CN111166322A - Electrocardiosignal characteristic wave extraction method - Google Patents
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Abstract
An extraction method of electrocardiosignal characteristic waves belongs to the technical field of electrocardiosignal processing. The invention aims at the problems that the existing electrocardiosignal processing method has poor detection effect on noise signals or abnormal waveforms and the processing method has large calculation amount. The method comprises the following steps: collecting original electrocardiosignals of a tested person; preprocessing to obtain electrocardiosignals to be identified; eliminating outliers, and performing data smoothing processing of RR interphase to obtain electrocardiosignals with clear waveform characteristic wave groups; detecting a QRS wave group of the electrocardiosignals with clear waveform characteristic wave groups by adopting a threshold value method, and enabling data points of the QRS wave group to be completely zero; obtaining an electrocardiosignal after QRS return to zero; obtaining the electrocardiosignal after the P wave returns to zero; detecting the T wave peak value by adopting a threshold value method; then detecting a starting point and an end point of the T wave by using a derivation method; therefore, the extraction of the characteristic waves in the electrocardiosignals to be identified is sequentially realized. The invention is used for extracting the characteristic waves of the electrocardiosignals.
Description
Technical Field
The invention relates to an extraction method of electrocardiosignal characteristic waves, belonging to the technical field of electrocardiosignal processing.
Background
Electrocardiography, as a measure of electrical activity that reflects activation of the heart, has been an important basis for clinical detection and diagnosis of heart disease. Conventional electrocardiographic analysis usually records data in about 10 seconds in a hospital, and diagnosis is done manually by a doctor. The diagnosis method has low efficiency, is easy to be doped with subjective factors, and can not detect some abnormal electrocardiographic waveforms.
The electrocardiosignal can reflect the beat rhythm of the whole heart, and is the best method for measuring and diagnosing abnormal heart rhythm. The Electrocardiographic (ECG) waveform is composed of P wave, QRS complex, T wave and 50% -75% of possible U wave, as shown in FIG. 6.
Generally, the electrocardiosignal acquired directly by an instrument has a low amplitude, and is inevitably doped with various noises, such as power frequency interference, baseline drift and myoelectric interference, and the unsteady noises cause great obstacles to the detection of the ST segment. The frequency range of normal electrocardiosignals is between 0.05 Hz and 100Hz, the energy of QRS wave groups accounts for a large proportion, the amplitude is much higher than that of a P wave, and the P wave and the T wave are between 0.5 Hz and 10 Hz.
In arrhythmia monitoring, the morphology of the P wave and the QRS complex, the correlation between the P wave and the QRS complex, and whether the PR interval is constant or not are generally analyzed; therefore, the QRS complex and other characteristics of the electrocardiosignal are important for clinical diagnosis of arrhythmia and the like.
Common QRS complex detection methods include template detection, digital filtering, nonlinear transformation detection, wavelet transformation, and neural network. The traditional digital filtering method is to extract the QRS component of the electrocardiosignal in a pure digital filtering mode to eliminate interference; then, a decision signal with more obvious characteristics is obtained by utilizing nonlinear transformation; and finally, realizing R wave detection by using a threshold value and a related strategy. However, a single digital filter has poor detection effect on noise signals or abnormal waveforms; the wavelet transformation and other methods have strong anti-interference capability, but have large calculated amount, and are not suitable for real-time monitoring of electrocardiosignals in a short time.
Disclosure of Invention
The invention provides an electrocardiosignal characteristic wave extraction method, aiming at the problems that the existing electrocardiosignal processing method has poor detection effect on noise signals or abnormal waveforms and the processing method has large calculation amount.
The invention relates to an extraction method of electrocardiosignal characteristic waves, which comprises the following steps:
the method comprises the following steps: collecting original electrocardiosignals of a tested person at a sampling frequency of 1000 Hz;
step two: preprocessing an original electrocardiosignal to obtain an electrocardiosignal to be identified;
step three: eliminating outliers of the electrocardiosignals to be identified by adopting a Gaussian weighted moving average filtering method, and smoothing data of RR interphase to obtain electrocardiosignals with clear waveform characteristic wave groups;
step four: detecting a QRS wave group of the electrocardiosignals with clear waveform characteristic wave groups by adopting a threshold value method, and enabling data points of the QRS wave group to be completely zero; obtaining an electrocardiosignal after QRS return to zero;
step five: detecting a P wave peak value of the QRS zeroed electrocardiosignals by adopting a threshold value method; then detecting a P wave starting point and a P wave finishing point by using a derivation method, and completely zeroing P wave data points to obtain an electrocardiosignal after P waves are zeroed;
step six: detecting the T wave peak value of the electrocardiosignal after the P wave is reset to zero by adopting a threshold value method; then detecting a starting point and an end point of the T wave by using a derivation method; therefore, the extraction of the characteristic waves in the electrocardiosignals to be identified is sequentially realized.
According to the method for extracting characteristic waves of electrocardiosignals of the invention,
in the first step, original electrocardiosignals are collected for 10 minutes; the storage format of the original electrocardiosignals is mat, and the amplitude is-0.5-0.7 mV.
According to the method for extracting characteristic waves of electrocardiosignals of the invention,
in the second step, the obtaining process of the electrocardiosignals to be identified comprises the following steps:
performing polynomial weighted fitting on the original electrocardiosignals by adopting a local weighted regression scatter smooth filtering method, and estimating by using a least square method; the method comprises the steps of selecting local data in a preset proportion in an original electrocardiosignal, fitting the local data into a polynomial regression curve, and obtaining the electrocardiosignal to be identified after filtering and data smoothing processing.
According to the method for extracting characteristic waves of electrocardiosignals of the invention,
the second step specifically comprises:
step two, firstly: carrying out local unary cubic polynomial estimation on the original electrocardiosignal, and assuming the midpoint (x) of the original electrocardiosignali,yi) Is xiTaking the cubic weight function w to the height of the curve of the weight functioni(vj) Comprises the following steps:
wherein, Delta[i](vj)＝|xi-xj|/|xq-xj|i,j＝1,2,...,n；q＝[hn]H represents the degree of fitting a polynomial;
so as to obtain the original electrocardiosignal m (X) containing noisei) Is estimated value of
Further, an error r is obtainedi＝Yi-m(Xi)，YiRepresenting the electrocardiosignals estimated by the least square method;
step two: computing robust weights δi：
Wherein the content of the first and second substances,
step two and step three: using robust weights δiFor original electrocardioThe signal is subjected to local polynomial estimation again to obtain a new error ri；
Step two, four: and repeating the second step and the second step for pseudo-ginseng times to obtain steady estimation, and obtaining the electrocardiosignals to be identified after filtering and data smoothing.
According to the method for extracting characteristic waves of electrocardiosignals of the invention,
in the third step, the method for eliminating outliers comprises the following steps:
and eliminating outliers existing in the electrocardiosignals to be identified by adopting a Gaussian weighted moving average filtering method with thirty series numbers for the electrocardiosignals to be identified.
According to the method for extracting characteristic waves of electrocardiosignals of the invention,
the third step specifically comprises:
inputting an electrocardiosignal x (i) to be identified into a 30-level Gaussian filter, setting an output sequence as y (i), and calculating a single mean value of the output sequence y (i) as follows:
accordingly, the calculation process of the 30-step Gaussian filter is as follows:
yp(i)＝yp(i-1)+yp-1(i+k)-yp-1(i-(k+1)),i＝pk+2,pk+3,...,n-pk，
the electrocardiosignal after filtering treatment has fixed boundary effect interval length, and after iterative calculation of preset length, the convergence is stable, and outliers are removed, so that the electrocardiosignal with clear waveform characteristic wave group is obtained.
According to the method for extracting characteristic waves of electrocardiosignals of the invention,
in the fourth step, the process of obtaining the QRS return-to-zero electrocardiosignals comprises the following steps:
setting a threshold value a to be 0.2mV by using a threshold value method, detecting the peak value of an R wave, and setting the minimum distance between the peak values of the R wave to be 500 data points; marking the position of the R wave crest, and fixing the range of the QRS wave group between 80 data points in front of the R wave crest and 160 data points behind the R wave crest; detecting the positions of Q waves and S waves between 40 to 10 data points before the R wave peak by using a maximum value detection method, and marking the corresponding wave peak positions; and (4) according to the characteristics of the QRS wave group, completely zeroing the QRS wave group data points in the window data to obtain the electrocardiosignals after the QRS is zeroed.
According to the method for extracting characteristic waves of electrocardiosignals of the invention,
in the fifth step, the process of obtaining the electrocardiosignals after the P wave returns to zero comprises the following steps:
setting a threshold b to be 0.005mV by using a threshold method, detecting P wave peak values, and setting the minimum distance between the P wave peak values to be 550 data points; re-searching the maximum value of the electrocardiosignal after QRS return to zero in a way that the width is not less than 20 data points as a P wave peak value, setting the width of the peak value to be not less than 20 data points, and marking the position of the P wave peak value; according to the characteristics of the P wave, finding the starting point and the end point of the P wave by using a derivation method for 100 data before and after the peak of the P wave in the window data, marking the position, and then clearing all data points of the P wave; obtaining the electrocardiosignal after the P wave returns to zero.
According to the method for extracting characteristic waves of electrocardiosignals of the invention,
in the sixth step, the method for obtaining the starting point and the end point of the T wave comprises the following steps:
setting a threshold c to be 0.002mV by using a threshold method, detecting T wave peak values, and setting the minimum distance between the peak values to be 550 data points; and finding the starting point and the end point of the T wave by using a derivation method, and marking the positions of the characteristic points of the T wave.
The invention has the beneficial effects that: the method has simple filtering process and small calculated amount; the characteristic extraction method is high in accuracy, can effectively save calculation time, and brings convenience to the function expansion of the electrocardiosignal processing and analyzing system.
The method is a multi-threshold electrocardiosignal characteristic real-time detection method based on a multi-digital filtering method.
Drawings
FIG. 1 is a flow chart illustrating a method for extracting characteristic waves of an electrocardiographic signal according to the present invention;
FIG. 2 is a LOWESS filtered waveform of an ECG signal;
FIG. 3 is a diagram of a Gaussian filtered waveform of an ECG signal;
FIG. 4 is a waveform of an original cardiac signal;
FIG. 5 is a waveform diagram of an electrocardiographic signal to be recognized after feature extraction; in the figure, ECG signal represents electrocardiosignal, Q-wave represents Q wave, R-wave represents R wave, S-wave represents S wave, Q-pre represents Q wave front, S-post represents S wave back, P-wave represents P wave, and T-wave represents T wave;
fig. 6 is a waveform diagram of a normal cardiac signal.
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.
It should be noted that the embodiments and features of the embodiments may be combined with each other without conflict.
The invention is further described with reference to the following drawings and specific examples, which are not intended to be limiting.
In a first specific embodiment, as shown in fig. 1, the present invention provides a method for extracting an electrocardiographic signal characteristic wave, including:
the method comprises the following steps: collecting original electrocardiosignals of a tested person at a sampling frequency of 1000 Hz;
step two: preprocessing an original electrocardiosignal to obtain an electrocardiosignal to be identified;
step three: eliminating outliers of the electrocardiosignals to be identified by adopting a Gaussian weighted moving average filtering method, and smoothing data of RR interphase to obtain electrocardiosignals with clear waveform characteristic wave groups;
step four: detecting a QRS wave group of the electrocardiosignals with clear waveform characteristic wave groups by adopting a threshold value method, and enabling data points of the QRS wave group to be completely zero; obtaining an electrocardiosignal after QRS return to zero;
step five: detecting a P wave peak value of the QRS zeroed electrocardiosignals by adopting a threshold value method; then detecting a P wave starting point and a P wave finishing point by using a derivation method, and completely zeroing P wave data points to obtain an electrocardiosignal after P waves are zeroed;
step six: detecting the T wave peak value of the electrocardiosignal after the P wave is reset to zero by adopting a threshold value method; then detecting a starting point and an end point of the T wave by using a derivation method; therefore, the extraction of the characteristic waves in the electrocardiosignals to be identified is sequentially realized.
In this embodiment, a, filtering and impurity removal values can be performed on the electrocardiosignals based on the LOSSES method; B. calculating a threshold value according to the electrocardiosignals; C. detecting the position of an R wave in the electrocardiosignal according to the threshold; D. filtering the electrocardiosignal based on a Gaussian filtering method to correct the waveform of the electrocardiosignal; E. calculating a threshold value according to the electrocardiosignal T wave; F. calculating a threshold value according to the electrocardiosignals; G. and detecting the P wave of the electrocardiosignal according to a threshold value.
The characteristic values of the electrocardiosignals comprise: p wave crest, starting point and end point; a PR segment; the QRS wave group comprises R wave, Q wave and S wave peaks, and a starting point of the Q wave and an end point of the S wave; an ST segment; t wave crest, starting point and end point; QT intervals and RR intervals.
After the characteristic values are extracted, the positions of wave crests of P waves, R waves and T waves can be determined by using a threshold value method; determining the starting point and the end point of P waves, R waves, Q waves, S waves and T waves by using a derivation method; and finally, determining PR segment, ST segment, QT interval and RR interval according to the characteristics of the electrocardiosignal.
Further, in the first step, the original electrocardiosignals are collected for 10 minutes; the storage format of the original electrocardiosignals is mat, and the amplitude is-0.5-0.7 mV.
In the data processing process, Matlab software can be used for reading the electrocardiosignal original data into a working space.
In particular implementations, the test subjects may select a population of more stressed programmers and researchers, doctor's students, 30 men and 30 women in daily life. The experiment lasted 10 minutes, and the electrocardiosignals of the participants were collected simultaneously using the BITalino device from the company PluX WirelessBiosignals. When data are collected, the environment of the tested person is relatively quiet, noise interference is reduced, and larger limb movement is not suitable. The measurement mode is as follows: electrocardiosignals (ECG) are collected by adopting 3 patch electrodes.
Before the experiment, the participants were allowed to sit still for several minutes, and then relaxed and calm down.
Still further, in the second step, the obtaining process of the electrocardiosignals to be identified includes:
performing polynomial weighted fitting on the original electrocardiosignals by adopting a local weighted regression scatter smoothing (LOWESS) filtering method, and estimating by using a least square method; the method comprises the steps of selecting local data in a preset proportion in an original electrocardiosignal, fitting the local data into a polynomial regression curve, and obtaining the electrocardiosignal to be identified after filtering and data smoothing processing.
By using the digital wave trap, 50Hz power frequency interference in the original data of the electrocardiosignal can be removed; by using the LOWESS filtering method, the baseline drift in the original electrocardiosignal data can be removed.
Still further, the second step specifically includes:
step two, firstly: carrying out local unary cubic polynomial estimation on the original electrocardiosignal, and assuming the midpoint (x) of the original electrocardiosignali,yi) Is xiTaking the cubic weight function w to the height of the curve of the weight functioni(vj) Comprises the following steps:
wherein, Delta[i](vj)＝|xi-xj|/|xq-xj|i,j＝1,2,...,n；q＝[hn]H represents the degree of fitting a polynomial;
so as to obtain the original electrocardiosignal m (X) containing noisei) Is estimated value of
Further, an error r is obtainedi＝Yi-m(Xi)，YiRepresenting the electrocardiosignals estimated by the least square method;
step two: computing robust weights δi：
Wherein the content of the first and second substances,
step two and step three: using robust weights δiCarrying out local polynomial estimation on the original electrocardiosignal again to obtain a new error ri；
Step two, four: and repeating the second step and the second step for pseudo-ginseng times to obtain steady estimation, and obtaining the electrocardiosignals to be identified after filtering and data smoothing.
The local weighted regression scatter smoothing (LOWESS) algorithm is mainly used for carrying out polynomial weighted fitting on points to be fitted according to local observation data, estimating by using a least square method, and fitting a subset into a polynomial regression curve by taking a certain proportion of local data. The method is beneficial to displaying the local rule and trend of the data, and is convenient to observe the change trend of the whole curve.
Still further, in the third step, the method for rejecting outliers comprises:
and eliminating outliers existing in the electrocardiosignals to be identified by adopting a Gaussian weighted moving average filtering method with thirty-grade numbers for the electrocardiosignals to be identified, and smoothing data of RR intervals of the electrocardiosignals to enable the waveforms of the characteristic wave groups to be clearer.
And eliminating outliers existing in the electrocardiosignals by adopting a Gaussian weighted moving average filtering method, and smoothing data to enable data points in the RR interphase to be convenient for the detection of the following characteristic points.
Still further, the third step specifically includes:
inputting an electrocardiosignal x (i) to be identified into a 30-level Gaussian filter, setting an output sequence as y (i), and calculating a single mean value of the output sequence y (i) as follows:
accordingly, the calculation process of the 30-step Gaussian filter is as follows:
yp(i)＝yp(i-1)+yp-1(i+k)-yp-1(i-(k+1)),i＝pk+2,pk+3,...,n-pk，
the electrocardiosignal after filtering treatment has fixed boundary effect interval length, and after iterative calculation of preset length, the convergence is stable, and outliers are removed, so that the electrocardiosignal with clear waveform characteristic wave group is obtained.
The above is the calculation process of the 30-level gaussian weighted moving average filtering. The mean function adopts a Gaussian moving average filtering method under the weight function, and has simple structure and less calculation amount. The length of the boundary effect interval of the electrocardiosignals after filtering is fixed, the data are converged and stabilized after iterative computation with a certain length, the electrocardiosignal smoothing effect is achieved, and the difficulty in extracting the electrocardiosignal features is reduced.
Further, in the fourth step, the process of obtaining the QRS zeroed electrocardiosignal includes:
setting a threshold value a to be 0.2mV by using a threshold value method, detecting the peak value of an R wave, and setting the minimum distance between the peak values of the R wave to be 500 data points; marking the position of the R wave crest, and fixing the range of the QRS wave group between 80 data points in front of the R wave crest and 160 data points behind the R wave crest; detecting the positions of Q waves and S waves between 40 to 10 data points before the R wave peak by using a maximum value detection method, and marking the corresponding wave peak positions; and (4) according to the characteristics of the QRS wave group, completely zeroing the QRS wave group data points in the window data to obtain the electrocardiosignals after the QRS is zeroed.
In the embodiment, the minimum distance between peak values is set firstly, and after the position of the R wave is found, the position of the wave crest is marked; and according to the characteristics of the electrocardiosignals, fixing the range of the QRS wave group between the front and the back of the wave crest, detecting the positions of the Q wave and the S wave by using a maximum value detection method, and marking the position of the wave crest.
Furthermore, in the fifth step, the process of obtaining the electrocardiographic signal after the P-wave return to zero includes:
setting a threshold b to be 0.005mV by using a threshold method, detecting P wave peak values, and setting the minimum distance between the P wave peak values to be 550 data points; re-searching the maximum value of the electrocardiosignal after QRS return to zero in a way that the width is not less than 20 data points as a P wave peak value, setting the width of the peak value to be not less than 20 data points, and marking the position of the P wave peak value; according to the characteristics of the P wave, finding the starting point and the end point of the P wave by using a derivation method for 100 data before and after the peak of the P wave in the window data, marking the position, and then clearing all data points of the P wave; obtaining the electrocardiosignal after the P wave returns to zero.
In the present embodiment, the minimum distance and the minimum width between peaks are set, and the maximum value in the data is searched again by the maximum value detection method, and the P-wave peak position is marked.
Still further, in the sixth step, the method for obtaining the start point and the end point of the T wave includes:
setting a threshold c to be 0.002mV by using a threshold method, detecting T wave peak values, and setting the minimum distance between the peak values to be 550 data points; and finding the starting point and the end point of the T wave by using a derivation method, and marking the positions of the characteristic points of the T wave.
In the present embodiment, the minimum distance between the peaks is first set to obtain the peak of the T wave, and the starting point and the end point of the T wave are found by using the derivation method, and the position of the starting point of the T wave is marked.
According to the starting point marks of the T wave, the P wave and the QRS complex, PR segment, ST segment, QT interval and RR interval of the electrocardiosignal can be separated.
After the processing process, detecting P wave, QRS wave group, PR interval, T wave, QT interval and ST interval of the electrocardiosignal; after the characteristics of the electrocardiosignals are obtained and identified, the physiological parameters are analyzed and extracted, and the HRV characteristic analysis can be carried out on the electrocardiosignals after the RR period of the electrocardiosignals is obtained most importantly.
Although the invention herein has been described with reference to particular embodiments, it is to be understood that these embodiments are merely illustrative of the principles and applications of the present invention. It is therefore to be understood that numerous modifications may be made to the illustrative embodiments and that other arrangements may be devised without departing from the spirit and scope of the present invention as defined by the appended claims. It should be understood that features described in different dependent claims and herein may be combined in ways different from those described in the original claims. It is also to be understood that features described in connection with individual embodiments may be used in other described embodiments.
Claims (9)
1. An extraction method of electrocardiosignal characteristic waves is characterized by comprising the following steps:
the method comprises the following steps: collecting original electrocardiosignals of a tested person at a sampling frequency of 1000 Hz;
step two: preprocessing an original electrocardiosignal to obtain an electrocardiosignal to be identified;
step three: eliminating outliers of the electrocardiosignals to be identified by adopting a Gaussian weighted moving average filtering method, and smoothing data of RR interphase to obtain electrocardiosignals with clear waveform characteristic wave groups;
step four: detecting a QRS wave group of the electrocardiosignals with clear waveform characteristic wave groups by adopting a threshold value method, and enabling data points of the QRS wave group to be completely zero; obtaining an electrocardiosignal after QRS return to zero;
step five: detecting a P wave peak value of the QRS zeroed electrocardiosignals by adopting a threshold value method; then detecting a P wave starting point and a P wave finishing point by using a derivation method, and completely zeroing P wave data points to obtain an electrocardiosignal after P waves are zeroed;
step six: detecting the T wave peak value of the electrocardiosignal after the P wave is reset to zero by adopting a threshold value method; then detecting a starting point and an end point of the T wave by using a derivation method; therefore, the extraction of the characteristic waves in the electrocardiosignals to be identified is sequentially realized.
2. The method for extracting an electrocardiographic signal characteristic wave according to claim 1,
in the first step, original electrocardiosignals are collected for 10 minutes; the storage format of the original electrocardiosignals is mat, and the amplitude is-0.5-0.7 mV.
3. The method for extracting an electrocardiographic signal characteristic wave according to claim 2,
in the second step, the obtaining process of the electrocardiosignals to be identified comprises the following steps:
performing polynomial weighted fitting on the original electrocardiosignals by adopting a local weighted regression scatter smooth filtering method, and estimating by using a least square method; the method comprises the steps of selecting local data in a preset proportion in an original electrocardiosignal, fitting the local data into a polynomial regression curve, and obtaining the electrocardiosignal to be identified after filtering and data smoothing processing.
4. The method for extracting an electrocardiographic signal characteristic wave according to claim 3,
the second step specifically comprises:
step two, firstly: carrying out local unary cubic polynomial estimation on the original electrocardiosignal, and assuming the midpoint (x) of the original electrocardiosignali,yi) Is xiTaking the cubic weight function w to the height of the curve of the weight functioni(vj) Comprises the following steps:
wherein, Delta[i](vj)＝|xi-xj|/|xq-xj|i,j＝1,2,...,n；q＝[hn]H represents the degree of fitting a polynomial;
so as to obtain the original electrocardiosignal m (X) containing noisei) Is estimated value of
Further, an error r is obtainedi＝Yi-m(Xi)，YiRepresenting the electrocardiosignals estimated by the least square method;
step two: computing robust weights δi：
Wherein the content of the first and second substances,
step two and step three: using robust weights δiCarrying out local polynomial estimation on the original electrocardiosignal again to obtain a new error ri；
Step two, four: and repeating the second step and the second step for pseudo-ginseng times to obtain steady estimation, and obtaining the electrocardiosignals to be identified after filtering and data smoothing.
5. The method for extracting an electrocardiographic signal characteristic wave according to claim 4,
in the third step, the method for eliminating outliers comprises the following steps:
and eliminating outliers existing in the electrocardiosignals to be identified by adopting a Gaussian weighted moving average filtering method with thirty series numbers for the electrocardiosignals to be identified.
6. The method for extracting an electrocardiographic signal characteristic wave according to claim 5,
the third step specifically comprises:
inputting an electrocardiosignal x (i) to be identified into a 30-level Gaussian filter, setting an output sequence as y (i), and calculating a single mean value of the output sequence y (i) as follows:
accordingly, the calculation process of the 30-step Gaussian filter is as follows:
yp(i)＝yp(i-1)+yp-1(i+k)-yp-1(i-(k+1)),i＝pk+2,pk+3,...,n-pk，
the electrocardiosignal after filtering treatment has fixed boundary effect interval length, and after iterative calculation of preset length, the convergence is stable, and outliers are removed, so that the electrocardiosignal with clear waveform characteristic wave group is obtained.
7. The method for extracting an electrocardiographic signal characteristic wave according to claim 6,
in the fourth step, the process of obtaining the QRS return-to-zero electrocardiosignals comprises the following steps:
setting a threshold value a to be 0.2mV by using a threshold value method, detecting the peak value of an R wave, and setting the minimum distance between the peak values of the R wave to be 500 data points; marking the position of the R wave crest, and fixing the range of the QRS wave group between 80 data points in front of the R wave crest and 160 data points behind the R wave crest; detecting the positions of Q waves and S waves between 40 to 10 data points before the R wave peak by using a maximum value detection method, and marking the corresponding wave peak positions; and (4) according to the characteristics of the QRS wave group, completely zeroing the QRS wave group data points in the window data to obtain the electrocardiosignals after the QRS is zeroed.
8. The method for extracting an electrocardiographic signal characteristic wave according to claim 7,
in the fifth step, the process of obtaining the electrocardiosignals after the P wave returns to zero comprises the following steps:
setting a threshold b to be 0.005mV by using a threshold method, detecting P wave peak values, and setting the minimum distance between the P wave peak values to be 550 data points; re-searching the maximum value of the electrocardiosignal after QRS return to zero in a way that the width is not less than 20 data points as a P wave peak value, setting the width of the peak value to be not less than 20 data points, and marking the position of the P wave peak value; according to the characteristics of the P wave, finding the starting point and the end point of the P wave by using a derivation method for 100 data before and after the peak of the P wave in the window data, marking the position, and then clearing all data points of the P wave; obtaining the electrocardiosignal after the P wave returns to zero.
9. The method for extracting an electrocardiographic signal characteristic wave according to claim 8,
in the sixth step, the method for obtaining the starting point and the end point of the T wave comprises the following steps:
setting a threshold c to be 0.002mV by using a threshold method, detecting T wave peak values, and setting the minimum distance between the peak values to be 550 data points; and finding the starting point and the end point of the T wave by using a derivation method, and marking the positions of the characteristic points of the T wave.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113876332A (en) * | 2021-10-27 | 2022-01-04 | 深圳大学 | Electrocardiosignal monitoring device and method |
| CN114027853A (en) * | 2021-12-16 | 2022-02-11 | 安徽心之声医疗科技有限公司 | QRS complex detection method, device, medium and equipment based on feature template matching |
Citations (20)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5355880A (en) * | 1992-07-06 | 1994-10-18 | Sandia Corporation | Reliable noninvasive measurement of blood gases |
| US5913308A (en) * | 1996-12-19 | 1999-06-22 | Hewlett-Packard Company | Apparatus and method for determining respiratory effort from muscle tremor information in ECG signals |
| US20060092475A1 (en) * | 2004-11-01 | 2006-05-04 | Canon Kabushiki Kaisha | Image processing apparatus and image processing method |
| CN101496716A (en) * | 2009-02-26 | 2009-08-05 | 周洪建 | Measurement method for detecting sleep apnoea with ECG signal |
| US20090240461A1 (en) * | 2006-03-14 | 2009-09-24 | Sony Corporation | Body movement detector, body movement detection method and body movement detection program |
| CN101843480A (en) * | 2009-03-27 | 2010-09-29 | 华为技术有限公司 | Method and device for processing bioelectrical signals |
| CN102626310A (en) * | 2012-04-23 | 2012-08-08 | 天津工业大学 | Electrocardiogram signal feature detection algorithm based on wavelet transformation lifting and approximate envelope improving |
| CA2796543A1 (en) * | 2011-12-01 | 2013-06-01 | Sony Corporation | System and method for performing depth estimation utilizing defocused pillbox images |
| CN104123560A (en) * | 2014-07-03 | 2014-10-29 | 中山大学 | Phase encoding characteristic and multi-metric learning based vague facial image verification method |
| CN104323771A (en) * | 2014-11-11 | 2015-02-04 | 北京海思敏医疗技术有限公司 | Method and device for detecting P-wave and T-wave in electrocardiogram (ECG) signal |
| US20150342489A1 (en) * | 2014-06-02 | 2015-12-03 | Indian Institute Of Technology Delhi | Qrs complex identification in electrocardiogram signals |
| CN105527617A (en) * | 2016-02-06 | 2016-04-27 | 哈尔滨工业大学 | Ground penetrating radar data background removing method based on robust principal component analysis |
| CN106037710A (en) * | 2014-11-24 | 2016-10-26 | 西门子公司 | Synthetic data-driven hemodynamic determination in medical imaging |
| CN106125395A (en) * | 2016-09-14 | 2016-11-16 | 京东方科技集团股份有限公司 | One is to box precision compensation method and the system of compensation |
| CN106419898A (en) * | 2016-08-12 | 2017-02-22 | 武汉中旗生物医疗电子有限公司 | Method removing electrocardiosignal baseline drift |
| CN107863128A (en) * | 2017-11-28 | 2018-03-30 | 广东工业大学 | A kind of multi-level flash cell error correction method, system, device and readable storage medium storing program for executing |
| CN108272447A (en) * | 2018-03-27 | 2018-07-13 | 厦门金网科技有限公司 | A kind of breathing and HR Heart Rate detecting system based on UWB technology |
| CN109359778A (en) * | 2018-11-13 | 2019-02-19 | 中石化石油工程技术服务有限公司 | Short-term Forecast of Natural Gas Load method based on Optimization Experience mode decomposition |
| CN109381181A (en) * | 2017-08-14 | 2019-02-26 | 深圳大学 | The end-point detecting method of electrocardiosignal signature waveform |
| US20190343429A1 (en) * | 2014-03-17 | 2019-11-14 | One Million Metrics Corp. | System and method for monitoring safety and productivity of physical tasks |
-
2020
- 2020-01-17 CN CN202010053269.1A patent/CN111166322A/en active Pending
Patent Citations (20)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5355880A (en) * | 1992-07-06 | 1994-10-18 | Sandia Corporation | Reliable noninvasive measurement of blood gases |
| US5913308A (en) * | 1996-12-19 | 1999-06-22 | Hewlett-Packard Company | Apparatus and method for determining respiratory effort from muscle tremor information in ECG signals |
| US20060092475A1 (en) * | 2004-11-01 | 2006-05-04 | Canon Kabushiki Kaisha | Image processing apparatus and image processing method |
| US20090240461A1 (en) * | 2006-03-14 | 2009-09-24 | Sony Corporation | Body movement detector, body movement detection method and body movement detection program |
| CN101496716A (en) * | 2009-02-26 | 2009-08-05 | 周洪建 | Measurement method for detecting sleep apnoea with ECG signal |
| CN101843480A (en) * | 2009-03-27 | 2010-09-29 | 华为技术有限公司 | Method and device for processing bioelectrical signals |
| CA2796543A1 (en) * | 2011-12-01 | 2013-06-01 | Sony Corporation | System and method for performing depth estimation utilizing defocused pillbox images |
| CN102626310A (en) * | 2012-04-23 | 2012-08-08 | 天津工业大学 | Electrocardiogram signal feature detection algorithm based on wavelet transformation lifting and approximate envelope improving |
| US20190343429A1 (en) * | 2014-03-17 | 2019-11-14 | One Million Metrics Corp. | System and method for monitoring safety and productivity of physical tasks |
| US20150342489A1 (en) * | 2014-06-02 | 2015-12-03 | Indian Institute Of Technology Delhi | Qrs complex identification in electrocardiogram signals |
| CN104123560A (en) * | 2014-07-03 | 2014-10-29 | 中山大学 | Phase encoding characteristic and multi-metric learning based vague facial image verification method |
| CN104323771A (en) * | 2014-11-11 | 2015-02-04 | 北京海思敏医疗技术有限公司 | Method and device for detecting P-wave and T-wave in electrocardiogram (ECG) signal |
| CN106037710A (en) * | 2014-11-24 | 2016-10-26 | 西门子公司 | Synthetic data-driven hemodynamic determination in medical imaging |
| CN105527617A (en) * | 2016-02-06 | 2016-04-27 | 哈尔滨工业大学 | Ground penetrating radar data background removing method based on robust principal component analysis |
| CN106419898A (en) * | 2016-08-12 | 2017-02-22 | 武汉中旗生物医疗电子有限公司 | Method removing electrocardiosignal baseline drift |
| CN106125395A (en) * | 2016-09-14 | 2016-11-16 | 京东方科技集团股份有限公司 | One is to box precision compensation method and the system of compensation |
| CN109381181A (en) * | 2017-08-14 | 2019-02-26 | 深圳大学 | The end-point detecting method of electrocardiosignal signature waveform |
| CN107863128A (en) * | 2017-11-28 | 2018-03-30 | 广东工业大学 | A kind of multi-level flash cell error correction method, system, device and readable storage medium storing program for executing |
| CN108272447A (en) * | 2018-03-27 | 2018-07-13 | 厦门金网科技有限公司 | A kind of breathing and HR Heart Rate detecting system based on UWB technology |
| CN109359778A (en) * | 2018-11-13 | 2019-02-19 | 中石化石油工程技术服务有限公司 | Short-term Forecast of Natural Gas Load method based on Optimization Experience mode decomposition |
Non-Patent Citations (18)
| Title |
|---|
| AL MASUD A.: "real time P, QRS and T wave detection by QRS matched filter method", 《PROCEEDINGS OF ARSSS INTERNATIONAL CONFERENCE》 * |
| JHA, S.; SINGH, O.; SUNKARIA, R.K.: "Modified approach for ECG signal denoising based on empirical mode decomposition and moving average filter", 《INTERNATIONAL JOURNAL OF MEDICAL ENGINEERING AND INFORMATICS》 * |
| PANDER, TOMASZ；PRZYBYLA, TOMASZ: "ECG Signal Enhancement with Serial Cascade OWA Filter", 《PROCEEDINGS OF THE 25TH INTERNATIONAL CONFERENCE MIXED DESIGN OF INTEGRATED CIRCUITS AND SYSTEM (MIXDES 2018)》 * |
| PINTO, JOAO RIBEIRO；CARDOSO, JAIME S；LOURENCO, ANDRE: "Towards a Continuous Biometric System Based on ECG Signals Acquired on the Steering Wheel", 《SENSORS》 * |
| RAHUL, J.; SORA, M.; SHARMA, L.: "Baseline correction of ECG using regression estimation method", 《2019 4TH INTERNATIONAL CONFERENCE ON INTERNET OF THINGS: SMART INNOVATION AND USAGES (IOT-SIU). PROCEEDINGS》 * |
| 刘琳岚,高声荣,舒坚.: "基于随机森林的链路质量预测", 《通信学报》 * |
| 李之鑫,乔建华,边崇: "高斯移动平均环境下带时滞的期权定价模型", 《苏州科技学院学报(自然科学版)》 * |
| 李文静,谢海滨,严序,周敏雄,向之明,杨光: "基于局部位移校正的磁共振图像相干平均", 《波谱学杂志》 * |
| 杨子立: "基于差分算法的ECG波形实时检测方法", 《牡丹江师范学院学报(自然科学版)》 * |
| 杨明祺: "基于无监督学习的心冲击信号心率变异性检测方法研究", 《万方》 * |
| 温兴贤: "炉内燃烧工况分析与诊断", 《中国优秀硕士学位论文全文数据库》 * |
| 程村: "广义高斯核模型在心电信号稀疏分解中的应用", 《工程地球物理学报》 * |
| 苏丽.: "远程心电监护诊断系统心电信号处理方法研究", 《中国博士学位论文全文数据库》 * |
| 苏腾: "心电信号降噪和QRS波识别方法研究", 《万方》 * |
| 钟佳成: "考虑风电预测误差区间的日内滚动经济调度", 《中国优秀硕士学位论文全文数据库》 * |
| 陈耿铎,曾有灵,李喆.: "自适应双阈值心电信号检测算法研究", 《暨南大学学报(自然科学与医学版)》 * |
| 黄喻: "LOWESS方法在同位素地层学中的应用", 《中国优秀硕士学位论文全文数据库》 * |
| 龚火青: "联合先验信息与学习机制的加权l_1最小模型研究", 《万方》 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113876332A (en) * | 2021-10-27 | 2022-01-04 | 深圳大学 | Electrocardiosignal monitoring device and method |
| CN113876332B (en) * | 2021-10-27 | 2023-12-08 | 深圳大学 | Electrocardiosignal monitoring device and method |
| CN114027853A (en) * | 2021-12-16 | 2022-02-11 | 安徽心之声医疗科技有限公司 | QRS complex detection method, device, medium and equipment based on feature template matching |
| CN114027853B (en) * | 2021-12-16 | 2022-09-27 | 安徽心之声医疗科技有限公司 | QRS complex detection method, device, medium and equipment based on feature template matching |
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