Diaphragm Electromyogram Noise Reduction Method, System, Device and Storage Medium

Abstract

The present invention discloses a diaphragm electromyography noise reduction method, system, device and storage medium, which relate to the field of computer technology. The diaphragm electromyography signal is filtered to obtain a first signal including an electrocardiogram signal, the electrocardiogram position detection is performed on the electrocardiogram signal to determine the first electrocardiogram interval in the electrocardiogram signal, and then only the first electrocardiogram interval is subjected to electrocardiogram feature extraction processing to obtain an electrocardiogram morphology signal, and then the first signal is subtracted from the electrocardiogram morphology signal to obtain a second signal. By fitting the electrocardiogram morphology signal, the obtained second signal can remove the electrocardiogram part of the signal, and only the part of the signal in the first electrocardiogram interval is processed, and the other part of the first diaphragm signal can be retained. The second electrocardiogram interval in the second signal is determined according to the electrocardiogram position detection, and the second signal is subjected to wavelet dynamic threshold filtering processing to obtain a second diaphragm signal. The processing of the second signal is also performed only on the second electrocardiogram interval, thereby improving the signal-to-noise ratio of the processed diaphragm signal.

Description

Technical Field

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

[0002] Measurement of diaphragmatic electromyography can be used to indirectly assess neural respiratory drive, allowing evaluation of the level and pattern of muscle activation, which is crucial for the treatment of respiratory diseases.

[0003] There are two main methods for collecting diaphragm EMG: esophageal diaphragm EMG and surface diaphragm EMG. Esophageal diaphragm EMG is an invasive method, acquiring signals by inserting a collection tube into the esophagus. While esophageal diaphragm EMG can produce relatively pure diaphragm signals, it is inherently invasive and therefore has certain limitations. Surface diaphragm EMG performs the same function as esophageal diaphragm EMG but offers the advantage of being non-invasive. Surface diaphragm EMG uses electrodes to measure locations of interest on the body surface. While surface acquisition reduces invasiveness and harm, it also increases interference from other muscle signals. In particular, diaphragm signals, attenuated by tissue and skin, become significantly weaker than electrocardiogram signals, making them difficult to detect.

[0004] In order to remove ECG interference from diaphragm signals, many methods have been proposed, including gating, template subtraction, mathematical morphology, wavelet and independent variable analysis. However, these algorithms are either unable to remove ECG interference while retaining most of the diaphragm information, or their clinical applications are limited due to factors such as excessive calculation complexity and the need to add redundant channels.

[0005] For example, Annemijn H. Jonkma, José Dilermando Costa Junior, and others proposed a template subtractor that does not require separate ECG signal recording. This solves the problem of requiring redundant channels and is effective in removing ECG signal interference from myoelectric and diaphragmatic signals. However, a single ECG signal template cannot take into account sudden changes in ECG signal information, which limits its effectiveness.

[0006] Fei-Yun Wu et al. proposed combining ICA and adaptive wavelet transform to remove ECG interference from the diaphragm. Using ICA, they divided the diaphragm signals from multiple channels into different subcarriers based on the statistical characteristics of the diaphragm and ECG signals. Wavelet adaptive filtering was then applied to each subcarrier to remove ECG noise. Finally, all signals were inversely transformed into clean diaphragm EMG signals. While this method can remove some ECG interference, it requires inputting multiple different signals from the same patient, complicating surface diaphragm EMG signal denoising. Overall, wavelet filtering offers superior denoising performance. However, the principle behind this denoising is often to calculate the energy of the ECG neighborhood as a threshold and then perform dynamic threshold filtering on the ECG interval. This makes it impossible, in principle, to obtain true diaphragm EMG information within the ECG interval.

[0007] In the above-mentioned algorithm process of removing the diaphragm electrical signal, the overall diaphragm electrical signal is attenuated, resulting in that the signal-to-noise ratio of the final diaphragm signal cannot be effectively improved. Summary of the Invention

[0008] The present invention aims to solve at least one of the technical problems existing in the prior art. To this end, the present invention provides a diaphragm electromyography noise reduction method, system, device and storage medium, which can improve the signal-to-noise ratio of the processed diaphragm signal.

[0009] In one aspect, an embodiment of the present invention provides a method for diaphragm myoelectric noise reduction, comprising the following steps:

[0010] Collect diaphragm electromyographic signals;

[0011] Filtering the diaphragm electromyographic signal to obtain a first signal, wherein the first signal includes an electrocardiogram signal and a first diaphragm signal;

[0012] Performing electrocardiographic position detection on the electrocardiographic signal to determine an electrocardiographic position representation value;

[0013] determining a first ECG interval in the ECG signal according to the ECG position representation value;

[0014] performing electrocardiographic feature extraction processing on the first electrocardiographic interval to obtain an electrocardiographic morphology signal;

[0015] Subtracting the electrocardiogram signal from the first signal to obtain a second signal;

[0016] determining a second ECG interval in the second signal according to the ECG position representation value;

[0017] Based on the second ECG interval, wavelet dynamic threshold filtering is performed on the second signal to obtain a second diaphragm signal.

[0018] According to some embodiments of the present invention, filtering the diaphragm electromyographic signal to obtain the first signal includes the following steps:

[0019] Inputting the diaphragm electromyographic signal into an 8th-order Butterworth filter with a bandwidth of 20 to 400 Hz and a notch filter with a center frequency of 50 Hz in sequence to obtain a first diaphragm signal;

[0020] Inputting the diaphragm electromyographic signal into an 8th-order Butterworth bandpass filter with a bandwidth of 10 to 50 Hz to obtain an electrocardiogram signal;

[0021] The first diaphragm signal and the electrocardiogram signal are integrated to obtain a first signal.

[0022] According to some embodiments of the present invention, performing ECG position detection on the ECG signal to determine an ECG position representation value includes the following steps:

[0023] Performing a square operation on the electrocardiogram signal to obtain an amplified electrocardiogram signal;

[0024] Dynamically update the first threshold of each ECG segment in the amplified ECG signal, wherein the first threshold is determined by the following formula:

[0025]

[0026] Where f is the input signal, Td is the threshold, N is the length of the ECG segment, n is the number of ECGs located, Rval(n) is the R value of the current ECG segment, and k1 and k2 are proportional coefficients;

[0027] According to the intersection of the first threshold and the amplified ECG signal within the length of the ECG segment, the real ECG segment R value is determined to obtain the corresponding ECG position representation value.

[0028] According to some embodiments of the present invention, the first ECG interval is expressed as:

[0029] [Rval'(n)-a*T,Rval'(n)+b*T];

[0030] Where Rval'(n) represents the nth ECG position representation value, T is the distance between two adjacent ECG positions, and a and b are distance coefficients.

[0031] According to some embodiments of the present invention, performing ECG feature extraction processing on the first ECG interval to obtain an ECG morphology signal includes the following steps:

[0032] The peak information and trough information of the first ECG interval are obtained through morphological dilation and erosion operations;

[0033] integrating the peak information and the trough information of the first ECG interval by an averaging operation to obtain an ECG morphology signal;

[0034] The ECG morphology signal is scaled according to the power represented by the first signal at the ECG position.

[0035] According to some embodiments of the present invention, the second ECG interval is expressed as:

[0036] [Rval'(n)-c*T,Rval'(n)+d*T];

[0037] Wherein, Rval'(n) represents the nth ECG position representation value, T is the distance between two adjacent ECG positions, c and d are both distance coefficients, c<a, d<b.

[0038] According to some embodiments of the present invention, performing wavelet dynamic threshold filtering on the second signal based on the second ECG interval to obtain the second diaphragm signal includes the following steps:

[0039] After performing wavelet decomposition of different scales on the second signal, calculating the standard deviation of each ECG interval neighborhood in the second ECG interval of each layer of the second signal, and using the standard deviation as a second threshold of the second ECG interval of each layer, wherein the second threshold is dynamically updated at different ECG locations in the second ECG interval of each layer;

[0040] processing the second ECG interval in the second signal according to the second threshold of each layer to obtain a multi-layer decomposition signal, wherein when the absolute value of the acquired signal of the second ECG interval in each layer is greater than the second threshold of the corresponding layer, replacing the acquired signal with the second threshold;

[0041] The multiple layers of decomposed information are reconstructed to obtain a second diaphragm signal.

[0042] On the other hand, an embodiment of the present invention further provides a diaphragm myoelectric noise reduction system, comprising:

[0043] The first module is used to collect diaphragm electromyographic signals;

[0044] A second module is configured to filter the diaphragm electromyographic signal to obtain a first signal, wherein the first signal includes an electrocardiogram signal and a first diaphragm signal;

[0045] The third module is configured to perform ECG position detection on the ECG signal to determine an ECG position representation value;

[0046] A fourth module is configured to determine a first ECG interval in the ECG signal according to the ECG position representation value;

[0047] A fifth module is configured to perform electrocardiographic feature extraction processing on the first electrocardiographic interval to obtain an electrocardiographic morphology signal;

[0048] a sixth module, configured to subtract the electrocardiogram signal from the first signal to obtain a second signal;

[0049] A seventh module is configured to determine a second ECG interval in the second signal according to the ECG position representation value;

[0050] An eighth module is configured to perform wavelet dynamic threshold filtering on the second signal based on the second ECG interval to obtain a second diaphragm signal.

[0051] On the other hand, an embodiment of the present invention further provides a diaphragm myoelectric noise reduction device, comprising:

[0052] at least one processor;

[0053] at least one memory for storing at least one program;

[0054] When the at least one program is executed by the at least one processor, the at least one processor implements the diaphragm electromyography noise reduction method as described above.

[0055] 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 noise reduction method as described above.

[0056] The above-mentioned technical solution of the present invention has at least one of the following advantages or beneficial effects: first, the diaphragm electromyographic signal is filtered to obtain a first signal, the electrocardiographic position detection is performed on the electrocardiographic signal to determine the electrocardiographic position characterization value, and the first electrocardiographic interval in the electrocardiographic signal is determined based on the electrocardiographic position characterization value. Then, only the first electrocardiographic interval is subjected to electrocardiographic feature extraction processing to obtain an electrocardiographic morphology signal. The first signal is then subtracted from the electrocardiographic morphology signal to obtain a second signal. By fitting the electrocardiographic morphology signal, the second signal obtained by subtracting the electrocardiographic morphology signal from the first signal is able to remove the electrocardiographic signal as much as possible, and only the signal of the first electrocardiographic interval is processed, so that the diaphragm signal of the non-first electrocardiographic interval portion can be retained. After obtaining the second signal, the second electrocardiographic interval in the second signal is further determined based on the electrocardiographic position characterization value. Based on the second electrocardiographic interval, the second signal is subjected to wavelet dynamic threshold filtering processing to obtain a clean second diaphragm signal. The processing of the second signal is also performed only on the second electrocardiographic interval, so that the diaphragm signal portion of the original signal is retained as much as possible, thereby improving the signal-to-noise ratio of the processed diaphragm signal. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1This is a flow chart of the diaphragm electromyography noise reduction method provided by an embodiment of the present invention;

[0058] Figure 2 is a flow chart of a diaphragm electromyography noise reduction method provided by another embodiment of the present invention;

[0059] Figure 3 Schematic diagram of diaphragm myoelectric noise reduction effect provided by an embodiment of the present invention;

[0060] Figure 4 Schematic diagram of the diaphragm electromyography noise reduction device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0061] The following describes embodiments of the present invention in detail. Examples of the embodiments 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 intended only to explain the present invention and are not to be construed as limiting the present invention.

[0062] In the description of the present invention, it should be understood that descriptions involving orientations, such as up, down, left, right, etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings. They are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation. Therefore, they cannot be understood as limitations on the present invention.

[0063] 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 suggesting relative importance or implicitly indicating the number of the indicated technical features or implicitly indicating the order of the indicated technical features.

[0064] The embodiment of the present invention provides a diaphragm myoelectric noise reduction method, combining Figure 1 and Figure 2 The embodiment of the present invention is described. The diaphragm electromyography 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.

[0065] Step S110, collecting diaphragm electromyographic signals;

[0066] Step S120, filtering the diaphragm electromyographic signal to obtain a first signal, wherein the first signal includes the electrocardiogram signal and a first diaphragm signal;

[0067] Step S130, performing ECG position detection on the ECG signal to determine an ECG position representation value;

[0068] Step S140, determining a first ECG interval in the ECG signal according to the ECG position representation value;

[0069] Step S150, performing ECG feature extraction processing on the first ECG interval to obtain an ECG morphology signal;

[0070] Step S160, subtracting the electrocardiogram signal from the first signal to obtain a second signal;

[0071] Step S170, determining a second ECG interval in the second signal according to the ECG position representation value;

[0072] Step S180 : Based on the second ECG interval, the second signal is subjected to wavelet dynamic threshold filtering to obtain a second diaphragm signal.

[0073] In this embodiment, the diaphragm electromyographic signal is first filtered to obtain an electrocardiogram (ECG) signal, a preliminary first diaphragm signal, and a first signal synthesized from the two. ECG position detection is performed on the ECG signal to determine an ECG position representation value, and a first ECG interval in the ECG signal is determined based on the ECG position representation value. ECG feature extraction is then performed only on the first ECG interval to obtain an ECG morphology signal. The ECG morphology signal is then subtracted from the first signal to obtain a second signal. By fitting the ECG morphology signal, the second signal obtained by subtracting the ECG morphology signal from the first signal is minimized from the ECG portion of the signal. Only the portion of the signal in the first ECG interval is processed, thereby retaining the diaphragm signal that is not in the first ECG interval. After obtaining the second signal, a second ECG interval in the second signal is further determined based on the ECG position representation value. Based on the second ECG interval, the second signal is subjected to wavelet dynamic threshold filtering to obtain a clean second diaphragm signal. The second signal is also processed only on the second ECG interval, preserving the diaphragm signal portion of the original signal as much as possible, thereby improving the signal-to-noise ratio of the extracted diaphragm signal.

[0074] According to some specific embodiments of the present invention, step S120 includes but is not limited to step S210, step S220 and step S230.

[0075] Step S210, inputting the diaphragm electromyographic signal into an 8th-order Butterworth filter with a bandwidth of 20 to 400 Hz and a notch filter with a center frequency of 50 Hz in sequence to obtain a first diaphragm signal;

[0076] Step S220 , inputting the diaphragm electromyographic signal into an 8th-order Butterworth bandpass filter with a bandwidth of 10 to 50 Hz to obtain an electrocardiographic signal;

[0077] Step S230 : integrating the first diaphragm signal and the electrocardiogram signal to obtain a first signal.

[0078] In this embodiment, the diaphragm electromyographic signal is passed through an 8th-order Butterworth filter with a bandwidth of 20-400 Hz and then through a notch filter with a center frequency of 50 Hz to obtain a preliminarily processed first diaphragm signal from the diaphragm electromyographic signal.

[0079] The diaphragm EMG signal is filtered through an 8th-order Butterworth bandpass filter with a bandwidth of 10-50 to obtain the ECG signal from the diaphragm EMG signal, preparing for subsequent ECG detection. It should be noted that the bandpass filter can remove the lower-frequency P and T waves in the ECG signal. After the above processing, only the QRS wave of the ECG signal and the first diaphragm signal remain.

[0080] The filtered electrocardiogram signal and the first diaphragm signal are integrated to obtain a first signal.

[0081] According to some specific embodiments of the present invention, step S130 includes but is not limited to step S310, step S320 and step S330.

[0082] Step S310, performing a square operation on the ECG signal to obtain an amplified ECG signal;

[0083] Step S320, dynamically updating the first threshold of each ECG segment in the amplified ECG signal;

[0084] Step S330 , determining the true R value of each ECG segment according to the intersection of the first threshold value of each ECG segment and the ECG amplified signal within the ECG segment length, and obtaining the corresponding ECG position representation value.

[0085] In this embodiment, for ECG position detection, since the QRS wave is the most obvious waveform in the ECG signal after filtering the diaphragm electromyography signal, and point R is the midpoint of the fluctuation in QRS, ECG position detection can be detection of point R.

[0086] First, the ECG signal is squared to better distinguish it from the diaphragm signal. The maximum value of the squared ECG signal within a certain interval is determined, and the position corresponding to this maximum value is used as the first ECG starting point. Half of this maximum value is then used as the current threshold. In subsequent detection, each time a new ECG signal is detected, the first threshold is updated. Specifically, the first threshold update rule is as follows:

[0087]

[0088] Where f is the input signal, Td is the threshold, N is the length of the first ECG segment detected, n is the number of ECGs located, Rval(n) is the R value of the current ECG segment, and k1 and k2 are proportional coefficients that control the contribution of Td(n-1) and Rval(n) to the current first threshold. The length of N is related to the sampling frequency of the diaphragm EMG signal. For example, if the sampling frequency is 2000 Hz, then N = 500.

[0089] After determining the real-time first threshold, ECG position detection is performed based on the intersection of the first threshold and the squared ECG signal. Specifically, the input squared ECG is compared with the current first threshold. When the current input signal is greater than the current first threshold, timing begins. Before the time length N is reached, if a signal less than the first threshold appears, the maximum value within the time length is determined to be Rval(n). The position corresponding to the maximum value Rval(n) is the ECG position representation value Rval'(n). The threshold is then updated based on Rval(n). Otherwise, it indicates that the current first threshold is too small, resulting in the intersection being located in the diaphragm signal rather than the ECG signal. In this case, k2 needs to be increased and the detection of input signals greater than the first threshold needs to be restarted. The pseudo-periodicity of the ECG signal is used to avoid misjudgment of the ECG signal.

[0090] According to some specific embodiments of the present invention, after determining the ECG position in the input signal, a first ECG interval is determined based on multiple ECG position representation values. The first ECG interval is expressed as:

[0091] [Rval'(n)-a*T,Rval'(n)+b*T];

[0092] Among them, Rval'(n) represents the nth ECG position representation value, T is the distance between two adjacent ECG positions, a and b are both distance coefficients. In this implementation, a=0.5, b=0.4. It should be noted that a and b can also be other values, and the embodiment of the present invention does not impose specific restrictions.

[0093] According to some specific embodiments of the present invention, step S150 includes but is not limited to step S410, step S420 and step S430.

[0094] Step S410, obtaining peak information and trough information of the first ECG interval through morphological dilation and erosion operations;

[0095] Step S420, integrating the peak information and the trough information of the first ECG interval by an averaging operation to obtain an ECG morphology signal;

[0096] Step S430: scaling the ECG morphology signal according to the power represented by the first signal at the ECG position.

[0097] In this embodiment, morphological ECG tracking is developed based on rigorous mathematical topology. After delineating the first ECG interval, morphological dilation and erosion operations are performed within the first ECG interval to obtain peak and trough information for the ECG segment. The peak and trough information of the ECG segment is then integrated using an averaging operation to obtain a segment with ECG morphology, i.e., the ECG morphology signal. Non-ECG intervals are not processed to maximize the preservation of the diaphragm signal.

[0098] Furthermore, after the ECG morphology signal is extracted, an average filter can be used to remove noise therein to obtain a cleaner ECG morphology signal.

[0099] To compensate for the peak reduction caused by the averaging filter, the obtained ECG morphology is denormalized. Specifically, a first average power of the first signal integrating the first diaphragm signal and the ECG signal is determined near point R, a second average power of the ECG morphology signal near point R is determined, the first power and the second power are compared to determine a scaling ratio, and the ECG morphology signal is scaled according to the scaling ratio so that the second average power of the ECG morphology signal near point R is equal to the first average power of the first signal near point R.

[0100] According to some specific embodiments of the present invention, a subtractor is used to subtract the first signal from the normalized ECG morphology signal to obtain a second signal, ie, a diaphragm signal without ECG.

[0101] According to some specific embodiments of the present invention, step S180 includes but is not limited to step S510, step S520 and step S530.

[0102] Step S510, after performing wavelet decomposition of different scales on the second signal, calculate the standard deviation of each ECG interval neighborhood in the second ECG interval of each layer of the second signal, and use the standard deviation as a second threshold of the second ECG interval of each layer, wherein the second threshold is dynamically updated at different ECG locations in the second ECG interval of each layer;

[0103] Step S520: Processing the second ECG interval in the second signal according to the second threshold of each layer to obtain a multi-layer decomposition signal, wherein when the absolute value of the collected signal of the second ECG interval in each layer is greater than the second threshold of the corresponding layer, the collected signal is replaced by the second threshold;

[0104] Step S530: reconstruct the multi-layer decomposition information to obtain a second diaphragm signal.

[0105] In this embodiment, a subtractor is used to obtain a diaphragm signal without an ECG. However, the morphological ECG fitting of the first ECG interval may leave ECG residuals in the case of abnormal ECG mutations in the patient. Therefore, wavelet dynamic threshold filtering is required to remove the remaining ECG residuals.

[0106] First, the second signal is subjected to a five-scale wavelet decomposition to highlight the ECG segments. Wavelet decomposition is similar to high-pass and low-pass filtering of the signal at each scale. After the five-scale wavelet decomposition, the low-frequency coefficients correspond to 0-62.5Hz, which corresponds to the main frequency band of the ECG signal (0-100Hz).

[0107] After the second signal is decomposed by wavelet, the second ECG interval where the ECG signal may exist is determined. The second ECG interval is expressed as:

[0108] [Rval'(n)-c*T,Rval'(n)+d*T];

[0109] Where Rval'(n) is the nth ECG position representation value located above, T is the distance between two adjacent ECG positions, and c and d are distance coefficients, where c < a and d < b. In this embodiment, c = 0.03 and d = 0.02. The ECG interval reduces the degree of loss of diaphragm EMG information after wavelet threshold filtering.

[0110] Then, the standard deviation of the area of each second ECG interval is calculated and used as the second threshold corresponding to the second ECG interval. The rule for processing the second ECG interval of the second signal according to the second threshold is as follows:

[0111]

[0112] Among them, θ(x) is the output signal, x is the input signal of the second signal, j is the number of layers processed, T j (x) is the second threshold of the jth layer. For each layer of the decomposed signal, in each second ECG interval, if the absolute value of the input signal is greater than the second threshold of the corresponding interval, it indicates that the current input signal is an ECG signal, and the output signal is the corresponding second threshold; otherwise, the output signal is equal to the input signal. After processing each layer of the second signal according to the above rules, the decomposed signals of each layer are reconstructed to obtain a clean second diaphragm signal.

[0113] The wavelet dynamic filtering process of this embodiment has the following advantages compared to the current wavelet filtering solution:

[0114] 1. The determined ECG interval is smaller. The second signal of the embodiment of the present invention has been processed by morphological filtering and a subtractor, and the residual ECG is only in a very small interval.

[0115] 2. The threshold processing method is different. For signals that are judged to be in the ECG interval, they are no longer simply set to zero, but are replaced by the standard deviation of the domain, which is more consistent with the fluctuation of the diaphragm electrical signal in the ECG neighborhood. Because in the part of the ECG interval that is greater than the threshold, there are not only ECG signals, but also diaphragms, and the zeroing operation removes the diaphragm components. Similarly, in the ECG interval, when the integrated signal is greater than the threshold, there is a certain degree of error in the method of using the domain standard deviation equal to the integrated signal, because the standard deviation of the ECG neighborhood cannot obtain the actual situation in the ECG. The morphology of the previous step of the embodiment of the present invention plus the subtractor can well obtain the diaphragm signal in the ECG. Therefore, by reducing the ECG interval determined in the wavelet threshold part, more diaphragm signals can be retained.

[0116] In some embodiments, reference Figure 3 Schematic diagram of the denoising effect of each process, Figure 3 It is a signal collected through the esophageal tube from patients with respiratory and heart diseases.

[0117] exist Figure 3 In the box, it can be clearly seen that after the morphological filtering and subtractor operation, the ECG interference interval is significantly reduced, and the diaphragm information in the original ECG interference interval can be well preserved. After wavelet threshold denoising, the ECG signal is accurately removed.

[0118] The median frequency and high-to-low power ratio of the diaphragm signal processed by the method of the embodiment of the present invention and the diaphragm signal processed by the traditional wavelet threshold denoising method are calculated respectively. It can be found that the median frequency and high-to-low power ratio of the diaphragm signal processed by the method proposed in this article are higher, which can intuitively illustrate that the signal has more high-frequency components, indicating that the diaphragm signal obtained using the example of the present invention is more accurate.

[0119] By processing the esophageal diaphragm electromyographic signals, the actually collected surface diaphragm electromyographic signals and the simulated diaphragm electromyographic data with different signal-to-noise ratios, the results show that the embodiment of the present invention performs better than the traditional algorithm in interference removal performance. Both the esophageal diaphragm electromyographic signals and the surface diaphragm electromyographic signals retain more diaphragm signals, and the universality advantage is more obvious.

[0120] On the other hand, an embodiment of the present invention further provides a diaphragm myoelectric noise reduction system, comprising:

[0121] The first module is used to collect diaphragm electromyographic signals;

[0122] A second module is configured to filter the diaphragm electromyographic signal to obtain a first signal, wherein the first signal includes an electrocardiogram signal and a first diaphragm signal;

[0123] The third module is used to perform ECG position detection on the ECG signal to determine the ECG position representation value;

[0124] A fourth module is configured to determine a first ECG interval in the ECG signal according to the ECG position representation value;

[0125] A fifth module is configured to perform electrocardiographic feature extraction processing on the first electrocardiographic interval to obtain an electrocardiographic morphology signal;

[0126] A sixth module, configured to subtract the electrocardiogram signal from the first signal to obtain a second signal;

[0127] A seventh module is configured to determine a second ECG interval in the second signal according to the ECG position representation value;

[0128] The eighth module is used to perform wavelet dynamic threshold filtering on the second signal based on the second ECG interval to obtain a second diaphragm signal.

[0129] It can be understood that the contents of the above-mentioned diaphragm electromyography 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 electromyography noise reduction method embodiment, and the beneficial effects achieved are also the same as those achieved by the above-mentioned diaphragm electromyography noise reduction method embodiment.

[0130] Reference Figure 4 , Figure 4 Schematic diagram of a diaphragm myoelectric noise reduction device provided by an embodiment of the present invention. The diaphragm myoelectric noise reduction device of the embodiment of the present invention includes one or more control processors and a memory. Figure 4 A control processor and a memory are taken as an example.

[0131] The control processor and the memory can be connected via a bus or other means. Figure 4 The bus connection is taken as an example.

[0132] 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 located relative to the control processor, and these remote memories may be connected to the diaphragm electromyography 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 a combination thereof.

[0133] Those skilled in the art will understand that Figure 4The device structure shown in does not constitute a limitation on the diaphragm myoelectric noise reduction device, and may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0134] The non-transient software program and instructions required to implement the diaphragm electromyography noise reduction method applied to the diaphragm electromyography noise reduction device in the above embodiment are stored in the memory. When executed by the control processor, the diaphragm electromyography noise reduction method applied to the diaphragm electromyography noise reduction device in the above embodiment is executed.

[0135] 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, enabling the one or more control processors to execute the diaphragm electromyography noise reduction method in the above method embodiment.

[0136] Those skilled in the art will appreciate that all or some of the steps and systems in the method disclosed above can be implemented as software, firmware, hardware, and appropriate combinations thereof. Some physical components or all physical components can be implemented as software executed by a processor, such as a central processing unit, a digital signal processor, or a microprocessor, or implemented as hardware, or implemented as an integrated circuit, such as an application-specific integrated circuit. Such software can be distributed on a computer-readable medium, and the computer-readable medium can include computer storage media (or non-transitory media) and communication media (or temporary media). As known to those skilled in the art, the term computer storage media is included in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data) and is volatile and non-volatile, removable, and non-removable. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory, or other memory technology, 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 can be used to store desired information and can be accessed by a computer. Furthermore, as is well known to those skilled in the art, 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.

[0137] The embodiments of the present invention are described in detail above with reference to the accompanying drawings. However, the present invention is not limited to the above embodiments. Various changes can be made within the scope of knowledge possessed by ordinary technicians in the relevant technical field without departing from the scope of the present invention.

Claims

1. A diaphragm electromyography noise reduction method, characterized in that: The following steps are involved: Collect diaphragm electromyographic signals; Filtering the diaphragm electromyographic signal to obtain a first signal, wherein the first signal includes an electrocardiogram signal and a first diaphragm signal; Performing electrocardiographic position detection on the electrocardiographic signal to determine an electrocardiographic position representation value; determining a first ECG interval in the ECG signal according to the ECG position representation value; performing electrocardiographic feature extraction processing on the first electrocardiographic interval to obtain an electrocardiographic morphology signal; Subtracting the electrocardiogram signal from the first signal to obtain a second signal; determining a second ECG interval in the second signal according to the ECG position representation value; Based on the second ECG interval, performing wavelet dynamic threshold filtering on the second signal to obtain a second diaphragm signal; The performing electrocardiographic position detection on the electrocardiographic signal to determine the electrocardiographic position representation value comprises the following steps: Performing a square operation on the electrocardiogram signal to obtain an amplified electrocardiogram signal; Dynamically update the first threshold of each ECG segment in the amplified ECG signal, wherein the first threshold is determined by the following formula: Where f is the input signal, Td is the threshold, N is the length of the first ECG segment, n is the number of ECGs located, Rval(n) is the R value of the current ECG segment, and k1 and k2 are proportional coefficients; According to the intersection of the first threshold and the amplified ECG signal within the length of the ECG segment, the real ECG segment R value is determined to obtain the corresponding ECG position representation value.

2. The diaphragm electromyography noise reduction method according to claim 1, characterized in that: The filtering process of the diaphragm electromyographic signal to obtain the first signal comprises the following steps: Inputting the diaphragm electromyographic signal into an 8th-order Butterworth filter with a bandwidth of 20 to 400 Hz and a notch filter with a center frequency of 50 Hz in sequence to obtain a first diaphragm signal; Inputting the diaphragm electromyographic signal into an 8th-order Butterworth bandpass filter with a bandwidth of 10 to 50 Hz to obtain an electrocardiogram signal; The first diaphragm signal and the electrocardiogram signal are integrated to obtain a first signal.

3. The diaphragm electromyography noise reduction method according to claim 2, characterized in that: The first ECG interval is expressed as: [Rval'(n)-a*T,Rval'(n)+b*T]; Where Rval'(n) represents the nth ECG position representation value, T is the distance between two adjacent ECG positions, and a and b are distance coefficients.

4. The diaphragm electromyography noise reduction method according to claim 3, characterized in that: Performing ECG feature extraction processing on the first ECG interval to obtain an ECG morphology signal includes the following steps: The peak information and trough information of the first ECG interval are obtained through morphological dilation and erosion operations; integrating the peak information and the trough information of the first ECG interval by an averaging operation to obtain an ECG morphology signal; The ECG morphology signal is scaled according to the power represented by the first signal at the ECG position.

5. The diaphragm electromyography noise reduction method according to claim 4, characterized in that: The second ECG interval is expressed as: [Rval'(n)-c*T,Rval'(n)+d*T]; Wherein, Rval'(n) represents the nth ECG position representation value, T is the distance between two adjacent ECG positions, c and d are both distance coefficients, c<a, d<b.

6. The diaphragm electromyography noise reduction method according to claim 5, characterized in that: The step of performing wavelet dynamic threshold filtering on the second signal based on the second ECG interval to obtain a second diaphragm signal comprises the following steps: After performing wavelet decomposition of different scales on the second signal, calculating the standard deviation of each ECG interval neighborhood in the second ECG interval of each layer of the second signal, and using the standard deviation as a second threshold of the second ECG interval of each layer, wherein the second threshold is dynamically updated at different ECG locations in the second ECG interval of each layer; processing the second ECG interval in the second signal according to the second threshold of each layer to obtain a multi-layer decomposition signal, wherein when the absolute value of the acquired signal of the second ECG interval in each layer is greater than the second threshold of the corresponding layer, replacing the acquired signal with the second threshold; The multiple layers of the decomposed signals are reconstructed to obtain a second diaphragm signal.

7. A diaphragm electromyography noise reduction system, characterized in that: include: The first module is used to collect diaphragm electromyographic signals; A second module is configured to filter the diaphragm electromyographic signal to obtain a first signal, wherein the first signal includes an electrocardiogram signal and a first diaphragm signal; The third module is configured to perform ECG position detection on the ECG signal to determine an ECG position representation value; A fourth module is configured to determine a first ECG interval in the ECG signal according to the ECG position representation value; A fifth module is configured to perform electrocardiographic feature extraction processing on the first electrocardiographic interval to obtain an electrocardiographic morphology signal; a sixth module, configured to subtract the electrocardiogram signal from the first signal to obtain a second signal; A seventh module is configured to determine a second ECG interval in the second signal according to the ECG position representation value; An eighth module is configured to perform wavelet dynamic threshold filtering on the second signal based on the second ECG interval to obtain a second diaphragm signal; The third module is specifically configured to perform the following steps: Performing a square operation on the electrocardiogram signal to obtain an amplified electrocardiogram signal; Dynamically update the first threshold of each ECG segment in the amplified ECG signal, wherein the first threshold is determined by the following formula: Where f is the input signal, Td is the threshold, N is the length of the first ECG segment, n is the number of ECGs located, Rval(n) is the R value of the current ECG segment, and k1 and k2 are proportional coefficients; According to the intersection of the first threshold and the amplified ECG signal within the length of the ECG segment, the real ECG segment R value is determined to obtain the corresponding ECG position representation value.

8. A diaphragm myoelectric 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 noise reduction method according to 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 noise reduction method according to any one of claims 1 to 6 when executed by the processor.

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