CN108272451A - A kind of QRS wave recognition methods based on improvement wavelet transformation - Google Patents
A kind of QRS wave recognition methods based on improvement wavelet transformation Download PDFInfo
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- A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
- A61B5/316—Modalities, i.e. specific diagnostic methods
- A61B5/318—Heart-related electrical modalities, e.g. electrocardiography [ECG]
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- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
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- A61B5/316—Modalities, i.e. specific diagnostic methods
- A61B5/318—Heart-related electrical modalities, e.g. electrocardiography [ECG]
- A61B5/346—Analysis of electrocardiograms
- A61B5/349—Detecting specific parameters of the electrocardiograph cycle
- A61B5/352—Detecting R peaks, e.g. for synchronising diagnostic apparatus; Estimating R-R interval
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- A61B5/316—Modalities, i.e. specific diagnostic methods
- A61B5/318—Heart-related electrical modalities, e.g. electrocardiography [ECG]
- A61B5/346—Analysis of electrocardiograms
- A61B5/349—Detecting specific parameters of the electrocardiograph cycle
- A61B5/366—Detecting abnormal QRS complex, e.g. widening
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- A—HUMAN NECESSITIES
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- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
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Abstract
The present invention relates to a kind of based on the QRS wave recognition methods for improving wavelet transformation, includes the following steps：1) electrocardiosignal to be identified is obtained；2) it uses Lifting Wavelet to carry out time-frequency domain conversation to electrocardiosignal, and the high-frequency noise in electrocardiosignal is filtered out using threshold method；3) QRS complex recognizer is used to obtain R crest value point locations.Compared with prior art, the present invention has many advantages, such as that reduction complexity, raising signal-to-noise ratio, processing are accurate.
Description
Technical field
The present invention relates to engineering in medicine technical fields, are identified based on the QRS wave for improving wavelet transformation more particularly, to a kind of
Method.
Background technology
The blood circulation function of heart spontaneity originates from SA node, and there are about 60-100 depolarising works for average minute clock
With.Along with a series of potential change during excited generation, propagation and the disappearance of heart, and pass through leading around heart
Electrical tissue and body fluid reflection come to body surface.The wavy curve occurred in each cardiac cycle on normal ECG changes
Regular, one section of most important one is referred to as QRS wave.
Identification to QRS complex is the matter of utmost importance of electro-cardiologic signal waveforms identification, it is important to the identification of R crest value points.It is logical
Cross the identification to R crest value points, it may be determined that heart rate, to distinguish normal and abnormal cardiac rate, the identification to QRS width can be true
The time for determining sequences of ventricular depolarization, as the foundation for medically diagnosing and analyzing.
In the research of early stage, people are asked using single time domain or the method for frequency domain come the identification for QRS complex of analyzing and researching
Topic, these methods do not have and meanwhile time-domain and frequency-domain resolution capability, be very restricted.With building for Wavelet Analysis Theory
It is vertical, based on when-wavelet transformation of frequency analysis successfully applied in ECG Signal Analysis, but its limitation is algorithm
Complexity is excessively high, and real-time is poor.
With the development of ecg computer technology and the communication technology and computer and internet in the family general
And so that the automatic collection of electrocardiosignal and diagnosis are possibly realized.A variety of analysis electrocardiosignal QRSs are proposed both at home and abroad at present
The method of signature waveform is used for Diagnosing Cardiac lesion.However, electrocardiosignal is a kind of very faint signal, only millivolt level,
It is highly prone to the pollution of interference.Current automatic analysis technology is not mature enough, also needed in terms of waveform recognition accuracy into
One step is improved, and significantly more efficient QRS preconditioning techniques and ecg characteristics parameter extraction technology are developed.
Invention content
It is an object of the present invention to overcome the above-mentioned drawbacks of the prior art and provide one kind based on improvement small echo
The QRS wave recognition methods of transformation.
The purpose of the present invention can be achieved through the following technical solutions：
A kind of QRS wave recognition methods based on improvement wavelet transformation, includes the following steps：
1) electrocardiosignal to be identified is obtained；
2) it uses Lifting Wavelet to carry out time-frequency domain conversation to electrocardiosignal, and is filtered out in electrocardiosignal using threshold method
High-frequency noise；
3) QRS complex recognizer is used to obtain R crest value point locations.
The step 2) specifically includes following steps：
21) Lifting Wavelet is constructed, odd even sampling is carried out to the electrocardiosignal of observation and is indicated with multiphase method；
22) multilayer lifting wavelet transform is carried out, and High-frequency Interference is removed using threshold method in every layer of lifting wavelet transform
Wavelet coefficient after carry out inverse transformation；
23) the electrocardiosignal sequence after removal noise is obtained.
In the step 21), the FIR filter group { h, g } in Lifting Wavelet,Concrete form be：
Wherein, P (z),Respectively synthesis filter group { h, g }, analysis filter groupPolyphase representation, ho
(z) and he(z) polyphase representation for being low-pass filter h, go(z) and ge(z) polyphase representation for being high-pass filter g,WithFor low-pass filterPolyphase representation,WithFor high-pass filterPolyphase representation.
The FIR filter group { h, g },Meet the following conditions：
Described 22) in, the thresholding functions using the Garrote functions of hyperbolic form as threshold method, i.e.,：
Wherein, dj,kFor the original wavelet coefficients under kth layer j scales,To estimate that wavelet coefficient, j are the scale decomposed,
λjFor threshold value, k is the number of plies, and σ estimates for noise bias, and length (X) is the length after the z-transform of electrocardiosignal.
The step 3) specifically includes following steps：
31) use Lifting Wavelet to electrocardiosignal sequence f (n), the n=1,2 after denoising ..., N carries out tower point of 4 scales
Solution, obtains profile signal s and detail signal dj(j=1,2,3,4)；
32) positive maximum point and negative maximum point on scale j=3 are obtained, positive maximum point is wavelet systems
Point of the slope by switching to 0 more than 0 or less than 0 in the positive ascending branch of number, negative maximum point are that wavelet coefficient is born in decent
Point of the slope by switching to 0 less than 0 or more than 0；
33) the positive and negative maximum point of dynamic threshold process is used, when positive maximum point is more than threshold value S1, negative maximum is less than
Threshold value S2When retain, otherwise remove the extreme point；
34) independent and redundancy extreme point is eliminated；
35) slope criterion is used to eliminate pseudo- extreme point, for the positive and negative maximum pair of identification, if its slope is less than slope threshold
Value S, then it is assumed that be noise, remove this to extreme point；
36) ask positive and negative extreme value to zero crossing, i.e. R crest values point.
In the step 33), threshold value S1And S2Calculating formula be：
Wherein, M, N are respectively d3In maximum value and minimum value, A1、A2Respectively d1And d2Positive maximum average value
With the average value of negative maximum.
In the step 35), slope threshold value S is the average value of first three section of QRS wave slope.
Compared with prior art, the present invention has the following advantages：
One, complexity is reduced：Wavelet transformation therein is replaced using lifting wavelet transform, reduces the complexity of algorithm,
Reduce demand of the algorithm for memory, improves the real-time of algorithm；
Two, signal-to-noise ratio, processing are improved accurately：The defect for overcoming soft-threshold and hard threshold function is believed after improving denoising
Number signal-to-noise ratio, overcome waveform after saltus step of the hard threshold method at threshold value leads to denoising and introduce extra concussion, work as coefficient
When much larger than threshold value, treated, and coefficient approximation is constant, is not in that there are constant deviations to lead to weight for coefficient after soft-threshold is handled
The inaccurate problem of structure signal.
Description of the drawings
Fig. 1 is the polyphase representation of Lifting Wavelet in the present invention.
Fig. 2 is that lifting scheme wavelet structure converts decomposition diagram in the present invention.
Fig. 3 is that lifting scheme constructs inverse wavelet transform decomposition diagram in the present invention.
Specific implementation mode
The present invention is described in detail with specific embodiment below in conjunction with the accompanying drawings.
Embodiment：
(1) wavelet basis is selected, the realization of its Via Lifting Scheme is constructed；
(2) 4 layers of lifting wavelet transform are carried out to observation signal X, ensure that the wavelet coefficient of High-frequency Interference is handled by threshold method,
Realize SNR estimation and compensation；
(3) each layer wavelet coefficient being handled by formula (1), threshold value uses fixed threshold form,
Wherein, σ estimates for noise bias, and X is the result after the z-transform of signal.
(4) to treated, signal carries out signal f (n), n=1 after Lifting Wavelet inverse transformation obtains removal noise,
2 ..., N, N are signal sequence length.
By the research to ECG Signal Filtering Algorithm it is found that electrocardiosignal low frequency components P waves, T waves and baseline drift
Mainly on larger scale (scale j ＞ 4), QRS complex has accumulated 98% energy, wave crest within the scope of 0~38Hz
It is concentrated mainly on 10~20Hz, centre frequency is in 17Hz or so, therefore the energy of the ingredient of QRS wave is concentrated mainly on wavelet transformation
On j=3~j=4 scales after decomposition, and on j=3 scales, smaller by high-frequency noise interference, wave-shape amplitude is larger, most
It is adapted to carry out QRS wave shape identification.The present invention is according to the relationship between modulus maximum and Signal Singularity, using lifting scheme
Wavelet structure becomes the complexity for bringing and reducing algorithm, improves the algorithm speed of service.
Following steps are specifically included using the transformation of lifting scheme wavelet structure：
First, ECG Signal Filtering Algorithm is given for the deficiency of existing algorithm, in conjunction with wavelet function feedback algorithm
To filter out the noise in electrocardiosignal.The present invention replaces wavelet transformation therein using lifting wavelet transform.Signal X={ xk}
Z-transform be defined as：
Only retain odd number or even number of samples point, is with multiphase method representation：
FIR filter h={ hk1,...hk2Z-transform be defined as (Laurent multinomials)：
It is expressed as using multinomial：H (z)=he(z2)+z-1ho(z2).Schematic diagram as shown in Figure 1, wherein P (z),Respectively
Filter group { h, g } is represented,Polyphase representation, concrete form is：
Using polyphase matrix form come indicate the perfect reconstruction filter bank of wavelet transformation for：
And P (z),Concrete composition element be Laurent multinomials, it is therefore necessary to meet
DetP (z)=czk (7)
Definition：One filter group { h, g } is complementary, if corresponding polyphase representation matrix meets detP (z)=1.
If { h, g } is complementary, then any filter with h complementations can be expressed as：
gnew(z)=g (z)+h (z) s (z2) (8)
Wherein, s (z) is Laurent multinomials.Otherwise it also sets up, that is, meets gnewWith h complementations.Correspondingly, analysis filtering
Device part can obtain,
If { h, g } is complementary, then any filter with g complementations is represented by,
hnew(z)=h (z)+g (z) t (z2) (10)
Wherein, t (z) is Laurent multinomials.If { h, g } is complementary, then there is one group of Laurent multinomials si(z),
ti(z) (1≤i≤m) and non-zero constant K make P (z) meet
Based on above-mentioned theorem, converted with lifting scheme wavelet structure, the schematic diagram of inverse wavelet transform is respectively Fig. 2, Fig. 3 institutes
Show.
Due to the difference of individual, the identification of R crest value points is it is possible that more inspections or missing inspection.According to human ecg signal
Feature, QRS wave width are no more than 0.22 second, so the interval between R-R peak points should be less than 80, otherwise should reject the peak value
Point.In addition, when algorithm recognizes 1.5 times of periods of R -- R interval and does not find next maximum point, it should reduce original
Recognition threshold re-recognizes the segment data, prevents missing inspection.
Improved R crest values point recognizer flow is as follows:
(1) use the lifting scheme of Traditional Wavelet to electrocardiosignal sequence f (n), n=1,2 ..., it is tower that N carries out 4 scales
It decomposes, obtains profile signal s and detail signal dj(j=1,2,3,4)；
(2) the positive and negative maximum point on scale j=3 is found out.In the positive ascending branch of positive maximum point, i.e. wavelet coefficient tiltedly
Point of the rate by switching to 0 more than 0 or less than 0；Negative maximum point, i.e. wavelet coefficient bear decent in slope by be less than 0 switch to 0 or
Point more than 0.
(3) the positive and negative maximum point of dynamic threshold process is used.Positive maximum point is greater than threshold value S1, negative maximum
It is less than threshold value S2, otherwise remove the extreme point.Threshold calculations formula is:
Wherein M, N are respectively d3In maximum value and minimum value, A1,A2The average value of respectively preceding 2 sections of positive maximum and
The average value of negative maximum.
(4) independent and redundancy extreme point is eliminated.
(5) slope criterion is used to eliminate pseudo- extreme point.For the positive and negative maximum pair of identification, if its slope k<Slope
Threshold value S, then it is assumed that noise removes this to extreme point.Slope k is calculated as follows:
Wherein max, min are positive and negative maximum, and m, n are the position where it, and threshold value S is taken as first three section of QRS wave slope
Average value.
(6) ask positive and negative extreme value to zero crossing, i.e. R wave crests.
Claims (8)
1. a kind of based on the QRS wave recognition methods for improving wavelet transformation, which is characterized in that include the following steps：
1) electrocardiosignal to be identified is obtained；
2) it uses Lifting Wavelet to carry out time-frequency domain conversation to electrocardiosignal, and the high frequency in electrocardiosignal is filtered out using threshold method
Noise；
3) QRS complex recognizer is used to obtain R crest value point locations.
2. according to claim 1 a kind of based on the QRS wave recognition methods for improving wavelet transformation, which is characterized in that described
Step 2) specifically include following steps：
21) Lifting Wavelet is constructed, odd even sampling is carried out to the electrocardiosignal of observation and is indicated with multiphase method；
22) multilayer lifting wavelet transform is carried out, and using the small of threshold method removal High-frequency Interference in every layer of lifting wavelet transform
Inverse transformation is carried out after wave system number；
23) the electrocardiosignal sequence after removal noise is obtained.
3. according to claim 2 a kind of based on the QRS wave recognition methods for improving wavelet transformation, which is characterized in that described
Step 21) in, the FIR filter group { h, g } in Lifting Wavelet,Concrete form be：
Wherein, P (z),Respectively synthesis filter group { h, g }, analysis filter groupPolyphase representation, ho(z) and
he(z) polyphase representation for being low-pass filter h, go(z) and ge(z) polyphase representation for being high-pass filter g,WithFor
Low-pass filterPolyphase representation,WithFor high-pass filterPolyphase representation.
4. according to claim 3 a kind of based on the QRS wave recognition methods for improving wavelet transformation, which is characterized in that described
FIR filter group { h, g },Meet the following conditions：
5. according to claim 2 a kind of based on the QRS wave recognition methods for improving wavelet transformation, which is characterized in that described
22) in, the thresholding functions using the Garrote functions of hyperbolic form as threshold method, i.e.,：
Wherein, dj,kFor the original wavelet coefficients under kth layer j scales,To estimate that wavelet coefficient, j are the scale decomposed, λjFor
Threshold value, k are the number of plies, and σ estimates for noise bias, and length (X) is the length after the z-transform of electrocardiosignal.
6. according to claim 1 a kind of based on the QRS wave recognition methods for improving wavelet transformation, which is characterized in that described
Step 3) specifically include following steps：
31) use Lifting Wavelet to after denoising electrocardiosignal sequence f (n), n=1,2 ..., N carry out 4 scale QMF compressions,
Obtain profile signal s and detail signal dj(j=1,2,3,4)；
32) obtain positive maximum point and negative maximum point on scale j=3, positive maximum point be wavelet coefficient just
Point of the slope by switching to 0 more than 0 or less than 0 in ascending branch, negative maximum point are that wavelet coefficient bears slope in decent
Point by switching to 0 less than 0 or more than 0；
33) the positive and negative maximum point of dynamic threshold process is used, when positive maximum point is more than threshold value S1, negative maximum is less than threshold value
S2When retain, otherwise remove the extreme point；
34) independent and redundancy extreme point is eliminated；
35) slope criterion is used to eliminate pseudo- extreme point, for the positive and negative maximum pair of identification, if its slope is less than slope threshold value S,
Then it is considered noise, removes this to extreme point；
36) ask positive and negative extreme value to zero crossing, i.e. R crest values point.
7. according to claim 6 a kind of based on the QRS wave recognition methods for improving wavelet transformation, which is characterized in that described
Step 33) in, threshold value S1And S2Calculating formula be：
Wherein, M, N are respectively d3In maximum value and minimum value, A1、A2Respectively d1And d2The average value of positive maximum and negative
The average value of maximum.
8. according to claim 6 a kind of based on the QRS wave recognition methods for improving wavelet transformation, which is characterized in that described
Step 35) in, slope threshold value S be first three section of QRS wave slope average value.
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Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109820501A (en) * | 2018-11-12 | 2019-05-31 | 浙江清华柔性电子技术研究院 | A kind of recognition methods of R wave of electrocardiosignal, device, computer equipment |
| CN110916645A (en) * | 2019-12-10 | 2020-03-27 | 电子科技大学 | QRS wave identification method combining wavelet transformation and image segmentation network |
| CN111616697A (en) * | 2020-06-05 | 2020-09-04 | 江苏科技大学 | Electrocardiosignal denoising algorithm based on new threshold function wavelet transform |
| CN111920407A (en) * | 2020-07-27 | 2020-11-13 | 广东省医疗器械研究所 | Electrocardio feature extraction method, system, device and medium based on wavelet transformation |
| CN112107310A (en) * | 2020-09-30 | 2020-12-22 | 西安理工大学 | ECG identity recognition method based on IWT and AGA-BP models |
| CN112257518A (en) * | 2020-09-30 | 2021-01-22 | 西安交通大学第二附属医院 | ECG identity recognition method based on WT and WOA-PNN algorithm |
| CN112353397A (en) * | 2020-11-17 | 2021-02-12 | 西安理工大学 | Electrocardiogram signal identity recognition method |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101268936A (en) * | 2008-02-27 | 2008-09-24 | 天津大学 | Electrocardio-compression method and decrypting method of wireless cardiogram monitor |
| CN102240208A (en) * | 2010-05-11 | 2011-11-16 | 南京医科大学第一附属医院 | Electrocardiosignal denoising wavelet algorithm implementable in single chip microcomputer |
| 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 |
| CN103156599A (en) * | 2013-04-03 | 2013-06-19 | 河北大学 | Detection method of electrocardiosignal R characteristic waves |
| CN103750835A (en) * | 2013-11-12 | 2014-04-30 | 天津工业大学 | Electrocardiosignal characteristic detection algorithm |
| CN104352234A (en) * | 2014-10-27 | 2015-02-18 | 中国科学院半导体研究所 | Detection method for peak singular point of physiologic electric signal |
| CN105212922A (en) * | 2014-06-11 | 2016-01-06 | 吉林大学 | The method and system that R wave of electrocardiosignal detects automatically are realized towards FPGA |
-
2018
- 2018-02-11 CN CN201810141472.7A patent/CN108272451B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101268936A (en) * | 2008-02-27 | 2008-09-24 | 天津大学 | Electrocardio-compression method and decrypting method of wireless cardiogram monitor |
| CN102240208A (en) * | 2010-05-11 | 2011-11-16 | 南京医科大学第一附属医院 | Electrocardiosignal denoising wavelet algorithm implementable in single chip microcomputer |
| 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 |
| CN103156599A (en) * | 2013-04-03 | 2013-06-19 | 河北大学 | Detection method of electrocardiosignal R characteristic waves |
| CN103750835A (en) * | 2013-11-12 | 2014-04-30 | 天津工业大学 | Electrocardiosignal characteristic detection algorithm |
| CN105212922A (en) * | 2014-06-11 | 2016-01-06 | 吉林大学 | The method and system that R wave of electrocardiosignal detects automatically are realized towards FPGA |
| CN104352234A (en) * | 2014-10-27 | 2015-02-18 | 中国科学院半导体研究所 | Detection method for peak singular point of physiologic electric signal |
Non-Patent Citations (4)
| Title |
|---|
| 刘雄飞 等: "基于提升小波变换和多种策略的QRS波检测算法", 《中国医学物理学杂志》 * |
| 岑小林 等: "基于Matlab的小波提升与心电信号R波检测", 《数字技术与应用》 * |
| 王小飞 等: "基于提升小波和改进包络的心电特征检测算法", 《系统仿真学报》 * |
| 郭显久 等: "提升小波变换及其在信号去噪中的应用", 《大连水产学院学报》 * |
Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109820501A (en) * | 2018-11-12 | 2019-05-31 | 浙江清华柔性电子技术研究院 | A kind of recognition methods of R wave of electrocardiosignal, device, computer equipment |
| CN109820501B (en) * | 2018-11-12 | 2023-11-28 | 浙江清华柔性电子技术研究院 | Electrocardiosignal R wave identification method and device and computer equipment |
| CN110916645A (en) * | 2019-12-10 | 2020-03-27 | 电子科技大学 | QRS wave identification method combining wavelet transformation and image segmentation network |
| CN111616697A (en) * | 2020-06-05 | 2020-09-04 | 江苏科技大学 | Electrocardiosignal denoising algorithm based on new threshold function wavelet transform |
| CN111616697B (en) * | 2020-06-05 | 2022-07-08 | 江苏科技大学 | Electrocardiosignal denoising algorithm based on new threshold function wavelet transform |
| CN111920407A (en) * | 2020-07-27 | 2020-11-13 | 广东省医疗器械研究所 | Electrocardio feature extraction method, system, device and medium based on wavelet transformation |
| CN111920407B (en) * | 2020-07-27 | 2023-09-22 | 广东省医疗器械研究所 | Electrocardiogram feature extraction method, system, device and medium based on wavelet transformation |
| CN112107310A (en) * | 2020-09-30 | 2020-12-22 | 西安理工大学 | ECG identity recognition method based on IWT and AGA-BP models |
| CN112257518A (en) * | 2020-09-30 | 2021-01-22 | 西安交通大学第二附属医院 | ECG identity recognition method based on WT and WOA-PNN algorithm |
| CN112353397A (en) * | 2020-11-17 | 2021-02-12 | 西安理工大学 | Electrocardiogram signal identity recognition method |
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