CN115251947A - Method for removing myoelectric noise in electrocardiosignal based on singular value decomposition - Google Patents
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Abstract
The invention discloses a method for removing electromyographic noise in electrocardiosignals based on singular value decomposition, which specifically comprises the following steps: s1, synthesizing a noise electrocardiosignal: the synthesized noise signal is mainly used for verifying the effectiveness of the algorithm and evaluating the noise reduction effect, and the clean electrocardiosignal and the myoelectric noise are superposed on different amplification coefficients to synthesize analog noise signals with different pollution degrees; s2, searching for the QRS wave of the electrocardiosignal: the invention relates to the technical field of electrocardiosignal noise reduction, and discloses a method for extracting main characteristics of electrocardiosignals by searching for the QRS waves of the electrocardiosignals, wherein the electrocardiosignals comprise P waves, T waves, QRS waves and the like. Compared with the traditional denoising method, the method for removing the myoelectric noise in the electrocardiosignals based on the singular value decomposition has a strong denoising effect when the electrocardiosignals are seriously polluted by noise, the output signal-to-noise ratio and the noise lifting are higher than those of a wavelet transform threshold value method and a band-pass filter, the strong denoising capability is represented, and meanwhile, the root mean square error is smaller than that of the traditional method, so that the distortion is smaller.
Description
Technical Field
The invention relates to the technical field of electrocardiosignal noise reduction, in particular to a method for removing myoelectric noise in electrocardiosignals based on singular value decomposition.
Background
At present, cardiovascular diseases become one of the most serious diseases threatening residents in China, prevention and monitoring of cardiovascular diseases become a non-negligible problem, electrocardiosignals serve as the most direct physiological signals of human bodies and can reflect heart health conditions, but the electrocardiosignals are easily interfered by various noises, including baseline drift, mains supply interference and myoelectric noise, wherein the myoelectric noise is mainly generated by micro-motion of human muscles, the frequency domain range of the myoelectric noise is overlapped with the frequency domain of the electrocardiosignals, a filter based on frequency domain segmentation cannot be used for removing the myoelectric noise, and the removal of the myoelectric noise becomes a difficulty in filtering the electrocardiosignals.
The existing electromyographic noise removing method mainly adopts a band-pass filter, a wavelet transform threshold method and other methods, wherein the band-pass filter is mainly based on frequency domain filtering of Fourier transform and has the advantage of simple calculation but cannot remove the electromyographic noise and the frequency domain overlapping part of electrocardiosignals; the wavelet transform is a new transform analysis method, it inherits and develops the thought of short-time Fourier transform localization, overcome the window size and not along with the disadvantage of frequency transform at the same time, can analyze the localization of time (space), the wavelet threshold method is to utilize wavelet transform to decompose the electrocardio signal into detail coefficient and similar coefficient of different levels, then use soft or hard threshold method to remove the noise to the coefficient, use wavelet inverse transform to reconstruct the subband signal and get the clean electrocardio signal finally, the wavelet transform can highlight the characteristic of the signal, but the choice of the threshold is difficult, the threshold is too large, will cause the distortion and excessive noise reduction of the signal, the threshold is too small, the noise removal is incomplete.
The existing methods are only suitable for electrocardiosignals which do not contain or contain a small amount of myoelectric noise, only weak myoelectric noise can be removed by a band-pass filter and a wavelet transform threshold method, and for a motion scene, the electrocardiosignals contain a large amount of myoelectric noise, the noise reduction effect is weak, and the electrocardiosignals cannot be suitable for the condition of a larger signal-to-noise ratio; meanwhile, the existing method does not consider the quasi-periodic quality of the electrocardiosignal, namely the regularity of the heartbeat of a human body, a band-pass filter carries out filtering by using different noise and signal frequencies, but the frequency domains of the electromyographic noise and the electrocardiosignal have overlapping parts, wavelet transformation considers that the signal is decomposed into sub-band signals, and then the sub-band signals are removed by using a threshold method, but the electromyographic noise in the sub-band signals cannot be measured.
Disclosure of Invention
Technical problem to be solved
Aiming at the defects of the prior art, the invention provides a method for removing myoelectric noise in electrocardiosignals based on singular value decomposition, which can utilize the quasi-periodic nature of the electrocardiosignals to construct a track matrix, then use the singular value decomposition to extract the main waveform characteristics, improve the correlation and the output signal-to-noise ratio of the electrocardiosignals before and after filtering, reduce the root mean square error and realize the effective removal of the myoelectric noise at different noise levels.
(II) technical scheme
In order to achieve the purpose, the invention is realized by the following technical scheme: a method for removing electromyographic noise in electrocardiosignals based on singular value decomposition specifically comprises the following steps:
s1, synthesizing a noise electrocardiosignal: the synthesized noise signal is mainly used for verifying the effectiveness of the algorithm and evaluating the noise reduction effect, and the clean electrocardiosignal and the myoelectric noise are superposed on different amplification coefficients to synthesize analog noise signals with different pollution degrees;
s2, searching for QRS waves of the electrocardiosignals: searching for an electrocardiosignal QRS wave to extract the main characteristics of the electrocardiosignal, wherein the electrocardiosignal comprises a P wave, a T wave, a QRS wave and the like, and the QRS wave has the maximum amplitude and energy and is an essential step in electrocardiosignal analysis;
s3, calculating the interval of adjacent R waves: calculating adjacent R wave intervals, and calculating RR intervals (two adjacent R wave intervals) according to the existing R wave positions to obtain RR wave intervals with different lengths, wherein the RR wave numbers are as follows, RR = [ R ]1R2,R2R3,R3R4,…Rn-1Rn]；
S4, heart beat segmentation: the specific idea is that the electrocardiosignal is cut into different segments by a heart-splitting beat by taking an R wave as the center, and the R peak interval R between the electrocardiosignal and the R peak on the left side is obtained according to the RR interval array obtained in the last steplR, right R peak spacing RRrMaximum heart beat interval RRmax0.5R to the left of the selected R peaklR,To the right of the R peak, 0.5RR was takenrThe length is taken as a complete heart beat, the heart beats obtained by segmentation have unequal length, and the left and the right are simultaneously filled with 0 to 0.5RRmaxThe total length of the heart beat is unified as RRmax+1；
S5, constructing a track matrix: constructing a periodic track matrix, and superposing a series of previously processed heart beat sequences to form a two-dimensional track matrix, wherein the formula of the track matrix A is as follows:
s6, singular value decomposition: singular value decomposition decomposes a two-dimensional matrix into mutually intersecting eigenvalues and eigenvectors, singular value decomposition being a matrix decomposition method defined as: a = U Σ VTU and V are left and right singular matrixes divided by the matrix A, elements above a sigma division main diagonal are all 0, the main diagonal elements are called singular values, the singular values are arranged from large to small, and the largest singular value represents a main component of the signal;
s7, selecting singular values: when reconstructing an electrocardiosignal, selecting the maximum singular value as a clean signal, taking other singular values as noise and setting the noise to be 0, then reconstructing a two-dimensional signal matrix, and finally taking out each row in the matrix to restore a clean ECG signal;
s8, electrocardiosignal reduction: the two-dimensional matrix is restored to a filtered clean signal.
Preferably, R in the array in step S31R2Indicating the separation between the first R peak and the second R peak.
Preferably, S in the step S51The position of the first R peak, Z is half of the maximum RR interval as the reference length, each row in the matrix represents a heart beat after the division is finished, an electrocardiosignal consists of n heart beats, and the R position S of each heart beatnAre in strict alignment.
Preferably, the two-dimensional signal matrix formula reconstructed in step S7 is: a' = u1σ1v1Where A' is the reconstructed signal matrix, σ1Is the maximum singular value, u1，v1Respectively, the singular vectors corresponding to the singular values.
(III) advantageous effects
The invention provides a method for removing electromyographic noise in electrocardiosignals based on singular value decomposition. The method has the following beneficial effects:
(1) Compared with the traditional denoising method, the method for removing the myoelectric noise in the electrocardiosignals based on the singular value decomposition has a strong denoising effect when the electrocardiosignals are seriously polluted by noise, the output signal-to-noise ratio and the noise lifting are higher than those of a wavelet transform threshold value method and a band-pass filter, the strong denoising capability is represented, and meanwhile, the root mean square error is smaller than that of the traditional method, so that the distortion is smaller.
(2) Compared with the traditional noise removal scheme, the method for removing the electromyographic noise in the electrocardiosignals based on the singular value decomposition considers the inherent periodic property of the electrocardiosignals, namely the QRS waves in the electrocardiosignals are the main characteristic, have periodicity and correspond to the heartbeat of a human body, the singular value decomposition can extract the main components of the matrix, and meanwhile, the heart beat segmentation method fully considers the difference of the interval length of each heart beat and avoids the QRS wave dislocation caused by the fixed-length heart beat segmentation.
Drawings
FIG. 1 is a flow chart of a method for removing electromyographic noise according to the present invention;
FIG. 2 is a schematic diagram of the heartbeat segmentation method of the present invention;
FIG. 3 is a comparison of the myoelectric noise removal according to the present invention and the prior art;
FIG. 4 is a noise reduction evaluation chart for different input SNR according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1-4, an embodiment of the present invention provides a technical solution: a method for removing myoelectric noise in electrocardiosignals based on singular value decomposition is used for ECG noise reduction and specifically comprises the following steps:
s1, synthesizing an ECG signal containing electromyographic noise (EMG): (1) The method comprises the following steps that a python is used for reading an electromyographic noise signal MA from an MIT-BIH noise pressure database, the MIT-BIH noise pressure database collects electromyographic noise, mains supply interference and baseline drift, and is a gold standard for testing a noise reduction algorithm, the MIT-BIH arrhythmia database contains rhythms such as common arrhythmia beat atrial premature beat, ventricular premature beat and conduction block, the two databases are used for verifying the effect of the method, and No. 101 clean electrocardiosignals are read from the MIT-BIH arrhythmia database;
(2) According to the principle of noise superposition, ECGstimulate＝ECGclear+σEMG，ECGstimulatIn order to synthesize a mixed signal, sigma is a noise superposition proportion, EMG is electromyographic noise MA, the length of a clean signal is 2500 data points, corresponding electromyographic noise data is selected to be 2500 data points, 2dB,4dB,8dB and 169B noises are respectively input, and different signal-to-noise ratios are calculated according to different sigma values.
S2 and QRS wave searching, QRS waves are mainly searched from ECG signals, the QRS waves are the most obvious peak values in the electrocardiosignals, at present, general peak value searching algorithms such as pan-Tompks algorithm, wavlet algorithm and the like are adopted, the pan-Tompks algorithm is selected, R waves in the electrocardiosignals are extracted, the corresponding positions of the R waves are obtained, and the corresponding R wave position arrays are extracted to be [154,407,561,923,1195,1468,1736,1996,2246], and contain 9 heart beat R wave positions.
S3, calculating adjacent RR wave intervals, constructing an RR interval array, calculating RR wave intervals (adjacent R wave peak distance in ECG) according to the obtained R wave position data, wherein the RR interval array is [253,154,362,272,273,268,260 and 250], and the maximum RR interval can be found in the array to be 362.
And S4, heart beat segmentation, as shown in FIG. 2, a red heart beat is a heart beat to be segmented, a second heart beat is selected to be specifically described, wherein the left side distance L1w is 253, the right side distance L2 is 154, according to the segmentation method, the left side length is 0.5L 1 121, the right side length is 0.5L 2 77, the left side and the right side are simultaneously filled to the half of the maximum length and 181, the total length of the heart beat is 363, and all the heart beats are segmented and filled to obtain a series of heart beats with the same length.
S5, constructing a track matrix, and storing heartbeats by using a numpy high-performance library function matrix in python, wherein each row of the matrix is a single heartbeat, and a data diagram of the first three rows of the matrix is shown in FIG. 3.
S6, decomposing a track matrix by using singular values, extracting main waveform characteristics of the electrocardiosignals, wherein the singular value decomposition adopts np.linear.svd (traix) function in a numpy library of python, numpy () is a calculation library specially developed for scientific calculation by python, and comprises matrix decomposition, matrix singular value decomposition, matrix QR decomposition and the like, and the numpy library function is adopted to realize the singular value decomposition in the embodiment.
S7, selecting the maximum singular value as the electrocardiosignal characteristic, reconstructing a signal matrix, wherein singular values obtained by singular value decomposition are [14.95642755, 2.32091189, 1.63704141, 1.12070935, 0.83488297, 0.52250907, 0.40032248, 0.33921127 and 0.18226298], so that the first singular value occupies main components, selecting 14.95 as the clean signal characteristic in the electrocardiosignal, setting other singular values as 0, and reconstructing a two-dimensional track matrix.
S8, a two-dimensional signal matrix is restored into a filtered clean signal, electromyographic noise is removed from the restored electrocardiosignal, as shown in FIG. 3, the noise-containing signal comprises 4dB noise signal, the electrocardiosignal can be seen to be seriously polluted by the noise, the signal decomposed by using singular value does not basically contain the electromyographic noise, the signal after being filtered by a wavelet transform threshold value method is seriously distorted, and a band-pass filter basically has no noise reduction effect; as can be seen from FIGS. 4b and c, at different noise levels, the errors of the method of the present invention are smaller than those of wavelet transform and band-pass filter values, which indicates that the signal distortion degree is small; from fig. 4d, under the condition of high myoelectric noise pollution (0 dB input signal-to-noise ratio), the correlation between the signal and the clean signal after filtering by the method of the present invention still remains 94%, and under the condition of 0dB input signal-to-noise ratio, the correlation between the wavelet transform and the band-pass filter is lower than 80%, and the signal is severely distorted.
And those not described in detail in this specification are well within the skill of those in the art.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (4)
1. A method for removing myoelectric noise in electrocardiosignals based on singular value decomposition is characterized by comprising the following steps: the method specifically comprises the following steps:
s1, synthesizing a noise electrocardiosignal: the synthesized noise signals are mainly used for verifying the effectiveness of an algorithm and evaluating the noise reduction effect, and the clean electrocardiosignals and the myoelectricity noise are superposed on different amplification coefficients to synthesize simulated noise signals with different pollution degrees;
s2, searching for the QRS wave of the electrocardiosignal: searching for an electrocardiosignal QRS wave to extract the main characteristics of the electrocardiosignal, wherein the electrocardiosignal comprises a P wave, a T wave, a QRS wave and the like, and the QRS wave has the maximum amplitude and energy and is an essential step in electrocardiosignal analysis;
s3, calculating the interval of adjacent R waves: calculating adjacent R wave intervals, and calculating RR intervals (two adjacent R wave intervals) according to the existing R wave positions to obtain RR wave intervals with different lengths, wherein the RR wave numbers are as follows, RR = [ R ]1R2,R2R3,R3R4,…Rn-1Rn]；
S4, heart beat segmentation: the specific idea is that the electrocardiosignal is cut into different segments with the R wave as the center by the heart-splitting beat, and the R peak interval R with the left side is obtained according to the RR interval array obtained in the previous steplR, right R peak spacing RRrMaximum heart beat interval RRmax0.5R to the left of the selected R peaklR, 0.5RR to the right of the R peakrThe length is taken as a complete heart beat, the heart beats obtained by segmentation have unequal length, and the left side and the right side are simultaneously filled with 0-0.5 RRmaxThe total length of the heart beat is unified as RRmax+1；
S5, constructing a track matrix: constructing a periodic track matrix, and superposing a series of previously processed heart beat sequences to form a two-dimensional track matrix, wherein the formula of the track matrix A is as follows:
s6, singular value decomposition: singular value decomposition decomposes a two-dimensional matrix into mutually intersecting eigenvalues and eigenvectors, singular value decomposition being a matrix decomposition method defined as: a = U Σ VTU and V are left and right singular matrixes divided by the matrix A, elements except a main diagonal line are all 0, the main diagonal line elements are called singular values, the singular values are arranged from large to small, and the maximum singular value represents a signal main component;
s7, selecting singular values: when reconstructing an electrocardiosignal, selecting the maximum singular value as a clean signal, taking other singular values as noise and setting the noise to be 0, then reconstructing a two-dimensional signal matrix, and finally taking out each row in the matrix to restore a clean ECG signal;
s8, electrocardiosignal reduction: the two-dimensional matrix is restored to a filtered clean signal.
2. The method for removing myoelectric noise in electrocardiosignals based on singular value decomposition according to claim 1, characterized in that: r in the array in the step S31R2Indicating the separation between the first R peak and the second R peak.
3. The method for removing myoelectric noise in electrocardiosignals based on singular value decomposition according to claim 1, characterized in that: s in the step S51The position of the first R peak, Z is half of the maximum RR interval as the reference length, each row in the matrix represents a heart beat after the division is finished, an electrocardiosignal consists of n heart beats, and the R position S of each heart beatnAre in strict alignment.
4. The method for removing the electromyographic noise in the electrocardiosignals based on the singular value decomposition as claimed in claim 1, wherein: the formula of the two-dimensional signal matrix reconstructed in step S7 is: a' = u1σ1v1Where A' is the reconstructed signal matrix, σ1Is the maximum singular value, u1，v1Are singular vectors corresponding to the singular values, respectively.
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