal.

[0064] In each time window, the calculated time domain feature vector and frequency domain feature vector are combined to form the feature vector of the corresponding time window. The feature vectors of all time windows are combined to form the feature matrix of the entire bioelectric signal, which can be used for subsequent analysis.

[0065] S103: Selecting a preset type waveform and setting initial parameters of the preset type waveform according to the target feature information.

[0066] The dynamic characteristics and frequency components of bioelectric signals determine their basic form. By analyzing the time domain and frequency domain characteristics of bioelectric signals, the most suitable waveform model (such as sine wave, square wave, pulse wave, etc.) can be found to describe the bioelectric signal. The selection of preset type waveforms and the initial parameter settings will help achieve accurate results in bioelectric signal reconstruction, filtering processing or system simulation.

[0067] In one embodiment, a preset type waveform is selected and initial parameters of the preset type waveform are set based on target feature information, including: determining the dynamic characteristics of the bioelectric signal based on the time domain feature vector, and determining the frequency composition and spectral distribution of the bioelectric signal based on the frequency domain feature vector; based on the dynamic characteristics, frequency composition and spectral distribution of the bioelectric signal, a matching preset type waveform is selected and initial parameters of the preset type waveform are set.

[0068] Specifically, the dynamic characteristics describe the changing trend, fluctuation amplitude and transient behavior of the bioelectric signal. The changing trend of the bioelectric signal is determined by whether the mean value deviates from zero, the fluctuation amplitude of the bioelectric signal is determined by the fluctuation of the standard deviation and peak-to-peak value, and the transient behavior of the bioelectric signal is determined by the magnitude of the zero crossing rate.

[0069] Frequency composition and spectrum distribution reflect the energy concentration of bioelectric signals at different frequencies. Frequency composition includes main frequency, frequency center and bandwidth. Spectrum distribution includes power spectrum density.

[0070] For example, assuming that the bioelectric signal in the i-th time window is: i , the time domain feature vector is: V1, the frequency domain feature vector is: V2, the preset type waveform is: y(t), and the preset category waveform parameters include: amplitude A, frequency f, phase Φ, offset θ, duty cycle D. According to the dynamic characteristics, frequency composition and spectrum distribution of the bioelectric signal, the process of selecting a matching preset type waveform can be expressed as:

[0071] If it is determined according to the time domain feature vector V1 that the zero crossing rate is lower than the preset first threshold value and the mean value tends to zero, the preset type waveform selected is a sine wave, which is expressed as:

[0072]

[0073] If it is determined according to the time domain feature vector V1 that the zero crossing rate is greater than the second threshold (the second threshold is greater than the first threshold), the selected preset type waveform is a square wave, which is expressed as:

[0074] y(t)=A·sgn(sin(2πft))+θ

[0075] If it is determined according to the time domain feature vector V1 that the zero crossing rate is greater than the first threshold and less than the second threshold, the preset type waveform is selected as a pulse wave, which is expressed as:

[0076] y(t)=A·δ(t-nT)

[0077] Determine the frequency domain characteristic vector V2 in the time window W i If the internal bioelectric signal is dominated by a single frequency, the frequency f of the preset type waveform is set as the main frequency. If it is determined that within the time window W i When multiple frequencies of the internal bioelectric signal are superimposed, the frequency f of the preset type waveform is set as the center frequency, and the amplitude A of the preset type waveform is determined according to the root mean square value of the time domain feature vector. Determine the phase of the preset type of waveform based on the mean of the time domain eigenvector Offset θ = V mean , Duty cycle (only for square wave or pulse wave)

[0078] in, is the preset initial phase, V RMS is the root mean square value, V mean is the mean value, T high High level duration.

[0079] S104: Based on a feedback control algorithm, dynamically adjust the parameters of the preset type of waveform according to the real-time changes of the bioelectric signal.

[0080] In one embodiment, based on a feedback control algorithm, the parameters of a preset type of waveform are dynamically adjusted according to real-time changes in bioelectric signals, including: extracting time domain characteristics and frequency domain characteristics of the bioelectric signal in real time; comparing the time domain characteristics and frequency domain characteristics with the parameters of the preset type of waveform, respectively, and determining errors based on the comparison results; analyzing the errors based on a feedback control algorithm and determining a control strategy; and dynamically adjusting the parameters of the preset type of waveform according to the control strategy.

[0081] Specifically, time domain features reflect the time series change trend of the signal, such as the average value, peak-to-peak value, standard deviation, etc. These features can be used to match the waveform parameters such as amplitude, phase, offset, etc.

[0082] For example, if the preset waveform type is a sine wave, the amplitude A should be close to half of the peak-to-peak value of the time domain feature, and the difference between the corresponding amplitude A and half of the peak-to-peak value is the amplitude error. If the preset waveform type is a square wave, the corresponding amplitude A should be close to the root mean square value, and the difference between the corresponding amplitude A and the root mean square value is the amplitude error.

[0083] The average value included in the time domain feature can be used to reflect the DC offset of the signal. If the average value is not 0, the value corresponding to the average value is the offset error. The zero-crossing rate included in the time domain feature is used to judge the periodic change and phase of the signal. The difference between the zero-crossing rate and the phase Φ of the bioelectric signal can be calculated as the phase error.

[0084] In one embodiment, analyzing the error based on the feedback control algorithm and determining the control strategy include:

[0085] The error size is analyzed based on the feedback control algorithm, and the adjustment value of the waveform parameter that needs to be adjusted is calculated.

[0086] Specifically, if the value corresponding to an error is large, specifically greater than the third threshold, the first preset parameter, also known as the main parameter such as amplitude, frequency, etc., needs to be adjusted to ensure that the waveform basically matches the bioelectric signal. If the values ​​corresponding to all errors are less than the maximum threshold, the second preset parameter, also known as the secondary parameter such as phase, offset, duty cycle, etc., needs to be adjusted to further optimize the bioelectric signal output.

[0087] For example, based on the feedback control algorithm error analysis, the control strategy can be expressed as calculating the adjustment value of the waveform parameter that needs to be adjusted by the following formula:

[0088]

[0089] Among them, Δp i (t) is the adjustment value of the waveform parameter that needs to be adjusted, K p is the proportionality coefficient, K i is the integration coefficient, K d is the differential coefficient, E i (t) represents the i-th error at time t. It should be noted that K p , K i and K d All are pre-set according to the application.

[0090] In one embodiment, dynamically adjusting the parameters of a preset type of waveform according to a control strategy includes: dynamically adjusting the parameters of a preset type of waveform according to the calculated adjustment values ​​of each waveform parameter.

[0091] S105: Obtaining a simulated bioelectric signal according to the adjusted waveform parameters.

[0092] Specifically, each waveform parameter is updated according to the calculated adjustment value, and a simulated bioelectric signal is obtained and output.

[0093] Through the above analysis, it can be known that the method for generating simulated bioelectric signals based on dynamic waveform regulation provided by the embodiment of the present application includes: periodically collecting the electrical signal data of the target organism; processing the electrical signal data to extract the target characteristic information; selecting a preset type of waveform and setting the initial parameters of the preset type of waveform according to the target characteristic information; dynamically adjusting the parameters of the preset type of waveform according to the real-time signal changes based on the feedback control algorithm; and obtaining a simulated bioelectric signal according to the adjusted waveform parameters. By adjusting the waveform parameters of the signal in real time to generate a simulated bioelectric signal that meets the individual characteristics, the difference between the simulated bioelectric signal and the actual bioelectric signal can be effectively reduced, thereby improving the universality of the application.

[0094] See also Figure 2 , Figure 2 This is a schematic diagram of the structure of a device for generating simulated bioelectric signals based on dynamic waveform control provided in an embodiment of the present application. Figure 2 It can be seen that the simulated bioelectric signal generating device 20 based on dynamic waveform control provided in the embodiment of the present application includes:

[0095] The acquisition module 201 is used to periodically acquire the bioelectric signals of the target object.

[0096] The extraction module 202 is used to process the bioelectric signal and extract target feature information.

[0097] The selection module 203 is used to select a preset type of waveform and set initial parameters of the preset type of waveform according to target feature information.

[0098] The adjustment module 204 is used to dynamically adjust the parameters of the preset type of waveform according to the real-time changes of the bioelectric signal based on the feedback control algorithm.

[0099] The generating module 205 is used to obtain a simulated bioelectric signal according to the adjusted waveform parameters.

[0100] In one embodiment, the extraction module 202 is specifically configured to:

[0101] The bioelectric signals are processed to extract time domain features and frequency domain features respectively.

[0102] In one embodiment, the extraction module 202 includes:

[0103] A division unit, used for dividing the bioelectric signal into a plurality of time windows;

[0104] The first obtaining unit is used to calculate the time domain characteristic value of the corresponding bioelectric signal in each time window to obtain the time domain characteristic vector;

[0105] The second obtaining unit is used to convert the time domain signal into a frequency domain signal in each time window, calculate the frequency domain eigenvalue, and obtain the frequency domain eigenvector.

[0106] In one embodiment, the selection module 203 includes:

[0107] A first determination unit, used to determine the dynamic characteristics of the bioelectric signal according to the time domain feature vector, and to determine the frequency composition and spectrum distribution of the bioelectric signal according to the frequency domain feature vector;

[0108] The selection unit is used to select a matching preset type waveform and set initial parameters of the preset type waveform according to the dynamic characteristics, frequency composition and spectrum distribution of the bioelectric signal.

[0109] In one embodiment, the adjustment module 204 includes:

[0110] An extraction unit, used for extracting time domain features and frequency domain features of bioelectric signals in real time;

[0111] A comparison unit, used to compare the time domain characteristics and the frequency domain characteristics with the parameters of the preset type of waveform respectively, and determine the error according to the comparison result;

[0112] A second determination unit is used to analyze errors based on a feedback control algorithm and determine a control strategy;

[0113] The adjustment unit is used to dynamically adjust the parameters of the preset type waveform according to the control strategy.

[0114] In one embodiment, the second determining unit is specifically configured to:

[0115] The error size is analyzed based on the feedback control algorithm, and the adjustment value of the waveform parameters that need to be adjusted is calculated.

[0116] In one embodiment, the adjustment unit is specifically configured to:

[0117] According to the calculated adjustment values ​​of the waveform parameters, the parameters of the preset type of waveform are dynamically adjusted.

[0118] It should be noted that the specific implementation process of the above modules or units can refer to the specific implementation process of each step in the previous method embodiment, which will not be repeated here.

[0119] See also Figure 3 As shown, Figure 3 A schematic block diagram of a simulated bioelectric signal generating device based on dynamic waveform control provided in an embodiment of the present application.

[0120] Exemplarily, the simulated bioelectric signal generating device 30 based on dynamic waveform regulation includes a processing module 301 and a memory 302 .

[0121] Exemplarily, the processing module 301 and the memory 302 are connected via a bus 303 , and the bus 303 is, for example, an I2C (Inter-integrated Circuit) bus.

[0122] Specifically, the processing module 301 may be a micro-controller unit (MCU), a central processing unit (CPU) or a digital signal processing module (DSP).

[0123] Specifically, the memory 302 may be a Flash chip, a read-only memory (ROM) disk, an optical disk, a USB flash drive, or a mobile hard disk.

[0124] The processing module 301 is used to run the computer program stored in the memory 302, and implement the steps of the above-mentioned method for generating simulated bioelectric signals based on dynamic waveform regulation when executing the computer program.

[0125] Exemplarily, the processing module 301 is used to run the computer program stored in the memory 302, and implement the following steps when executing the computer program:

[0126] Periodically collecting bioelectric signals of the target object;

[0127] Process the bioelectric signals and extract target feature information;

[0128] According to the target characteristic information, a preset type waveform is selected and initial parameters of the preset type waveform are set;

[0129] Based on the feedback control algorithm, the parameters of the preset type of waveform are dynamically adjusted according to the real-time changes of the bioelectric signal;

[0130] According to the adjusted waveform parameters, a simulated bioelectric signal is obtained.

[0131] In one embodiment, the bioelectric signal is processed to extract target feature information, including:

[0132] The bioelectric signals are processed to extract time domain features and frequency domain features respectively.

[0133] In one embodiment, the bioelectric signal is processed to extract time domain features and frequency domain features respectively, including:

[0134] Divide the bioelectric signal into multiple time windows;

[0135] Calculate the time domain eigenvalue of the corresponding bioelectric signal in each time window to obtain the time domain eigenvector;

[0136] In each time window, the time domain signal is converted into a frequency domain signal, the frequency domain eigenvalue is calculated, and the frequency domain eigenvector is obtained.

[0137] In one embodiment, selecting a preset type of waveform and setting initial parameters of the preset type of waveform according to target feature information includes:

[0138] Determine the dynamic characteristics of the bioelectric signal according to the time domain feature vector, and determine the frequency composition and spectrum distribution of the bioelectric signal according to the frequency domain feature vector;

[0139] According to the dynamic characteristics, frequency composition and spectrum distribution of the bioelectric signal, a matching preset type waveform is selected and initial parameters of the preset type waveform are set.

[0140] In one embodiment, based on a feedback control algorithm, the parameters of a preset type of waveform are dynamically adjusted according to real-time changes in the bioelectric signal, including:

[0141] Extract the time domain and frequency domain features of bioelectric signals in real time;

[0142] Compare the time domain characteristics and the frequency domain characteristics with the parameters of the preset type of waveform respectively, and determine the error according to the comparison results;

[0143] Analyze errors based on feedback control algorithms and determine control strategies;

[0144] Dynamically adjust the parameters of the preset type waveform according to the control strategy.

[0145] In one embodiment, analyzing the error based on a feedback control algorithm and determining a control strategy includes:

[0146] The error size is analyzed based on the feedback control algorithm to determine the adjustment value of the waveform parameter that needs to be adjusted.

[0147] In one embodiment, dynamically adjusting the parameters of a preset type of waveform according to a control strategy includes:

[0148] According to the calculated adjustment values ​​of the waveform parameters, the parameters of the preset type of waveform are dynamically adjusted.

[0149] The specific principles and implementation methods of the simulated bioelectric signal generation device based on dynamic waveform regulation provided in the embodiment of the present application are similar to the implementation of the simulated bioelectric signal generation method based on dynamic waveform regulation in the aforementioned embodiment, and will not be repeated here.

[0150] The embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by the processing module, the processing module implements the following steps:

[0151] Periodically collecting bioelectric signals of the target object;

[0152] Process the bioelectric signals and extract target feature information;

[0153] According to the target characteristic information, a preset type waveform is selected and initial parameters of the preset type waveform are set;

[0154] Based on the feedback control algorithm, the parameters of the preset type of waveform are dynamically adjusted according to the real-time changes of the bioelectric signal;

[0155] According to the adjusted waveform parameters, a simulated bioelectric signal is obtained.

[0156] In one embodiment, the bioelectric signal is processed to extract target feature information, including:

[0157] The bioelectric signals are processed to extract time domain features and frequency domain features respectively.

[0158] In one embodiment, the bioelectric signal is processed to extract time domain features and frequency domain features respectively, including:

[0159] Divide the bioelectric signal into multiple time windows;

[0160] Calculate the time domain eigenvalue of the corresponding bioelectric signal in each time window to obtain the time domain eigenvector;

[0161] In each time window, the time domain signal is converted into a frequency domain signal, the frequency domain eigenvalue is calculated, and the frequency domain eigenvector is obtained.

[0162] In one embodiment, selecting a preset type of waveform and setting initial parameters of the preset type of waveform according to target feature information includes:

[0163] Determine the dynamic characteristics of the bioelectric signal according to the time domain feature vector, and determine the frequency composition and spectrum distribution of the bioelectric signal according to the frequency domain feature vector;

[0164] According to the dynamic characteristics, frequency composition and spectrum distribution of the bioelectric signal, a matching preset type waveform is selected and initial parameters of the preset type waveform are set.

[0165] In one embodiment, based on a feedback control algorithm, the parameters of a preset type of waveform are dynamically adjusted according to real-time changes in the bioelectric signal, including:

[0166] Extract the time domain and frequency domain features of bioelectric signals in real time;

[0167] Compare the time domain characteristics and the frequency domain characteristics with the parameters of the preset type of waveform respectively, and determine the error according to the comparison results;

[0168] Analyze errors based on feedback control algorithms and determine control strategies;

[0169] Dynamically adjust the parameters of the preset type waveform according to the control strategy.

[0170] In one embodiment, analyzing the error based on a feedback control algorithm and determining a control strategy includes:

[0171] The error size is analyzed based on the feedback control algorithm, and the adjustment value of the waveform parameters that need to be adjusted is calculated.

[0172] In one embodiment, dynamically adjusting the parameters of a preset type of waveform according to a control strategy includes:

[0173] According to the calculated adjustment values ​​of the waveform parameters, the parameters of the preset type of waveform are dynamically adjusted.

[0174] The computer-readable storage medium may be an internal storage unit of the device for generating analog bioelectric signals based on dynamic waveform regulation in the aforementioned embodiment, such as a hard disk or memory of the device for generating analog bioelectric signals based on dynamic waveform regulation. The computer-readable storage medium may also be an external storage device of the device for generating low-frequency signals in a multi-channel pulse, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc., equipped on the device for generating analog bioelectric signals based on dynamic waveform regulation.

[0175] It should be understood that the terminology used in this application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application.

[0176] It will also be understood that the term "and / or" used in this application and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0177] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed in the present application, and these modifications or replacements should be included in the protection scope of the present application. Therefore, the protection scope of the present application shall be based on the protection scope of the claims.

Claims

1. A method for generating simulated bioelectric signals based on dynamic waveform control, characterized in that: include: Periodically collecting bioelectric signals of the target object; Processing the bioelectric signal to extract target feature information; According to the target characteristic information, a preset type of waveform is selected and initial parameters of the preset type of waveform are set; Based on a feedback control algorithm, dynamically adjusting the parameters of the preset type of waveform according to the real-time changes of the bioelectric signal; According to the adjusted waveform parameters, a simulated bioelectric signal is obtained.

2. The method for generating simulated bioelectric signals based on dynamic waveform control according to claim 1, characterized in that: Processing the bioelectric signal to extract target feature information includes: The bioelectric signal is processed to extract time domain features and frequency domain features respectively.

3. The method for generating simulated bioelectric signals based on dynamic waveform control according to claim 2, characterized in that: The processing of the bioelectric signal to extract time domain features and frequency domain features respectively includes: Dividing the bioelectric signal into a plurality of time windows; Calculate the time domain eigenvalue of the corresponding bioelectric signal in each time window to obtain the time domain eigenvector; In each time window, the time domain signal is converted into a frequency domain signal, the frequency domain eigenvalue is calculated, and the frequency domain eigenvector is obtained.

4. The method for generating simulated bioelectric signals based on dynamic waveform control according to claim 3, characterized in that: The step of selecting a preset type of waveform and setting initial parameters of the preset type of waveform according to the target characteristic information includes: Determine the dynamic characteristics of the bioelectric signal according to the time domain feature vector, and determine the frequency composition and spectrum distribution of the bioelectric signal according to the frequency domain feature vector; According to the dynamic characteristics, frequency composition and spectrum distribution of the bioelectric signal, a matching preset type waveform is selected and initial parameters of the preset type waveform are set.

5. The method for generating simulated bioelectric signals based on dynamic waveform control according to claims 1 to 4, characterized in that: The method of dynamically adjusting the parameters of the preset type of waveform according to the real-time changes of the bioelectric signal based on the feedback control algorithm includes: Extracting time domain features and frequency domain features of the bioelectric signal in real time; Comparing the time domain feature and the frequency domain feature with the parameters of the preset type of waveform respectively, and determining an error according to the comparison result; Analyze the error based on a feedback control algorithm and determine a control strategy; According to the control strategy, the parameters of the preset type of waveform are dynamically adjusted.

6. The method for generating simulated bioelectric signals based on dynamic waveform control according to claim 5, characterized in that: The step of analyzing the error based on a feedback control algorithm and determining a control strategy comprises: The magnitude of the error is analyzed based on a feedback control algorithm, and the adjustment value of the waveform parameter that needs to be adjusted is calculated.

7. The method for generating simulated bioelectric signals based on dynamic waveform control according to claim 6, characterized in that: The dynamically adjusting the parameters of the preset type waveform according to the control strategy includes: The parameters of the preset type of waveform are dynamically adjusted according to the calculated adjustment values ​​of the waveform parameters.

8. A device for generating simulated bioelectric signals based on dynamic waveform control, characterized in that: include: A collection module, used for periodically collecting bioelectric signals of a target object; An extraction module, used to process the bioelectric signal and extract target feature information; A selection module, used to select a preset type of waveform and set initial parameters of the preset type of waveform according to the target characteristic information; An adjustment module, for dynamically adjusting the parameters of the preset type of waveform according to the real-time changes of the bioelectric signal based on a feedback control algorithm; The generating module is used to obtain the simulated bioelectric signal according to the adjusted waveform parameters.

9. An electronic device, characterized in that: include: Memory and processing modules; The memory is used to store computer programs; The processing module is used to execute the computer program and implement the steps of the method for generating simulated bioelectric signals based on dynamic waveform regulation as described in claim 8 when executing the computer program.

10. A computer-readable storage medium, characterized in that: The computer readable storage medium stores a computer program; When the computer program is executed by one or more processing modules, the one or more processing modules execute the steps of the method for generating simulated bioelectric signals based on dynamic waveform regulation as described in claim 8.

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