CN112006679A - Wearable electrocardiosignal R wave detection method based on window variance transformation - Google Patents

Wearable electrocardiosignal R wave detection method based on window variance transformation Download PDF

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CN112006679A
CN112006679A CN202010891751.2A CN202010891751A CN112006679A CN 112006679 A CN112006679 A CN 112006679A CN 202010891751 A CN202010891751 A CN 202010891751A CN 112006679 A CN112006679 A CN 112006679A
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electrocardiosignal
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刘辉
谢小云
王英龙
舒明雷
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Shandong Institute of Artificial Intelligence
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Abstract

A wearable electrocardiosignal R wave detection method based on window variance transformation is characterized in that noise and waveforms of amplitude values which have the largest influence on R wave detection are suppressed through the window variance transformation, a high-accuracy and strong-anti-interference real-time R wave detection method is realized by utilizing a self-adaptive updated threshold value and an RR interval, the original waveform form of a signal is taken as a starting point, characteristics capable of better representing the original signal are extracted, and the method is suitable for R wave detection of various electrocardiosignals. The method effectively inhibits the influence of large-amplitude noise interfering with R wave detection, can solve the problem that the false detection rate is increased due to larger noise amplitude when the R wave is detected by the traditional threshold-based method, and is suitable for R detection of various electrocardiosignals containing mixed superimposed noise, especially wearable dynamic electrocardiosignals.

Description

Wearable electrocardiosignal R wave detection method based on window variance transformation
Technical Field
The invention relates to the technical field of electrocardiosignal waveform identification, in particular to a wearable electrocardiosignal R wave detection method based on window variance transformation.
Background
The QRS complex is the waveform with the largest amplitude and the most obvious characteristics in the electrocardiosignal, the form of the QRS complex is mostly in a stronger pulse shape, and the duration time is between 0.06s and 0.1 s. In the electrocardiosignal waveform detection, the accurate detection of the R wave is the basis of other waveform detection. The current common R wave identification methods include a digital filtering method, a wavelet transformation method, an adaptive threshold value, a convolutional neural network and the like. However, the above detection methods are mostly designed based on high-quality ECG signals, and the detection results are susceptible to noise interference, and are poor in robustness, especially when processing wearable dynamic ECG signals which are susceptible to strong interference of mixed noise such as motion artifacts. The traditional R wave detection algorithm is not suitable for electrocardiosignals with higher noise level, so that the problem of accurately and efficiently identifying R waves in the electrocardiosignals with high interference is a great challenge of electrocardiosignal waveform identification.
Disclosure of Invention
In order to overcome the defects of the technology, the invention provides the wearable electrocardiosignal R wave detection method which has high robustness and effectively inhibits the influence of the large-amplitude noise with interference in R wave detection and is based on the window variance transformation. The technical scheme adopted by the invention for overcoming the technical problems is as follows:
a wearable electrocardiosignal R wave detection method based on window variance transformation comprises the following steps:
a) utilizing a computer to suppress noise in the wearable dynamic electrocardiosignal to obtain a denoised electrocardiosignal;
b) carrying out waveform enhancement processing by using window variance square transformation, highlighting the QRS wave group in the electrocardiosignal after noise reduction, and inhibiting other waveforms and noises;
c) according to the difference between the amplitude of the QRS complex and other waveforms in the electrocardiosignal, R wave identification and detection are carried out to obtain an R wave set RS。
Further, in the step a), the noise in the wearable dynamic electrocardiosignal y containing the noise is filtered by using a moving average filtering method and a median filtering method, and the noise is filtered by a formula
Figure BDA0002654548970000011
Calculating to obtain a signal for removing high-frequency noise and baseline drift
Figure BDA0002654548970000021
In the formula
Figure BDA0002654548970000022
Represents a moving average filtering method with a filtering window of M, M being the width of the filtering window, bwnThe baseline of the nth sample point extracted for median filtering. Further, step b) comprises the following steps:
b-1) extracting local extreme points of the electrocardiosignals subjected to noise reduction, and performing window division on the electrocardiosignals subjected to noise reduction according to the local extreme points and the front and back time window size w to obtain extreme point window signals segp＝[S1,S2,...,SK]，SKIs the K extreme point window signal;
b-2) using the formula
Figure BDA0002654548970000023
Calculating the variance v of the window corresponding to the Kth local extreme pointkIn the formula
Figure BDA0002654548970000024
Figure BDA0002654548970000025
For removing high-frequency noise and base line drift signals
Figure BDA0002654548970000026
The signal of the t-th sampling point in the sequence is obtained, and the variance wvt of the window signal of the extreme point is obtainedp＝[v1,v2,...,vK]；
b-3) dividing n sampling points between two adjacent local extreme points into a window, setting the variance of the window to be 0, and setting a window signal seg corresponding to a non-extreme points＝[ns1,ns2,...,nsK-1]Window signal segsCorresponding variance of wvts＝[0,0,...,0]；
b-4) combining the window signal variances of the extreme points wvt according to the sample point index size in the windowpAnd non-extremum point variance wvtsObtaining the window variance variation corresponding to the whole signalAnd wvt, performing a squaring operation on wvt to obtain a signal swvt with enhanced QRS complexes.
Further, step c) comprises the steps of:
c-1) according to a threshold value
Figure BDA0002654548970000027
Generating a candidate set R of R-wavesC，lsAs the number of candidate points, RC＝{j|swvtj＞thrswvt,j＝1,2,...,ls}；
c-2) Using the candidate set RCInitializing parameters, and setting amplitude threshold thraIs a candidate set RCInitializing an electrocardiosignal RR interval thr by using 75 quantile values of the amplitude corresponding to the internal sampling pointsr＝360ms；
c-3) calculating the amplitude a of the jth candidate pointjWill satisfy the condition aj＞thraAnd
Figure BDA0002654548970000031
candidate points of (2) are added into the R wave set RsIn the formula
Figure BDA0002654548970000032
For the current R wave set RsThe index value of the last R-wave in (a);
c-4) traversing R wave candidate set RCFor the jth candidate point, using the formula
Figure BDA0002654548970000033
Updating the amplitude threshold thr in step c-2)aIn the formula
Figure BDA0002654548970000034
Is a candidate set RCThe mean of the amplitudes corresponding to the nth candidate point,
Figure BDA0002654548970000035
for the current R wave set RsThe amplitude of the tth R wave;
Figure BDA0002654548970000036
for the current R wave set RsThe number of (2);
c-5) by the formula
Figure BDA0002654548970000037
Updating the RR interval of the electrocardiosignal in the step c-2);
c-6) successively traversing the candidate set RCUpdate the amplitude threshold thraAnd RR interval thr of electrocardiosignalrJudging a decision rule according to the step c-3), and adding candidate points meeting the decision condition into the R wave set RsIn (3), R-wave detection is completed.
Further, a moving average filtering method with the width of 5 sampling points and a median filtering method with the filtering width of 200ms are adopted to filter the noise in the wearable dynamic electrocardiosignal y containing the noise.
The invention has the beneficial effects that: noise and waveform of amplitude which has the largest influence on R wave detection are suppressed through window variance transformation, a real-time R wave detection method with high accuracy and strong anti-interference performance is realized by using a self-adaptive updated threshold and an RR interval, the characteristics capable of better representing original signals are extracted by taking the original waveform form of the signals as a starting point, and the method is suitable for R wave detection of various electrocardiosignals. The method effectively inhibits the influence of large-amplitude noise interfering with R wave detection, can solve the problem that the false detection rate is increased due to larger noise amplitude when the R wave is detected by the traditional threshold-based method, and is suitable for R detection of various electrocardiosignals containing mixed superimposed noise, especially wearable dynamic electrocardiosignals.
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FIG. 1 is a flow chart of a method of the present invention;
FIG. 2 is a flow chart of a signal enhancement process of the present invention;
FIG. 3 is a flow chart of the R-wave detection process of the present invention.
Detailed Description
The invention will be further explained with reference to fig. 1, fig. 2 and fig. 3.
A wearable electrocardiosignal R wave detection method based on window variance transformation comprises the following steps:
a) utilizing a computer to suppress noise in the wearable dynamic electrocardiosignal to obtain a denoised electrocardiosignal; b) carrying out waveform enhancement processing by using window variance square transformation, highlighting the QRS wave group in the electrocardiosignal after noise reduction, and inhibiting other waveforms and noises;
c) according to the difference between the amplitude of the QRS complex and other waveforms in the electrocardiosignal, R wave identification and detection are carried out to obtain an R wave set RS。
Noise and waveform of amplitude which has the largest influence on R wave detection are suppressed through window variance transformation, a real-time R wave detection method with high accuracy and strong anti-interference performance is realized by using a self-adaptive updated threshold and an RR interval, the characteristics capable of better representing original signals are extracted by taking the original waveform form of the signals as a starting point, and the method is suitable for R wave detection of various electrocardiosignals. The method effectively inhibits the influence of large-amplitude noise interfering with R wave detection, can solve the problem that the false detection rate is increased due to larger noise amplitude when the R wave is detected by the traditional threshold-based method, and is suitable for R detection of various electrocardiosignals containing mixed superimposed noise, especially wearable dynamic electrocardiosignals.
In the step a), the noise in the wearable dynamic electrocardiosignal y containing the noise is filtered by using a moving average filtering method and a median filtering method, and the noise is filtered by a formula
Figure BDA0002654548970000041
Calculating to obtain a signal for removing high-frequency noise and baseline drift
Figure BDA0002654548970000042
In the formula
Figure BDA0002654548970000043
M is the width of the filter window, bwnThe baseline of the nth sample point extracted for median filtering.
The step b) comprises the following steps:
b-1) extracting local extreme points of the electrocardiosignals after noise reduction, and performing noise reduction according to the local extreme pointsThe electrocardiosignal is subjected to window division according to the size of a front time window and a rear time window as w to obtain an extreme point window signal segp＝[S1,S2,…,SK]，SKIs the K extreme point window signal;
b-2) using the formula
Figure BDA0002654548970000051
Calculating the variance v of the window corresponding to the Kth local extreme pointkIn the formula
Figure BDA0002654548970000052
Figure BDA0002654548970000053
For removing high-frequency noise and base line drift signals
Figure BDA0002654548970000054
The signal of the t-th sampling point in the sequence is obtained, and the variance wvt of the window signal of the extreme point is obtainedp＝[v1,v2,…,vK]；
b-3) dividing n sampling points between two adjacent local extreme points into a window, setting the variance of the window to be 0, and setting a window signal seg corresponding to a non-extreme points＝[ns1,ns2,…,nsK-1]Window signal segsCorresponding variance of wvts＝[0,0,...,0]；
b-4) combining the window signal variances of the extreme points wvt according to the sample point index size in the windowpAnd non-extremum point variance wvtsAnd obtaining a window variance transformation wvt corresponding to the whole signal, and performing a squaring operation on wvt to obtain a signal swvt with enhanced QRS complexes.
The step c) comprises the following steps:
c-1) according to a threshold value
Figure BDA0002654548970000055
Generating a candidate set R of R-wavesC，lsAs the number of candidate points, RC＝{j|swvtj＞thrswvt,j＝1,2,...,ls}；
c-2) Using the candidate set RCInitializing parameters, and setting amplitude threshold thraIs a candidate set RCInitializing an electrocardiosignal RR interval thr by using 75 quantile values of the amplitude corresponding to the internal sampling pointsr＝360ms；
c-3) calculating the amplitude a of the jth candidate pointjWill satisfy the condition aj＞thraAnd
Figure BDA0002654548970000056
candidate points of (2) are added into the R wave set RsIn the formula
Figure BDA0002654548970000057
For the current R wave set RsThe index value of the last R-wave in (a);
c-4) traversing R wave candidate set RCFor the jth candidate point, using the formula
Figure BDA0002654548970000061
Updating the amplitude threshold thr in step c-2)aIn the formula
Figure BDA0002654548970000062
Is a candidate set RCThe mean of the amplitudes corresponding to the nth candidate point,
Figure BDA0002654548970000063
for the current R wave set RsThe amplitude of the tth R wave;
Figure BDA0002654548970000064
for the current R wave set RsThe number of (2);
c-5) by the formula
Figure BDA0002654548970000065
Updating the RR interval of the electrocardiosignal in the step c-2);
c-6) successively traversing the candidate set RCUpdate the amplitude threshold thraAnd RR interval thr of electrocardiosignalrJudging a decision rule according to the step c-3), and adding candidate points meeting the decision condition into the R wave set RsIn (3), R-wave detection is completed.
Preferably, the noise in the wearable dynamic electrocardiosignal y containing noise is filtered by adopting a moving average filtering method with the width of 5 sampling points and a median filtering method with the filtering width of 200 ms.

Claims (5)

1. A wearable electrocardiosignal R wave detection method based on window variance transformation is characterized by comprising the following steps:
a) utilizing a computer to suppress noise in the wearable dynamic electrocardiosignal to obtain a denoised electrocardiosignal;
b) carrying out waveform enhancement processing by using window variance square transformation, highlighting the QRS wave group in the electrocardiosignal after noise reduction, and inhibiting other waveforms and noises;
c) according to the difference between the amplitude of the QRS complex and other waveforms in the electrocardiosignal, R wave identification and detection are carried out to obtain an R wave set RS。
2. The wearable electrocardiosignal R wave detection method based on window variance transformation as claimed in claim 1, characterized in that: in the step a), the noise in the wearable dynamic electrocardiosignal y containing the noise is filtered by using a moving average filtering method and a median filtering method, and the noise is filtered by a formula
Figure FDA0002654548960000011
Calculating to obtain a signal for removing high-frequency noise and baseline drift
Figure FDA0002654548960000012
In the formula
Figure FDA0002654548960000013
M is the width of the filter window, bwnThe baseline of the nth sample point extracted for median filtering.
3. The method for detecting the R wave of the wearable electrocardiosignal based on the window variance transformation as claimed in claim 2, wherein the step b) comprises the following steps:
b-1) extracting local extreme points of the electrocardiosignals subjected to noise reduction, and performing window division on the electrocardiosignals subjected to noise reduction according to the local extreme points and the front and back time window size w to obtain extreme point window signals segp＝[S1,S2,...,SK]，SKIs the K extreme point window signal;
b-2) using the formula
Figure FDA0002654548960000014
Calculating the variance v of the window corresponding to the Kth local extreme pointkIn the formula
Figure FDA0002654548960000015
Figure FDA0002654548960000016
For removing high-frequency noise and base line drift signals
Figure FDA0002654548960000017
The signal of the t-th sampling point in the sequence is obtained, and the variance wvt of the window signal of the extreme point is obtainedp＝[v1,v2,...,vK]；
b-3) dividing n sampling points between two adjacent local extreme points into a window, setting the variance of the window to be 0, and setting a window signal seg corresponding to a non-extreme points＝[ns1,ns2,...,nsK-1]Window signal segsCorresponding variance of wvts＝[0,0,...,0]；
b-4) combining the window signal variances of the extreme points wvt according to the sample point index size in the windowpAnd non-extremum point variance wvtsAnd obtaining a window variance transformation wvt corresponding to the whole signal, and performing a squaring operation on wvt to obtain a signal swvt with enhanced QRS complexes.
4. The wearable electrocardiosignal R wave detection method based on window variance transformation as claimed in claim 3, characterized in that: the step c) comprises the following steps:
c-1) according to a threshold value
Figure FDA0002654548960000021
Generating a candidate set R of R-wavesC，lsAs the number of candidate points, RC＝{j|swvtj＞thrswvt,j＝1,2,...,ls}；
c-2) Using the candidate set RCInitializing parameters, and setting amplitude threshold thraIs a candidate set RCInitializing an electrocardiosignal RR interval thr by using 75 quantile values of the amplitude corresponding to the internal sampling pointsr＝360ms；
c-3) calculating the amplitude a of the jth candidate pointjWill satisfy the condition aj＞thraAnd
Figure FDA0002654548960000022
candidate points of (2) are added into the R wave set RsIn the formula
Figure FDA0002654548960000023
For the current R wave set RsThe index value of the last R-wave in (a);
c-4) traversing R wave candidate set RCFor the jth candidate point, using the formula
Figure FDA0002654548960000024
Updating the amplitude threshold thr in step c-2)aIn the formula
Figure FDA0002654548960000025
Is a candidate set RCThe mean of the amplitudes corresponding to the nth candidate point,
Figure FDA0002654548960000026
for the current R wave set RsThe amplitude of the tth R wave;
Figure FDA0002654548960000027
for the current R wave set RsThe number of (2);
c-5) by the formula
Figure FDA0002654548960000031
Updating the RR interval of the electrocardiosignal in the step c-2);
c-6) successively traversing the candidate set RCUpdate the amplitude threshold thraAnd RR interval thr of electrocardiosignalrJudging a decision rule according to the step c-3), and adding candidate points meeting the decision condition into the R wave set RsIn (3), R-wave detection is completed.
5. The window variance transformation-based wearable electrocardiosignal R wave detection method according to claim 2, characterized in that: and filtering the noise in the wearable dynamic electrocardiosignal y containing the noise by adopting a moving average filtering method with the width of 5 sampling points and a median filtering method with the filtering width of 200 ms.
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