Bio-electricity signal anti-interference method, device and equipment and storage medium

Abstract

The invention relates to the technical field of bio-electricity signal processing, and provides a bio-electricity signal anti-interference method, device and equipment and a storage medium, and the method comprises the steps: collecting a bio-electricity signal in real time, and detecting environment noise; filtering the input signal according to the environmental noise and the bio-electricity signal to obtain a target bio-electricity signal; performing short-time Fourier transform on the target bio-electricity signal, and identifying and suppressing stable noise in a frequency domain; and analyzing instantaneous noise of the target bio-electricity signal by using wavelet transform, and removing transient interference in a time domain. According to the method, the parameters of the filter are dynamically adjusted by using the collected environmental noise, so that the interference with the bio-electricity signal can be more accurately removed, stable noise and transient noise are respectively processed through different algorithms of the frequency domain and the time domain, and the problems that a traditional filtering method is effective in stable noise and transient noise processing effect, signal distortion or false detection is easily caused, and the signal quality is poor are solved. And the transmission quality of weak bio-electricity signals is improved.

Description

Technical Field

[0001] The present invention relates to the field of bioelectric signal processing technology, and in particular to a bioelectric signal anti-interference method, device, equipment and storage medium. Background Art

[0002] Bioelectric signals (such as ECG, EMG, and EEG) are usually very weak, with signal amplitudes generally ranging from microvolts (μV) to millivolts (mV), and are easily interfered by various noises, such as power frequency interference, muscle artifacts, sensor noise, and environmental electromagnetic radiation. These interferences not only affect the quality of bioelectric signals, but may also cause data distortion or loss during transmission, making subsequent analysis results inaccurate. Traditional data anti-interference methods, such as filtering, noise reduction algorithms, and differential signal processing, can improve signal quality to a certain extent, but they cannot effectively suppress interference when faced with complex, multi-source interference, especially for weak signals. Summary of the invention

[0003] The embodiments of the present application provide a bioelectric signal anti-interference method, device, equipment and storage medium, which aims to more accurately remove interference overlapping with bioelectric signals by utilizing the collected environmental noise and dynamically adjusting the parameters of the filter, and further process stable noise and transient noise respectively through different algorithms in the frequency domain and time domain, thereby solving the problem that traditional filtering methods are ineffective in processing stable noise and transient noise, but are prone to signal distortion or false detection, thereby improving the transmission quality of weak bioelectric signals.

[0004] In a first aspect, an embodiment of the present application provides a bioelectric signal anti-interference method, comprising:

[0005] Collect bioelectric signals and detect environmental noise in real time;

[0006] Filter the input signal according to the environmental noise and the bioelectric signal to obtain the target bioelectric signal;

[0007] Perform short-time Fourier transform on the target bioelectric signal to identify and suppress stable noise in the frequency domain;

[0008] Wavelet transform is used to analyze the transient noise of the target bioelectric signal and remove transient interference in the time domain.

[0009] In one embodiment, filtering the input signal according to the environmental noise and the bioelectric signal to obtain the target bioelectric signal includes:

[0010] Determine the relative intensity of environmental noise based on environmental noise and bioelectric signals;

[0011] Dynamically adjust the step size parameters of the preset averaging algorithm based on relative strength;

[0012] The filter weights of the preset mean algorithm are updated based on the adjusted step size parameters, and the input signal is filtered based on the filter weights to obtain the target bioelectric signal.

[0013] In one embodiment, determining the relative intensity of the environmental noise according to the environmental noise and the bioelectric signal includes:

[0014] Determine the noise power ratio based on the environmental noise and the bioelectric signal;

[0015] Based on the noise power ratio, the relative strength of the ambient noise is determined.

[0016] In one embodiment, determining the noise power ratio according to the environmental noise and the bioelectric signal includes:

[0017] The environmental noise and the bioelectric signal are input into a preset noise power ratio calculation formula to calculate the noise power ratio.

[0018] In one embodiment, updating the filter weights of the preset mean algorithm based on the adjusted step size parameter includes:

[0019] The adjusted step length parameter is normalized based on the energy of the input signal to obtain the fused step length parameter;

[0020] Update the filter weights of the preset mean algorithm based on the fused step size parameters.

[0021] In one embodiment, the fused step size parameter is expressed as:

[0022]

[0023] Among them, μ norm (n) is the step size parameter after fusion, μ(n) is the adjusted step size parameter, is the bi-norm of the input signal, indicating the Euclidean length of the input signal in the M-dimensional space, x(n-1) is the input signal vector, M is the filter order, and ∈ is a constant.

[0024] In one embodiment, the filter weight of the preset mean algorithm is updated based on the fused step size parameter, which is expressed as:

[0025] ω i (n+1)=w i (n)+2·μ norm (n)·e(n)·x(ni)

[0026] Among them, ω i (n+1) is the i-th weight coefficient after update at the n+1th iteration, w i(n) is the ith weight coefficient at the nth iteration, e(n) is the error signal of the nth iteration, and x(ni) is the value of the input signal at the nith moment.

[0027] In a second aspect, an embodiment of the present application provides a bioelectric signal anti-interference device, comprising:

[0028] Acquisition module, used to collect bioelectric signals in real time and detect environmental noise;

[0029] A processing module, used for filtering the input signal according to the environmental noise and the bioelectric signal to obtain a target bioelectric signal;

[0030] An identification module, used for performing short-time Fourier transform on the target bioelectric signal to identify and suppress stable noise in the frequency domain;

[0031] The removal module is used to analyze the instantaneous noise of the target bioelectric signal using wavelet transform and remove transient interference in the time domain.

[0032] In one embodiment, the processing module includes:

[0033] A determination unit, used to determine the relative intensity of the environmental noise according to the environmental noise and the bioelectric signal;

[0034] An adjustment unit, used to dynamically adjust a step size parameter of a preset averaging algorithm according to the relative strength;

[0035] The processing unit is used to update the filter weight of the preset mean algorithm based on the adjusted step size parameter, and filter the input signal based on the filter weight to obtain the target bioelectric signal.

[0036] In one embodiment, the determining unit includes:

[0037] A first determination subunit is used to determine a noise power ratio according to the environmental noise and the bioelectric signal;

[0038] The second determining subunit is used to determine the relative intensity of the environmental noise according to the noise power ratio.

[0039] In one embodiment, the first determining subunit is specifically configured to:

[0040] The environmental noise and the bioelectric signal are input into a preset noise power ratio calculation formula to calculate the noise power ratio.

[0041] In one embodiment, the processing unit is specifically configured to:

[0042] The adjusted step length parameter is normalized based on the energy of the input signal to obtain the fused step length parameter;

[0043] Update the filter weights of the preset mean algorithm based on the fused step size parameters.

[0044] In one embodiment, the fused step size parameter is expressed as:

[0045]

[0046] Among them, μ norm (n) is the step size parameter after fusion, μ(n) is the adjusted step size parameter, is the bi-norm of the input signal, indicating the Euclidean length of the input signal in the M-dimensional space, x(n-1) is the input signal vector, M is the filter order, and ∈ is a constant.

[0047] In one embodiment, the filter weight of the preset mean algorithm is updated based on the fused step size parameter, which is expressed as:

[0048] ω i (n+1)=w i (n)+2·μ norm (n)·e(n)·x(ni)

[0049] Among them, ω i (n+1) is the i-th weight coefficient after update at the n+1th iteration, w i (n) is the ith weight coefficient at the nth iteration, e(n) is the error signal of the nth iteration, and x(ni) is the value of the input signal at the nith moment.

[0050] In a third aspect, an embodiment of the present application provides an electronic device, including:

[0051] Memory and processing modules;

[0052] The memory is used to store computer programs;

[0053] The processing module is used to execute the computer program and implement the steps of the bioelectric signal anti-interference method of the first aspect as described above when executing the computer program.

[0054] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program;

[0055] When the computer program is executed by one or more processing modules, the one or more processing modules execute the steps of the bioelectric signal anti-interference method of the first aspect as described above.

[0056] The embodiments of the present application provide a method, device, equipment and storage medium for bioelectric signal anti-interference, wherein the method for bioelectric signal anti-interference includes: real-time collection of bioelectric signals and detection of environmental noise; filtering the input signal according to the environmental noise and the bioelectric signal to obtain the target bioelectric signal; performing short-time Fourier transform on the target bioelectric signal to identify and suppress stable noise in the frequency domain; using wavelet transform to analyze the instantaneous noise of the target bioelectric signal, and removing transient interference in the time domain. By utilizing the collected environmental noise and dynamically adjusting the parameters of the filter, the interference overlapping with the bioelectric signal can be removed more accurately, and the stable noise and transient noise are further processed respectively by different algorithms in the frequency domain and time domain, which solves the problem that the traditional filtering method is ineffective in processing stable noise and transient noise, but is prone to signal distortion or false detection, thereby improving the transmission quality of weak bioelectric signals. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] 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.

[0058] Figure 1 A schematic diagram of a process for the bioelectric signal anti-interference method provided in an embodiment of the present application;

[0059] Figure 2 A schematic diagram of the structure of a bioelectric signal anti-interference device provided in an embodiment of the present application;

[0060] Figure 3 A schematic diagram of the structure of the bioelectric signal anti-interference device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0061] 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.

[0062] 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.

[0063] 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.

[0064] 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.

[0065] The technical solution provided by this application will be described in detail below with reference to the accompanying drawings.

[0066] See also Figure 1 , Figure 1 The flowchart of the bioelectric signal anti-interference method provided in the embodiment of the present application is shown in FIG. The bioelectric signal anti-interference method provided in the embodiment of the present application is performed by a device having signal acquisition and processing functions. Specifically, Figure 1 As shown, the bioelectric signal anti-interference method provided in the embodiment of the present application includes steps S101 to S104. The details are as follows:

[0067] S101: Collect bioelectric signals and detect environmental noise in real time.

[0068] Bioelectric signals reflect physiological activities in the human body (such as heartbeat, brain waves, muscle contraction, etc.) and are important data for medical diagnosis and health monitoring. Since these signals are very weak (usually in the μV to mV range), they must be collected in real time to avoid missing key events or signal distortion.

[0069] For example, bioelectric signals can be collected in real time by attaching electrodes to the surface of the skin.

[0070] Environmental noise, such as power frequency noise, electromagnetic interference, mechanical vibration, etc., can seriously affect the quality of bioelectric signals, causing distortion or loss of bioelectric signals. Environmental noise can be detected by corresponding sensors.

[0071] S102: Filter the input signal according to the environmental noise and the bioelectric signal to obtain the target bioelectric signal.

[0072] In one embodiment, an input signal is filtered based on environmental noise and bioelectric signals to obtain a target bioelectric signal, including: determining the relative intensity of environmental noise based on the environmental noise and the bioelectric signal; dynamically adjusting the step size parameter of a preset mean algorithm based on the relative intensity; updating the filter weight of the preset mean algorithm based on the adjusted step size parameter, and filtering the input signal based on the filter weight to obtain a target bioelectric signal.

[0073] In one embodiment, determining the relative intensity of the environmental noise based on the environmental noise and the bioelectric signal includes: determining a noise power ratio based on the environmental noise and the bioelectric signal; and determining the relative intensity of the environmental noise based on the noise power ratio.

[0074] In one embodiment, determining the noise power ratio based on the environmental noise and the bioelectric signal includes: inputting the environmental noise and the bioelectric signal into a preset noise power ratio calculation formula to calculate the noise power ratio.

[0075] Among them, the preset noise power ratio calculation formula is:

[0076]

[0077] Among them, p b is the average power of the bioelectric signal, p n is the average power of the ambient noise.

[0078] It can be seen from the above formula that the higher the noise power ratio, the stronger the bioelectric signal is than the noise, the relatively weaker the environmental noise is, and the signal is easy to be correctly identified and processed. If the noise power ratio is too low, it means that the bioelectric signal is submerged by the noise, making it difficult to effectively extract the bioelectric signal.

[0079] Exemplarily, a correlation relationship between the noise power ratio and the relative intensity of the ambient noise is pre-established, and after the noise power ratio is calculated, the relative intensity of the ambient noise can be determined based on the correlation relationship.

[0080] In addition, it should be noted that the filter weights of the preset mean algorithm are also called filter weights, which are dynamically updated over time to minimize the error between the input signal (including bioelectric signals and environmental noise) and the target signal. The step size parameter controls the speed and amplitude of the filter weight update. In order to be able to filter stably and efficiently under different input signal conditions, the step size parameter needs to be adaptively adjusted. Specifically, according to the relative intensity of the environmental noise, the step size parameter of the preset mean algorithm is dynamically adjusted, which can be expressed as:

[0081]

[0082] Among them, μ n is the adjusted step size parameter, and μ0 is the initial step size parameter.

[0083] In one embodiment, the filtering weights of the preset mean algorithm are updated based on the adjusted step size parameters, including: normalizing the adjusted step size parameters based on the energy of the input signal to obtain a fused step size parameter; and updating the filtering weights of the preset mean algorithm based on the fused step size parameters.

[0084] In one embodiment, the fused step size parameter is expressed as:

[0085]

[0086] Among them, μ norm (n) is the step size parameter after fusion, μ(n) is the adjusted step size parameter, is the bi-norm of the input signal, indicating the Euclidean length of the input signal in the M-dimensional space, x(n-1) is the input signal vector, M is the filter order, and ∈ is a constant.

[0087] In one embodiment, the filter weight of the preset mean algorithm is updated based on the fused step size parameter, which is expressed as:

[0088] ω i (n+·1))=w i (n)++2·μ norm (n)·e(n)·x(ni)

[0089] Among them, ω i (n+1) is the i-th weight coefficient after update at the n+1th iteration, w i (n) is the ith weight coefficient at the nth iteration, e(n) is the error signal of the nth iteration, and x(ni) is the value of the input signal at the nith moment.

[0090] S103: Perform short-time Fourier transform on the target bioelectric signal to identify and suppress stable noise in the frequency domain.

[0091] The short-time Fourier transform is different from the common Fourier transform. In this embodiment, the non-stationary signal is analyzed by the short-time Fourier transform, that is, the frequency component that changes in time is analyzed. It divides the target biological bioelectric signal into several short-time windows, and performs Fourier transform on the signal in each window, so as to obtain the joint distribution of the signal in time and frequency. Specifically, the process of short-time Fourier transform can be expressed as:

[0092]

[0093] Among them, x(t) is the target bioelectric signal, ω(t-τ) is the sliding window function, t is the current time, f is the frequency, and τ is the integral variable, which represents the time point in the signal x(t).

[0094] S104: Analyze the instantaneous noise of the target bioelectric signal using wavelet transform, and remove transient interference in the time domain.

[0095] The process of using wavelet transform to analyze the instantaneous noise of the target bioelectric signal can be expressed as:

[0096]

[0097] Among them, C(a, b) is the wavelet transform coefficient, which represents the characteristics of the signal at scale a and time position b, and reflects the similarity between the signal and the wavelet coefficient. is the conjugate of the wavelet function, which is used to analyze the signal and ensure that the transformation result is a real number. a is the scale parameter, which is used to control the degree of expansion and contraction of the wavelet function. b is the translation parameter, which is used to control the position of the wavelet function on the time axis.

[0098] The wavelet transform formula describes the process of wavelet transform, that is, by analyzing the bioelectric signal x(t) at different scales a and positions b, it can effectively capture the local features in the bioelectric signal and accurately locate the instantaneous noise and mutation in the bioelectric signal.

[0099] For the bioelectric signal after wavelet conversion, further removing transient interference in the time domain can ensure the smoothness and integrity of the bioelectric signal. This is because the remaining part of the instantaneous spike or artifact will cause a sudden change in the bioelectric signal. The processing in the time domain can improve the signal processing process and ensure the quality of the final signal.

[0100] The target bioelectric signal is a bioelectric signal that has been initially filtered to remove some environmental noise, but it may still be interfered by stable noise and transient noise in the frequency domain. Therefore, the embodiment of the present application further uses Fourier transform to identify and suppress stable noise in the frequency domain (such as 50Hz power frequency noise), uses wavelet transform to detect and remove transient noise in the target bioelectric signal (such as motion artifacts, spike interference, etc.), and performs smoothing and interpolation in the time domain to eliminate residual transient interference and ensure signal integrity.

[0101] Through the above analysis, it can be known that the bioelectric signal anti-interference method provided by the embodiment of the present application includes: real-time collection of bioelectric signals and detection of environmental noise; filtering the input signal according to the environmental noise and the bioelectric signal to obtain the target bioelectric signal; performing short-time Fourier transform on the target bioelectric signal to identify and suppress the stable noise in the frequency domain; using wavelet transform to analyze the instantaneous noise of the target bioelectric signal, and removing transient interference in the time domain. By utilizing the collected environmental noise and dynamically adjusting the parameters of the filter, the interference overlapping with the bioelectric signal can be removed more accurately, and the stable noise and transient noise can be processed respectively by different algorithms in the frequency domain and time domain, which solves the problem that the traditional filtering method is ineffective in processing stable noise and transient noise, but is prone to signal distortion or false detection, thereby improving the transmission quality of weak bioelectric signals.

[0102] See also Figure 2 As shown, Figure 2This is a schematic diagram of the structure of the bioelectric signal anti-interference device provided in the embodiment of the present application. Figure 2 It can be seen that the bioelectric signal anti-interference device 20 provided in the embodiment of the present application includes:

[0103] The acquisition module 201 is used to collect bioelectric signals in real time and detect environmental noise;

[0104] A processing module 202 is used to filter the input signal according to the environmental noise and the bioelectric signal to obtain a target bioelectric signal;

[0105] An identification module 203 is used to perform short-time Fourier transform on the target bioelectric signal to identify and suppress stable noise in the frequency domain;

[0106] The removal module 204 is used to analyze the instantaneous noise of the target bioelectric signal using wavelet transform and remove transient interference in the time domain.

[0107] In one embodiment, the processing module 202 includes:

[0108] A determination unit, used to determine the relative intensity of the environmental noise according to the environmental noise and the bioelectric signal;

[0109] An adjustment unit, used to dynamically adjust a step size parameter of a preset averaging algorithm according to the relative strength;

[0110] The processing unit is used to update the filter weight of the preset mean algorithm based on the adjusted step size parameter, and filter the input signal based on the filter weight to obtain the target bioelectric signal.

[0111] In one embodiment, the determining unit includes:

[0112] A first determination subunit is used to determine a noise power ratio according to the environmental noise and the bioelectric signal;

[0113] The second determining subunit is used to determine the relative intensity of the environmental noise according to the noise power ratio.

[0114] In one embodiment, the first determining subunit is specifically configured to:

[0115] The environmental noise and the bioelectric signal are input into a preset noise power ratio calculation formula to calculate the noise power ratio.

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

[0117] The adjusted step length parameter is normalized based on the energy of the input signal to obtain the fused step length parameter;

[0118] Update the filter weights of the preset mean algorithm based on the fused step size parameters.

[0119] In one embodiment, the fused step size parameter is expressed as:

[0120]

[0121] Among them, μ norm (n) is the step size parameter after fusion, μ(n) is the adjusted step size parameter, is the bi-norm of the input signal, indicating the Euclidean length of the input signal in the M-dimensional space, x(n-1) is the input signal vector, M is the filter order, and ∈ is a constant.

[0122] In one embodiment, the filter weight of the preset mean algorithm is updated based on the fused step size parameter, which is expressed as:

[0123] ω i (n+1)=w i (n)+2·μ norm (n)·e(n))·x(ni)

[0124] Among them, ω i (n+1) is the i-th weight coefficient after update at the n+1th iteration, w i (n) is the ith weight coefficient at the nth iteration, e(n) is the error signal of the nth iteration, and x(ni) is the value of the input signal at the nith moment.

[0125] 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.

[0126] See also Figure 3 As shown, Figure 3 A schematic block diagram of a bioelectric signal anti-interference device provided in an embodiment of the present application.

[0127] Exemplarily, the bioelectric signal anti-interference device 30 includes a processing module 301 and a memory 302 .

[0128] 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.

[0129] Specifically, the processing module 301 may be a micro-controller unit (MCU), a central processing unit (CPU) or a digital signal processing module (DSP).

[0130] 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.

[0131] The processing module 301 is used to run the computer program stored in the memory 302 and implement the steps of the above-mentioned bioelectric signal anti-interference method when executing the computer program.

[0132] 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:

[0133] Collect bioelectric signals and detect environmental noise in real time;

[0134] Filter the input signal according to the environmental noise and the bioelectric signal to obtain the target bioelectric signal;

[0135] Perform short-time Fourier transform on the target bioelectric signal to identify and suppress stable noise in the frequency domain;

[0136] Wavelet transform is used to analyze the instantaneous noise of the target bioelectric signal and remove transient interference in the time domain.

[0137] In one embodiment, filtering the input signal according to the environmental noise and the bioelectric signal to obtain the target bioelectric signal includes:

[0138] Determine the relative intensity of environmental noise based on environmental noise and bioelectric signals;

[0139] Dynamically adjust the step size parameters of the preset averaging algorithm based on relative strength;

[0140] The filter weights of the preset mean algorithm are updated based on the adjusted step size parameters, and the input signal is filtered based on the filter weights to obtain the target bioelectric signal.

[0141] In one embodiment, determining the relative intensity of the environmental noise according to the environmental noise and the bioelectric signal includes:

[0142] Determine the noise power ratio based on the environmental noise and the bioelectric signal;

[0143] Based on the noise power ratio, the relative strength of the ambient noise is determined.

[0144] In one embodiment, determining the noise power ratio according to the environmental noise and the bioelectric signal includes:

[0145] The environmental noise and the bioelectric signal are input into a preset noise power ratio calculation formula to calculate the noise power ratio.

[0146] In one embodiment, updating the filter weights of the preset mean algorithm based on the adjusted step size parameter includes:

[0147] The adjusted step length parameter is normalized based on the energy of the input signal to obtain the fused step length parameter;

[0148] Update the filter weights of the preset mean algorithm based on the fused step size parameters.

[0149] In one embodiment, the fused step size parameter is expressed as:

[0150]

[0151] Among them, μ norm (n) is the step size parameter after fusion, μ(n) is the adjusted step size parameter, is the bi-norm of the input signal, indicating the Euclidean length of the input signal in the M-dimensional space, x(n-1) is the input signal vector, M is the filter order, and ∈ is a constant.

[0152] In one embodiment, the filter weight of the preset mean algorithm is updated based on the fused step size parameter, which is expressed as:

[0153] ω i (n+1)=w i (n)+2·μ norm (n)·e(n))·x(ni)

[0154] Among them, ω i (n+1) is the i-th weight coefficient after update at the n+1th iteration, w i (n) is the ith weight coefficient at the nth iteration, e(n) is the error signal of the nth iteration, and x(ni) is the value of the input signal at the nith moment.

[0155] The computer-readable storage medium may be an internal storage unit of the bioelectric signal anti-interference device in the aforementioned embodiment, such as a hard disk or memory of the bioelectric signal anti-interference device. The computer-readable storage medium may also be an external storage device of the bioelectric signal anti-interference device, 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 bioelectric signal anti-interference device.

[0156] 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.

[0157] 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.

[0158] 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 bioelectric signal anti-interference method, characterized in that: include: Collect bioelectric signals and detect environmental noise in real time; Filtering the input signal according to the environmental noise and the bioelectric signal to obtain a target bioelectric signal; Performing a short-time Fourier transform on the target bioelectric signal to identify and suppress stable noise in the frequency domain; Wavelet transform is used to analyze the instantaneous noise of the target bioelectric signal, and transient interference is removed in the time domain.

2. The bioelectric signal anti-interference method according to claim 1, characterized in that: The filtering the input signal according to the environmental noise and the bioelectric signal to obtain the target bioelectric signal includes: Determining a relative intensity of the environmental noise according to the environmental noise and the bioelectric signal; Dynamically adjust a step size parameter of a preset averaging algorithm according to the relative strength; The filter weight of the preset mean algorithm is updated based on the adjusted step size parameter, and the input signal is filtered based on the filter weight to obtain the target bioelectric signal.

3. The bioelectric signal anti-interference method according to claim 2, characterized in that: The determining the relative intensity of the environmental noise according to the environmental noise and the bioelectric signal includes: Determining a noise power ratio according to the environmental noise and the bioelectric signal; The relative intensity of the environmental noise is determined according to the noise power ratio.

4. The bioelectric signal anti-interference method according to claim 3, characterized in that: The step of determining the noise power ratio according to the environmental noise and the bioelectric signal includes: The environmental noise and the bioelectric signal are input into a preset noise power ratio calculation formula to calculate the noise power ratio.

5. The bioelectric signal anti-interference method according to claim 2, characterized in that: The updating of the filter weight of the preset mean algorithm based on the adjusted step size parameter includes: Normalizing the adjusted step length parameter based on the energy of the input signal to obtain a fused step length parameter; The filter weight of the preset mean algorithm is updated based on the fused step size parameter.

6. The bioelectric signal anti-interference method according to claim 5, characterized in that: The fused step size parameter is expressed as: Among them, μ norm (n) is the step size parameter after fusion, μ(n) is the adjusted step size parameter, is the bi-norm of the input signal, indicating the Euclidean length of the input signal in the M-dimensional space, x(n-1) is the input signal vector, M is the filter order, and ∈ is a constant.

7. The bioelectric signal anti-interference method according to claim 5, characterized in that: The filtering weight of the preset mean algorithm is updated based on the fused step size parameter, which is expressed as: oh i (n+1)=w i (n)+2·μ norm (n)·e(n)·x(ni) Among them, ω i (n+1) is the i-th weight coefficient after update at the n+1th iteration, w i (n) is the ith weight coefficient at the nth iteration, e(n) is the error signal of the nth iteration, and x(ni) is the value of the input signal at the nith moment.

8. A bioelectric signal anti-interference device, characterized in that: include: Acquisition module, used to collect bioelectric signals in real time and detect environmental noise; A processing module, used for filtering the input signal according to the environmental noise and the bioelectric signal to obtain a target bioelectric signal; An identification module, used for performing short-time Fourier transform on the target bioelectric signal to identify and suppress stable noise in the frequency domain; The removal module is used to analyze the instantaneous noise of the target bioelectric signal using wavelet transform and remove transient interference in the time domain.

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 bioelectric signal anti-interference method as described in claims 1 to 7 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 bioelectric signal anti-interference method as described in claims 1 to 7.

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