Diaphragm electromyography signal noise reduction method, system, device and storage medium

Abstract

The present invention discloses a diaphragm electromyography signal noise reduction method, system, device and storage medium, and relates to the field of computer technology. The surface diaphragm electromyography signal is decomposed by multi-scale wavelets, and the wavelet coefficients of the corresponding scale are determined based on the frequency range of the electrocardiogram signal. Then, the wavelet coefficients are squared to amplify the wavelet characteristics of the electrocardiogram interference signal to obtain the wavelet energy coefficients. Then, the interference signal is located by using a double threshold algorithm, which can effectively distinguish noise from useful diaphragm electromyography signals. The average energy threshold is determined based on the wavelet energy coefficients in the adjacent interval of the interference interval where the electrocardiogram position is located, and the interference interval is subjected to noise reduction processing according to the average energy threshold to remove the interference signal therein, and the wavelet energy coefficients after noise reduction are restored and wavelet reconstructed to obtain the diaphragm electromyography signal. The present application can improve the signal-to-noise ratio of the diaphragm electromyography signal and retain more effective diaphragm electromyography signals.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to a diaphragm electromyographic signal noise reduction method, system, device and storage medium. Background Art

[0002] The electromyographic signal (EMGdi) generated by the diaphragm during breathing reflects important physiological information of the respiratory system and is an important basis for diagnosing respiratory diseases such as chronic obstructive pulmonary disease (COPD) and asthma. The acquisition of EMGdi signals is divided into invasive and surface detection. The surface electrode acquisition method has been applied to clinical respiratory monitoring due to its advantages of non-invasiveness, convenience and low cost. However, surface EMGdi is a micro-electric signal, which is affected by the power frequency and generates interference, motion artifacts, electromyography and electrocardiogram (ECG) signals of other respiratory muscles during the acquisition process. The amplitude of the ECG is much higher than EMGdi, and its main frequency band overlaps with EMGdi, which has an adverse effect on the extraction of EMGdi signals. In order to filter out the interference of the central electrical signal of EMGdi, clipping substitution, adaptive noise cancellation (ANC) and event synchronization cancellation (ESC) are used. These methods require the collection of additional ECG signals and use them as reference input to filter out the interference of ECG signals. At present, wavelet algorithm is also used for diaphragm EMG denoising, that is, the hard threshold or soft threshold method of wavelet is used to remove the noise signal as a whole, but this method will cause the loss of diaphragm EMG signal and incomplete removal of noise signal. Summary of the invention

[0003] The present invention aims to solve at least one of the technical problems existing in the prior art. To this end, the present invention proposes a diaphragm electromyography signal noise reduction method, system, device and storage medium, which can improve the signal-to-noise ratio of the diaphragm electromyography signal and retain more effective diaphragm electromyography signals.

[0004] On the one hand, an embodiment of the present invention provides a method for reducing noise of diaphragm electromyographic signals, comprising the following steps:

[0005] Collect surface diaphragm electromyographic signals;

[0006] Performing multi-scale wavelet decomposition on the surface diaphragm electromyographic signal, and determining wavelet coefficients of corresponding scales based on the frequency range of the electrocardiogram signal;

[0007] Squaring the wavelet coefficients to obtain wavelet energy coefficients;

[0008] A double threshold algorithm is used to detect the peak value of the wavelet energy coefficient to determine the ECG position;

[0009] Determine the interference interval according to the ECG position;

[0010] Determining an average energy threshold according to wavelet energy coefficients in adjacent intervals of the interference interval;

[0011] Performing noise reduction processing on the interference interval according to the average energy threshold to obtain a wavelet energy coefficient after noise reduction;

[0012] The diaphragm electromyographic signal is obtained by restoring and reconstructing the wavelet energy coefficient after denoising.

[0013] According to some embodiments of the present invention, the use of a dual threshold algorithm to detect the peak value of the wavelet energy coefficient and determine the ECG position includes the following steps:

[0014] According to the positive and negative change points of the slopes of every two points on the wavelet energy coefficient, several continuous local peaks are determined;

[0015] Initialize the high threshold and the low threshold according to the average value of several consecutive local peaks;

[0016] Determine the wavelet energy coefficient currently located between the low threshold and the high threshold as the current peak value;

[0017] The high threshold and the low threshold are updated according to the current peak value, and the peak value detection at the next moment is performed according to the updated high threshold and the low threshold.

[0018] According to some embodiments of the present invention, initializing the high threshold and the low threshold according to the average value of several consecutive local peak values ​​comprises the following steps:

[0019] Multiplying the average of several consecutive local peaks by the first multiple to obtain an initial high threshold;

[0020] An average value of several consecutive local peak values ​​is multiplied by a second multiple to obtain an initial low threshold value, wherein the first multiple is greater than 0.5 and less than 1, and the second multiple is greater than 0 and less than 0.5.

[0021] According to some embodiments of the present invention, the updating formula of the high threshold is as follows:

[0022]

[0023] The updating formula of the low threshold is as follows:

[0024]

[0025] Where P represents the current peak value, Represents the average value of several consecutive local peaks before the current peak, P m Represents the sum of several consecutive local peaks before the current peak, th h Indicates the high threshold before update, thh ′ represents the updated high threshold, th l Indicates the low threshold before update, th l ′ represents the updated low threshold, th0 and th1 are preset empirical parameters, k1, k2, k3, k4 are all threshold coefficients less than 1, where k1 <k2,k3<k4。

[0026] According to some embodiments of the present invention, determining the average energy threshold according to the wavelet energy coefficients in the adjacent intervals of the interference interval comprises the following steps:

[0027] Determine a left adjacent interval and a right adjacent interval of the interference interval according to a preset time length;

[0028] Determine the wavelet energy mean value according to the sum of the wavelet energy coefficients of the left adjacent interval and the sum of the wavelet energy coefficients of the right adjacent interval;

[0029] Compare the sum of the wavelet energy coefficients of the left adjacent interval and the sum of the wavelet energy coefficients of the right adjacent interval, and select the adjacent interval with the smaller sum of the wavelet energy coefficients;

[0030] The average energy threshold is determined according to the wavelet energy mean and the wavelet energy coefficients in the selected adjacent intervals.

[0031] According to some embodiments of the present invention, determining the average energy threshold according to the wavelet energy mean and the wavelet energy coefficients in the selected adjacent intervals comprises the following steps:

[0032] Determining the standard deviation of the wavelet energy coefficients in the selected adjacent intervals and the wavelet energy mean;

[0033] The standard deviation is added to the wavelet energy mean to obtain an average energy threshold.

[0034] According to some embodiments of the present invention, performing noise reduction processing on the interference interval according to the average energy threshold comprises the following steps:

[0035] The wavelet energy coefficients of the interference intervals that are greater than the average energy threshold are replaced by the average energy threshold.

[0036] On the other hand, an embodiment of the present invention also provides a diaphragm electromyography signal noise reduction system, comprising:

[0037] The first module is used to collect surface diaphragm electromyographic signals;

[0038] The second module is used to perform multi-scale wavelet decomposition on the surface diaphragm electromyographic signal and determine the wavelet coefficients of the corresponding scale based on the frequency range of the electrocardiogram signal;

[0039] The third module is used to square the wavelet coefficients to obtain wavelet energy coefficients;

[0040] The fourth module is used to detect the peak value of the wavelet energy coefficient by using a double threshold algorithm to determine the ECG position;

[0041] A fifth module is used to determine the interference interval according to the ECG position;

[0042] A sixth module is used to determine an average energy threshold according to wavelet energy coefficients in adjacent intervals of the interference interval;

[0043] The seventh module performs noise reduction processing on the interference interval according to the average energy threshold to obtain a wavelet energy coefficient after noise reduction;

[0044] The eighth module is used to restore and reconstruct the wavelet energy coefficients after noise reduction to obtain the diaphragm electromyographic signal.

[0045] On the other hand, an embodiment of the present invention further provides a diaphragm electromyographic signal noise reduction device, comprising:

[0046] at least one processor;

[0047] at least one memory for storing at least one program;

[0048] When the at least one program is executed by the at least one processor, the at least one processor implements the diaphragm electromyography signal noise reduction method as described above.

[0049] On the other hand, an embodiment of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer-executable instructions, and the computer-executable instructions are used to enable a computer to execute the diaphragm electromyography signal noise reduction method as described above.

[0050] The above technical solution of the present invention has at least one of the following advantages or beneficial effects: decomposing the surface diaphragm electromyographic signal through multi-scale wavelets, and determining the wavelet coefficients of the corresponding scale based on the frequency range of the electrocardiogram signal, and then squaring the wavelet coefficients to amplify the wavelet characteristics of the electrocardiogram interference signal to obtain the wavelet energy coefficients, and then using a double threshold algorithm to locate the interference signal, which can effectively distinguish between noise and useful diaphragm electromyographic signals. Determine the average energy threshold based on the wavelet energy coefficients in the adjacent interval of the interference interval where the electrocardiogram position is located, perform noise reduction processing on the interference interval according to the average energy threshold to remove the interference signal therein, and perform restoration and wavelet reconstruction operations on the wavelet energy coefficients after noise reduction to obtain the diaphragm electromyographic signal. The present application can improve the signal-to-noise ratio of the diaphragm electromyographic signal and retain more effective diaphragm electromyographic signals. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 is a flow chart of a method for reducing noise of diaphragm electromyographic signals provided by an embodiment of the present invention;

[0052] Figure 2 It is a schematic diagram of a diaphragm electromyographic signal noise reduction device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0053] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar components or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and cannot be understood as limiting the present invention.

[0054] In the description of the present invention, it should be understood that descriptions involving orientation, such as up, down, left, right, etc., the orientations or positional relationships indicated are based on the orientations or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present invention.

[0055] In the description of the present invention, if there is a description of first, second, etc., it is only for the purpose of distinguishing technical features, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features or implicitly indicating the sequence of the indicated technical features.

[0056] The embodiment of the present invention provides a method for reducing noise of diaphragm electromyographic signal, referring to Figure 1 As shown, the diaphragm electromyography signal noise reduction method of the embodiment of the present invention includes but is not limited to step S110, step S120, step S130, step S140, step S150, step S160, step S170 and step S180.

[0057] Step S110, collecting surface diaphragm electromyographic signals;

[0058] Step S120, performing multi-scale wavelet decomposition on the surface diaphragm electromyographic signal, and determining the wavelet coefficients of the corresponding scale based on the frequency range of the electrocardiogram signal;

[0059] Step S130, square the wavelet coefficients to obtain wavelet energy coefficients;

[0060] Step S140, using a double threshold algorithm to detect the peak value of the wavelet energy coefficient and determine the ECG position;

[0061] Step S150, determining the interference interval according to the ECG position;

[0062] Step S160, determining an average energy threshold according to wavelet energy coefficients in intervals adjacent to the interference interval;

[0063] Step S170, performing noise reduction processing on the interference interval according to the average energy threshold to obtain a noise-reduced wavelet energy coefficient;

[0064] Step S180, performing restoration and wavelet reconstruction operations on the denoised wavelet energy coefficients to obtain diaphragm electromyographic signals.

[0065] In some embodiments of step S110, the acquisition motor is placed close to the diaphragm to sample and obtain the surface diaphragm electromyographic signal. Due to the presence of respiratory muscles or electrocardiographic movement, the collected surface diaphragm electromyographic signal contains not only the diaphragm electromyographic signal itself, but also other interference signals, the main interference signal being the electrocardiographic signal. The sampling frequency of the surface diaphragm electromyographic signal in this embodiment can be 2000Hz.

[0066] In some embodiments of step S120, the main frequency range of the ECG signal energy is mainly concentrated in 0.5-50Hz. When the data sampling rate is 2000Hz, the lowest frequency range obtained after 5-scale wavelet decomposition is 0-62.5Hz. At this time, the ECG signal in the wavelet coefficient of this scale is the most obvious, and the diaphragm electromyography signal is weak. The wavelet coefficients of 5 scales are selected to locate the ECG signal, so as to obtain the ECG signal position information for application in ECG interference processing at each scale. Specifically, the wavelet function Haar is selected to perform five-scale wavelet decomposition on the surface diaphragm electromyography signal to obtain the high-frequency coefficient and low-frequency coefficient of the wavelet, and the low-frequency coefficient on the fifth scale is taken as the wavelet coefficient. The frequency range of the wavelet coefficient on the fifth scale is 0-62.5Hz, and the ECG signal is mainly concentrated on this scale.

[0067] In some embodiments of step S130, the wavelet coefficients in step S120 are squared to amplify the difference between the ECG interference and the diaphragm myoelectric signal, so as to facilitate more accurate QRS wave positioning. Specifically, the wavelet coefficients are processed by formula (1) to obtain the wavelet system energy coefficients.

[0068] PWx(j,k)=|Cx(j,k)| 2 ; (1)

[0069] Among them, Cx(j,k) represents the wavelet coefficient, PWx(j,k) represents the wavelet energy coefficient, j represents the scale, and k represents the sampling point.

[0070] In some embodiments of step S140, a dual threshold algorithm is used to detect the peak value of the wavelet energy coefficient to determine the ECG position, which includes steps S210 to S240.

[0071] Step S210, determining a number of continuous local peaks according to the positive and negative change points of the slopes between every two points on the wavelet energy coefficient;

[0072] Step S220, initializing a high threshold and a low threshold according to an average value of a plurality of consecutive local peak values;

[0073] Step S230, determining the wavelet energy coefficient currently between the low threshold and the high threshold as the current peak value;

[0074] Step S240, updating the high threshold and the low threshold according to the current peak value, and performing peak detection at the next moment according to the updated high threshold and the low threshold.

[0075] Exemplarily, the positive and negative change points of the slopes of every two points on the wavelet energy coefficient are determined to determine the five most recent consecutive local peaks, and the average value of the above five peaks is multiplied by the first multiple as the initial high threshold, and the average value of the above five peaks is multiplied by the second multiple as the initial low threshold to form subsequent high and low double threshold peak detection. In this embodiment, the first multiple is 0.5 to 1, and the second multiple is 0 to 0.5. Preferably, the first multiple is 0.75 and the second multiple is 0.25.

[0076] In the peak detection and high and low threshold update process of the high and low dual thresholds, the wavelet energy coefficient currently located between the low threshold and the high threshold is determined as the current peak. When it is detected that the current peak is greater than the high threshold, the high threshold and the low threshold are updated to k2 and k1 times the average of the latest five peaks, respectively. When the detected current peak is between the low threshold and the high threshold, the high threshold is updated to the old high threshold minus the weighted average of the latest six peaks and the difference between the average of the previous five peaks, and the low threshold is updated to 0.4 multiplied by the weighted average of the latest six peaks. Specifically, the high threshold is updated as shown in formula (2), and the low threshold is updated as shown in formula (3).

[0077]

[0078] The updating formula of the low threshold is as follows:

[0079]

[0080] Where P represents the current peak value, Represents the average value of several consecutive local peaks before the current peak, P m Represents the sum of several consecutive local peaks before the current peak, th h Indicates the high threshold before update, th h ′ represents the updated high threshold, th l Indicates the low threshold before update, th l′ represents the updated low threshold, th0 and th1 are preset empirical parameters, k1, k2, k3, k4 are all threshold coefficients less than 1, where k1 <k2,k3<k4。

[0081] For example, k1, k2, k3, and k4 can be set to 0.3, 0.7, 0.2, and 0.8, respectively. In the high threshold update formula, k1:k2=3:7 is set to ensure that there is a wide enough threshold to detect the peak value and maintain the robustness of the threshold change; in the low threshold update formula, k3:k4=2:8 is set to increase the current peak weight so that the fluctuation of the low threshold will not be missed due to the abnormal situation of the current threshold.

[0082] In formula (2) and formula (3), two empirical parameters th0 and th1 are specified. When the detection threshold is less than these two empirical parameters, the detected signal is the EMGdi signal, thereby avoiding missed detection.

[0083] In addition, in order to reduce the false detection rate and avoid repeated detection of the same QRS wave, the refractory period detection is added to the algorithm, that is, when the distance between two consecutive peaks is detected to be less than the preset time interval, only the largest peak of the two peaks is taken as the R wave position. It should be noted that the duration of a normal QRS wave is about 0.06 to 0.1s, so the preset time interval can be a value greater than 0.1s, and the value of the preset time interval in the embodiment of the present invention can be 0.2s.

[0084] In this embodiment, a high and low dual threshold peak detection algorithm is used to effectively identify and locate the peak of the QRS wave, and the high and low dual threshold and refractory period detection can effectively improve the robustness of the threshold and the accuracy of recognition, and reduce the probability of false detection and missed detection.

[0085] In some embodiments of step S150, the interference interval is determined by:

[0086] Taking the R peak value of the QRS wave detected above as the benchmark, the time domain position of the R peak value is the ECG position, and the interference interval is set to 0.5s before and after the ECG position. The interference interval is shown in formula (4):

[0087] T=[Pn-0.5t×f,Pn+0.5t×f]; (4)

[0088] Among them, Pn represents the detected nth R peak value, f is the frequency of the corresponding layer of wavelet decomposition, and t represents the value t as the interference duration. The value is selected according to individual differences. Generally, the value of t is between 0.06 and 0.1.

[0089] In some embodiments of step S160, the step of determining the average energy threshold according to the wavelet energy coefficients in the adjacent intervals of the interference interval includes steps S310 to S340.

[0090] Step S310, determining a left adjacent interval and a right adjacent interval of the interference interval according to a preset time length;

[0091] Step S320, determining the wavelet energy mean value according to the sum of the wavelet energy coefficients of the left adjacent interval and the sum of the wavelet energy coefficients of the right adjacent interval;

[0092] Step S330, comparing the sum of the wavelet energy coefficients of the left adjacent interval and the sum of the wavelet energy coefficients of the right adjacent interval, and selecting the adjacent interval with the smaller sum of the wavelet energy coefficients;

[0093] Step S340: determining an average energy threshold according to the wavelet energy mean and the wavelet energy coefficients in the selected adjacent intervals.

[0094] In this embodiment, the 0.06s time length on the left side of the interference interval is taken as the left adjacent interval, and the 0.06s time length on the right side of the interference interval is taken as the right adjacent interval. The sum of the wavelet energy coefficients Ar(Pn) of the right adjacent interval and the sum of the wavelet energy coefficients Al(Pn) of the left adjacent interval are calculated respectively, as shown in formulas (5) and (6).

[0095]

[0096]

[0097] Wherein, t1 represents the starting position of the interference interval, t1=Pn-0.5t×f, and t2 represents the ending position of the interference interval, t2=Pn+0.5t×f.

[0098] Determine the wavelet energy mean Avg based on the sum of the wavelet energy coefficients of the left adjacent interval and the right adjacent interval n , as shown in formula (7).

[0099] Avg n =(Ar(Pn)+Al(Pn)) / 0.06×f; (7)

[0100] Find the standard deviation of the wavelet energy coefficient and the wavelet energy mean in the left adjacent interval And the standard deviation of the wavelet energy coefficient and the wavelet energy mean in the right adjacent interval As shown in formulas (8) and (9).

[0101]

[0102]

[0103] Compare the sum of the wavelet energy coefficients of the left adjacent interval and the sum of the wavelet energy coefficients of the right adjacent interval, and select the standard deviation corresponding to the adjacent interval with the smaller sum of wavelet energy coefficients to determine the average energy threshold TH j (P n ), as shown in formulas (10) and (11).

[0104]

[0105] TH j (P n )=Avg n +σ; (11)

[0106] In some embodiments of step S170, after obtaining the average energy threshold, the interference interval T is subjected to noise reduction processing, the wavelet energy coefficients greater than the average energy threshold are replaced by the average energy threshold, and the wavelet energy coefficients less than the average energy threshold are not processed, thereby obtaining a new series of wavelet energy coefficients, as specifically shown in formula (12).

[0107]

[0108] In some embodiments of step S180, after obtaining the new wavelet energy coefficients after noise reduction, the wavelet energy coefficients are squared and assigned positive and negative values ​​to restore the wavelet coefficients after removing ECG interference, and then a clean diaphragm electromyographic signal is obtained through wavelet reconstruction.

[0109] In the above embodiment, the average energy threshold is determined based on the standard deviation of the wavelet energy coefficients of the left and right adjacent intervals of the interference interval and the wavelet energy mean of the adjacent intervals, and the data greater than the average energy threshold is replaced with the threshold, and the data less than the average energy threshold is retained. Compared with directly setting the interference signal to zero and applying the average energy threshold method to filter out ECG interference, that is, when the interference interval overlaps with the diaphragm electromyography signal, simply averaging the wavelet energy coefficients of the left and right adjacent intervals may cause the difference between the threshold and the wavelet energy coefficients of the left and right adjacent intervals to be too large, and the algorithm of the embodiment of the present invention adds the standard deviation feature, which can make the smoothing process of the interference interval lose less diaphragm electromyography signals and retain more diaphragm electromyography energy signal features, so as not to affect the subsequent envelope processing results.

[0110] The embodiment of the present invention further provides a diaphragm electromyographic signal noise reduction system, comprising:

[0111] The first module is used to collect surface diaphragm electromyographic signals;

[0112] The second module is used to perform multi-scale wavelet decomposition on the surface diaphragm electromyographic signal and determine the wavelet coefficients of the corresponding scale based on the frequency range of the electrocardiogram signal;

[0113] The third module is used to square the wavelet coefficients to obtain the wavelet energy coefficients;

[0114] The fourth module is used to detect the peak value of the wavelet energy coefficient using a double threshold algorithm to determine the ECG position;

[0115] The fifth module is used to determine the interference interval according to the ECG position;

[0116] The sixth module is used to determine the average energy threshold according to the wavelet energy coefficient in the adjacent interval of the interference interval;

[0117] The seventh module performs noise reduction processing on the interference interval according to the average energy threshold to obtain the wavelet energy coefficient after noise reduction;

[0118] The eighth module is used to restore and reconstruct the wavelet energy coefficients after noise reduction to obtain the diaphragm electromyographic signal.

[0119] It can be understood that the contents of the above-mentioned diaphragm EMG signal noise reduction method embodiment are all applicable to the present system embodiment, the functions specifically implemented by the present system embodiment are the same as those in the above-mentioned diaphragm EMG signal noise reduction method embodiment, and the beneficial effects achieved are also the same as those achieved by the above-mentioned diaphragm EMG signal noise reduction method embodiment.

[0120] Reference Figure 2 , Figure 2 Schematic diagram of a diaphragm electromyography signal noise reduction device provided by an embodiment of the present invention. The diaphragm electromyography signal noise reduction device of the embodiment of the present invention includes one or more control processors and a memory. Figure 2 A control processor and a memory are taken as an example.

[0121] The control processor and the memory can be connected via a bus or other means. Figure 2 The example of connecting through bus is taken in the following.

[0122] The memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a memory remotely arranged relative to the control processor, and these remote memories may be connected to the diaphragm electromyography signal noise reduction device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0123] Those skilled in the art will understand that Figure 2 The device structure shown in the figure does not constitute a limitation on the diaphragm electromyographic signal noise reduction device, and may include more or fewer components than shown in the figure, or a combination of certain components, or a different arrangement of components.

[0124] The non-transient software program and instructions required to implement the diaphragm electromyogram signal noise reduction method applied to the diaphragm electromyogram signal noise reduction device in the above-mentioned embodiment are stored in the memory. When executed by the control processor, the diaphragm electromyogram signal noise reduction method applied to the diaphragm electromyogram signal noise reduction device in the above-mentioned embodiment is executed.

[0125] In addition, an embodiment of the present invention also provides a computer-readable storage medium, which stores computer-executable instructions. The computer-executable instructions are executed by one or more control processors, so that the one or more control processors can execute the diaphragm electromyography signal denoising method in the above method embodiment.

[0126] It will be appreciated by those skilled in the art that all or some of the steps and systems in the disclosed method above may be implemented as software, firmware, hardware and appropriate combinations thereof. Some physical components or all physical components may be implemented as software executed by a processor, such as a central processing unit, a digital signal processor or a microprocessor, or may be implemented as hardware, or may be implemented as an integrated circuit, such as an application specific integrated circuit. Such software may be distributed on a computer-readable medium, which may include a computer storage medium (or a non-transitory medium) and a communication medium (or a temporary medium). As known to those skilled in the art, the term computer storage medium includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules or other data). Computer storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tapes, disk storage or other magnetic storage devices, or any other medium that may be used to store desired information and may be accessed by a computer. Furthermore, it is well known to those skilled in the art that communication media typically embodies computer readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism, and may include any information delivery media.

[0127] The embodiments of the present invention are described in detail above with reference to the accompanying drawings, but the present invention is not limited to the above embodiments, and various changes can be made within the knowledge scope of ordinary technicians in the relevant technical field without departing from the purpose of the present invention.

Claims

1. A method for reducing noise of diaphragm electromyographic signals, characterized in that: The following steps are involved: Collect surface diaphragm electromyographic signals; Performing multi-scale wavelet decomposition on the surface diaphragm electromyographic signal, and determining wavelet coefficients of corresponding scales based on the frequency range of the electrocardiogram signal; Squaring the wavelet coefficients to obtain wavelet energy coefficients; A double threshold algorithm is used to detect the peak value of the wavelet energy coefficient to determine the ECG position; Determine the interference interval according to the ECG position; Determining an average energy threshold according to wavelet energy coefficients in adjacent intervals of the interference interval; Performing noise reduction processing on the interference interval according to the average energy threshold to obtain a wavelet energy coefficient after noise reduction; The denoised wavelet energy coefficients are restored and reconstructed to obtain the diaphragm electromyographic signal; The method of using a dual threshold algorithm to detect the peak value of the wavelet energy coefficient and determine the ECG position includes the following steps: According to the positive and negative change points of the slopes of every two points on the wavelet energy coefficient, several continuous local peaks are determined; Initialize the high threshold and the low threshold according to the average value of several consecutive local peaks; Determine the wavelet energy coefficient currently located between the low threshold and the high threshold as the current peak value; The high threshold and the low threshold are updated according to the current peak value, and the peak value detection at the next moment is performed according to the updated high threshold and the low threshold.

2. The method for reducing noise of diaphragm electromyographic signals according to claim 1, characterized in that: Initializing the high threshold and the low threshold according to the average value of several consecutive local peak values ​​comprises the following steps: Multiplying the average of several consecutive local peaks by the first multiple to obtain an initial high threshold; An initial low threshold is obtained by multiplying an average value of a number of consecutive local peak values ​​by a second multiple, wherein the first multiple is greater than 0.5 and less than 1, and the second multiple is greater than 0 and less than 0.

5.

3. The method for reducing noise of diaphragm electromyographic signals according to claim 1, characterized in that: The update formula of the high threshold is as follows: The updating formula of the low threshold is as follows: Where P represents the current peak value, Represents the average value of several consecutive local peaks before the current peak, P m Represents the sum of several consecutive local peaks before the current peak, th h Indicates the high threshold before update, th h ' represents the updated high threshold, th l Indicates the low threshold before update, th l ' represents the updated low threshold, th0 and th1 are preset empirical parameters, k1, k2, k3, k4 are all threshold coefficients less than 1, among which k1 <k2,k3<k4。 4. The method for reducing diaphragm electromyographic signal noise according to claim 1, characterized in that: Determining the average energy threshold according to the wavelet energy coefficients in the adjacent intervals of the interference interval comprises the following steps: Determine a left adjacent interval and a right adjacent interval of the interference interval according to a preset time length; Determine the wavelet energy mean value according to the sum of the wavelet energy coefficients of the left adjacent interval and the sum of the wavelet energy coefficients of the right adjacent interval; Compare the sum of the wavelet energy coefficients of the left adjacent interval and the sum of the wavelet energy coefficients of the right adjacent interval, and select the adjacent interval with the smaller sum of the wavelet energy coefficients; The average energy threshold is determined according to the wavelet energy mean and the wavelet energy coefficients in the selected adjacent intervals.

5. The method for reducing diaphragm electromyographic signal noise according to claim 4, characterized in that: Determining the average energy threshold according to the wavelet energy mean and the wavelet energy coefficients in the selected adjacent intervals comprises the following steps: Determining the standard deviation of the wavelet energy coefficients in the selected adjacent intervals and the wavelet energy mean; The standard deviation is added to the wavelet energy mean to obtain an average energy threshold.

6. The method for reducing diaphragm electromyographic signal noise according to claim 1, characterized in that: The performing noise reduction processing on the interference interval according to the average energy threshold comprises the following steps: The wavelet energy coefficients of the interference intervals that are greater than the average energy threshold are replaced by the average energy threshold.

7. A diaphragm electromyography signal noise reduction system, characterized in that: include: The first module is used to collect surface diaphragm electromyographic signals; The second module is used to perform multi-scale wavelet decomposition on the surface diaphragm electromyographic signal and determine the wavelet coefficients of the corresponding scale based on the frequency range of the electrocardiogram signal; The third module is used to square the wavelet coefficients to obtain wavelet energy coefficients; The fourth module is used to detect the peak value of the wavelet energy coefficient by using a double threshold algorithm to determine the ECG position; A fifth module is used to determine the interference interval according to the ECG position; A sixth module is used to determine an average energy threshold according to wavelet energy coefficients in adjacent intervals of the interference interval; The seventh module performs noise reduction processing on the interference interval according to the average energy threshold to obtain a wavelet energy coefficient after noise reduction; The eighth module is used to restore and reconstruct the wavelet energy coefficient after noise reduction to obtain the diaphragm electromyographic signal; Wherein, the fourth module is specifically used for: According to the positive and negative change points of the slopes of every two points on the wavelet energy coefficient, several continuous local peaks are determined; Initialize the high threshold and the low threshold according to the average value of several consecutive local peaks; Determine the wavelet energy coefficient currently located between the low threshold and the high threshold as the current peak value; The high threshold and the low threshold are updated according to the current peak value, and the peak value detection at the next moment is performed according to the updated high threshold and the low threshold.

8. A diaphragm electromyographic signal noise reduction device, characterized in that: include: at least one processor; at least one memory for storing at least one program; When the at least one program is executed by the at least one processor, the at least one processor implements the diaphragm electromyography signal denoising method as described in any one of claims 1 to 6.

9. A computer-readable storage medium storing a program executable by a processor, characterized in that: The program executable by the processor is used to implement the diaphragm electromyography signal noise reduction method as described in any one of claims 1 to 6 when executed by the processor.

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