CN105286857A - R wave rapid detection method adaptive to electrocardiogram waveform pathological change - Google Patents
R wave rapid detection method adaptive to electrocardiogram waveform pathological change Download PDFInfo
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Abstract
The invention discloses an R wave rapid detection algorithm adaptive to electrocardiogram waveform pathological change. The method, by summarizing the different characteristics of various pathological electrocardiograms such as arrhythmia, reverse wave, W wave, tall peaked P wave, tall peaked T wave and the like on an electrocardiogram signal first-order derivative and a first-order derivative square signal, can overcome the limitation of a conventional difference threshold algorithm on setting a plurality of thresholds and avoid the influence on self-adaption threshold detection due to relatively high heart rate variability among different patients through such strategies as low-threshold return-to-zero treatment, R wave classification detection as well as threshold judgment and updating for a non-classical R waveform and the like. According to the method, the algorithm is simple and easy to implement, and simultaneously the algorithm is capable of achieving rapid and accurate R wave detection on the various pathological electrocardiograms; and the algorithm is especially suitable for real-time QRS wave detection on electrocardiogram signals in mobile portable equipment. The algorithm disclosed by the invention, inspected by virtue of an MIT-BIH database, is 99.71% in sensitivity and is 99.73% in positive predictive value.
Description
Technical field
The present invention relates to a kind of monitoring technology, namely a kind of R ripple method for quick adapting to ecg wave form pathological change, is applied to ECG detecting association area.
Background technology
Cardiovascular disease is the number one killer of human health, and its prevention and diagnosis are the major issues now faced by medical circle needs.Electrocardiogram (Electrocardiogram, ECG or EKG) is that the electrical activity utilizing electrocardiograph to produce from each cardiac cycle of body surface record heart changes the figure produced.The electrocardiogram of standard mainly comprises the wave characters (as figure mono-) such as P ripple, QRS complex wave, T ripple and U ripple, these mark sheets understand the generation of cardiac electric excitement, propagation and recovery process, their position, the index such as shape and interval are the strong indicator detecting various heart disease clinically.R crest is the most important and significant wave character in whole electrocardiogram, represents the depolarization moment of ventricle, and waveform has slope the most precipitous in whole cardiac cycle and higher amplitude.Simultaneously, R crest restrains the maximum position of rate of change as electrocardiosignal list breath-group, can be used as the basic point of all the other waveforms location after identifying, and obtain the important parameters such as RR interval, heart rate, heart rate variability by detecting R crest and then research and application is carried out to the every disease of patient.Therefore, the detection of R crest has important clinical meaning in ECG signal sampling, has become basis and the foundation of the location algorithm of each waveform in electrocardiosignal in modern electrocardiosignal research.
In recent years, along with the very fast development of embedded medical equipment and the information processing technology, novel real-time portability electrocardio monitoring starts to grow up gradually with automatization's medical diagnosis on disease equipment, it has, and volume is little, wearable, simple to operate, with low cost, correctness high, family, small-middle hospital and community medical therapy unit can be commonly used to, and then easily condition-inference, disease forecasting etc. be carried out to patient.These portable and wearable devices are had higher requirement to R crest detection algorithm, not only want accuracy high, and want algorithm relatively simple, occupying system resources is little, and the speed of service is fast simultaneously.
Current R wave crest of electrocardiosignal context of detection algorithm is numerous, as: difference threshold algorithm, Wavelet Transform, template matching method, length and energy conversion and neutral net etc.In the face of noninvasive electrocardiosignal noise jamming is large, weak output signal and the strong again feature of variability, each method has its good and bad point.Conventional differential threshold method is easy, is easy to realize, and processing speed is very fast simultaneously; Wavelet Transform has good time frequency localization characteristic, and accuracy in detection is high, but data amount of calculation in its processing procedure is large, is not suitable for real-time process; Template matching method principle is simple, but its to high-frequency noise and baseline drift very responsive; The differentiation effect of neural net method is better, but its training time is longer, and real-time is poor.The angle little from occupying system resources and the speed of service is fast, differential threshold algorithm has greater advantage.But conventional differential threshold method is too simple, there is certain limitation.People have made a large amount of improvement to conventional differential threshold method in recent years, and the most outstanding improvement sets up decision condition, adaptive threshold and backtracking heavily to examine, and considerably increase the accuracy that differential threshold method detects.But various heart disease can cause electrocardiogram that various types of pathologic wave deformation (as shown in Figure 2) occurs, the difference threshold algorithm improved is only applicable to comparatively normal waveform, situation treatment effects such as (such as positive wave and the ripple that falls alternately occur) is significantly changed for the various pathological change of electrocardiosignal poor as the arrhythmia in Fig. 2, R Wave pattern, the threshold value renewal of redundancy simultaneously calculates judgement and the operational efficiency reducing algorithm is heavily examined in various backtracking, masks differential threshold method advantage originally.Therefore, we have proposed the thinking of carrying out discriminant classification on the basis of difference threshold algorithm for the feature of the ill waveform of R ripple typical case, reduce the setting of threshold value simultaneously, accurately can locate the R point of dissimilar electrocardiosignal while making algorithm manage speed aloft, thus solve various typical pathologic waveform brings in Fig. 2 false retrieval, the problem such as undetected.This algorithm is simply easy to quick realization, and occupying system resources is few, processes degree of accuracy higher simultaneously, can adapt to the change of multiple pathology ecg wave form, therefore be suitable for the process of ECG signal on novel Wearable, portable set.
Summary of the invention
The present invention is not suitable with mainly for conventional differential threshold method the feature that ecg wave form pathological change causes accuracy rate to reduce, based on the different characteristic performance of dissimilar pathology ecg wave form on one jump sub-signal and first-order difference quadrature signal, propose a kind of method that can detect multiclass pathology ECG R wave rapidly and accurately.This algorithm is simply easy to realize, and calculation process speed is fast and can adapt to the changes such as most of pathology ecg wave form and arrhythmia, and Detection accuracy is high.
It is to detect the method for multiclass pathology ECG R wave fast that the present invention realizes the technical scheme that above-mentioned purpose takes, and mainly comprises the steps:
(1) Filtering Processing is carried out to primary signal, remove baseline drift and higher-order noise and Hz noise;
(2) signal characteristic abstraction and strengthening: first first-order difference is carried out to the signal after processing and square, expose the variation characteristic of signal and the signal that difference changes is attributed to same dimension and compares, subsequently to square after signal make Low threshold part return-to-zero, retain the signal that rate of change is large, outstanding signal characteristic;
(3) by comparing the power of signal intensity degree after process, judge the correctness of selected scope;
(4) judge that R ripple in-scope and R ripple choose mode according to the crest number of signal in range of signal described in (3), consider the change of patient R wave characteristic be divided into normal variation rate and little rate of change two greatly class detect respectively;
(5) first-order difference signal is revert to, in R ripple span by compare sequencing that its maximum rate of change judges that waveform changes with determine detection R ripple type of waveform so that determine detection mode, reduce the selection range of R ripple further, finally accurate R ripple position, location on signal after the filtering simultaneously;
(6) upgrade threshold value, continue to detect;
Beneficial effect of the present invention is: (1) reduces the various restrictions of conventional differential threshold method to height, only Low threshold special handling is carried out to first derivative quadrature signal, method is simple and efficient, and in eliminating, generally high-frequency noise and most of T ripple P greatly remain the feature of R ripple while ripple by a narrow margin; (2) take into full account the diversity of different patient's ECG R wave shape, determine that R ripple type carries out classification and Detection according to the variation characteristic of R waveform, the accurate detection of polymorphic type pathology QRS waveform can be realized; (3) limitation of the setting of multiple temporal capacity-threshold in traditional algorithm is considered and the variation of different patient heart rate is larger adaptive threshold is detected to the impact brought, reduce while interpolation rate of change threshold value compares reservation algorithm accuracy of detection as far as possible and reduce threshold value, especially the setting of adaptive threshold, preventing ARR well while, greatly improve its operational efficiency, reduce false retrieval and the undetected impact on subsequent detection.
Accompanying drawing explanation
Fig. 1 is normal electrocardiosignal form;
Fig. 2 is the various pathological change waveforms of electrocardiosignal;
Fig. 3 is the signal aspect before and after filtering;
Fig. 4, for working as waveform generation pause and transition in rhythm or melody, changes the testing result of this algorithm when reducing;
Fig. 5 ought occur that W type ripple and waveform become suddenly the testing result of this algorithm in large situation simultaneously;
Fig. 6 is the testing result of this algorithm under the positive wave alternating senses of falling ripple in waveform;
Fig. 7 be ecg-r wave entirely for fall ripple when this algorithm testing result;
Fig. 8 is the testing result of this algorithm when there is arrhythmia;
Fig. 9 is the testing result of MIT-BIH data base.
Figure 10 is the testing result of other 6 data bases.
Detailed description of the invention
The present invention is described in further detail in conjunction with the accompanying drawings and embodiments.
The present invention can obtain the R ripple position of electrocardiosignal as follows in real time:
(1) Filtering Processing is carried out to the primary signal collected, wave filter used is second order Butterworth band filter, choosing cut-off frequency is 3 ~ 25HZ, and remove baseline drift and process higher-order noise and Hz noise to a certain extent, filter effect is as Fig. 3.
(2) signal characteristic abstraction and strengthening:
First first-order difference is carried out to the signal after process and square, expose the variation characteristic of signal and the signal that difference changes be attributed to same dimension: by the first-order difference of electrocardiosignal after the filtering of forward difference formulae discovery, formula used is
Δx
n＝x
n+1-x
n＝x′(ξ),(x
n＜ξ＜x
n+1)
By the differential signal Δ x obtained
nwork square obtains y
n, formula used is:
y
n＝Δx
n 2
Subsequently to square after signal make Low threshold part return-to-zero, retain the large signal of rate of change, outstanding signal characteristic: the difference quadrature signal y getting every 10 ~ 30s
nfirst three second, make meansigma methods, m is the number of the signaling point of 3s, and formula used is:
Consider that patient's electrocardiosignal can change in time, after change, former average no longer adapts to new electrocardiosignal, so the every 10 ~ 30s of this average upgrades once, as first derivative quadrature signal y
nwhen being less than G1*average, return-to-zero being done to solve the less high-frequency noise of some threshold values to give prominence to signal characteristic further to it, obtains new characteristic signal y
n'.Compare threshold G1 gets between 1.2 ~ 8.Consider the problem that process noise is larger in real time, G1 can adjust within the scope of this.
(3) by comparing the power of signal intensity degree after process, whether judge in the selected scope of institute containing R ripple;
Signal y after process
n' remaining the larger signal of rate of change, whether correctly can judge selected scope according to the maximum rate of change feature of R ripple: when signal y being detected
n' >0 time, respectively find y in two adjacent behind this position respectively regions
n' maximum M1, M2 also compares, if G2*M1>M2, then show that near this position, its change rate signal is the maximum in neighbouring certain limit, meet the feature that R ripple rate of change is maximum, namely determine, containing a R ripple, to perform step (4).According to the potential change feature that human heart is beated, the span in adjacent two regions is respectively between 0 ~ 0.4s and 0.2 ~ 0.5s, and the value of compare threshold G2 is between 0.8 ~ 2.According to the data global feature of MIT-BIH data base, best region span is 0 ~ 0.25s and 0.25 ~ 0.35s.Because the cardiac electrophysiology feature of different people is different, this parameter value can do appropriateness adjustment.
(4) according to signal y in certain limit
n' crest number judge R ripple in-scope:
After decision signal meets condition described in (3), in selected region span, find signal y
n' crest number p.
Crest decision condition: the maximum of signal absolute change rate among a small circle.The method choosing crest is herein that the value at this place is greater than one, front and back point, and with its before and after the difference of numerical value of the 3rd point be not less than the difference of the numerical value of itself and 1 point in front and back.
Due to R ripple, to there is rate of change high, normal its y of R ripple
n' near at least should there are two crests representing its rising edge and trailing edge respectively, if R ripple reduces suddenly, then may there is crest, i.e. a p>0 only representing its rising edge rate of change or trailing edge rate of change in it.
The determination of R ripple in-scope:
As p>1, judge to there is R ripple between first crest and last crest.
As p=1, judged by the size of the time difference and threshold value RR that compare this peak and previous R ripple:
Owing to can not determine that this ripple is P ripple or the T ripple of R ripple or pathologic high rate of change, introduce adaptive threshold RR to judge: threshold value RR is similar to average heart rate, can upgrade after obtaining new R ripple, when its time difference is greater than 0.65RR, judge to there is R ripple in certain limit around this crest.
(5) the most value by signal first derivative in R ripple span judges that R ripple span and R ripple choose mode further, signal after the filtering finally determines R ripple position simultaneously:
Be divided into p>1 and p=1 and normal R ripple and the less R ripple of rate of change two kinds of situations:
As p>1, this R ripple is the R ripple of a normal variation rate, and the appearance position according to positive change maximum judges: in the determinating area obtained, find first derivative signal Δ x
nminima Δ x
min, then before and after it, find Δ x in certain limit respectively
nmaximum Δ x
max1with Δ x
max2and comparing, if Δ x
max2<G3* Δ x
max1, then original signal x is got
nin at Δ x
max1with Δ x
minposition between maximum x
maxas R ripple position (positive wave), no then gets x
nin at Δ x
minwith Δ x
max2position between minima x
minas R ripple position (ripple).The span of compare threshold G3 is between 0.7 ~ 3.
As p=1, this R ripple is the R ripple of a little rate of change, judges according to the qualitative change of maximum rate of change and crest size: find signal x respectively simultaneously
nin before and after this crest correspondence position maximum x in 0.15s
maxwith minima x
minand judging, if large this crest location i.e. of its positive change rate corresponding first-order difference Δ x
nnumerical value be greater than 0 and x
maxvalue be not less than 0.3 ~ 0.6 times of a R crest numerical value, then get this x
maxfor new R crest, no gets x
minfor new R crest, its position is designated as R
n.
(6) upgrade threshold value, continue to detect:
After a new R ripple finds, calculate its heart rate, upgrade RR simultaneously, the judgement for R ripple less during p=1:
When up-to-date R peak separation is greater than 0.6*RR and is less than 2.5*RR, judge that its spacing is comparatively normal, upgrade threshold value, formula is:
RR＝0.6RR+0.4(R
n-R
n-1)
Often detect that namely a R ripple upgrades threshold value RR, ensure the evenness of this value, threshold value calculation method and new spacing only account for 0.4 times of total value simultaneously, and reduction false retrieval and abnormal heart beating are on the impact of subsequent detection.
Because electrocardiosignal exists the refractory stage of certain hour, consider that arrhythmia sets the impact brought on threshold value simultaneously, continue the 0.3s after abandoning detected R ripple when detecting, after 0.3s, repeat step (3) ~ (5), continue the detection carrying out R ripple backward.
The feature that this method is first larger according to the rate of change of R ripple in electrocardiosignal, gets the quadrature signal after its first derivative and process as choosing and judging the method for R ripple.Because its electrocardiosignal feature of dissimilar patient has different difference, though modern differential threshold method makes improvement to the fixed threshold in traditional method, define adaptive threshold, but still there is certain limitation, be difficult to the accuracy ensureing changeableization waveforms detection.For reducing the setting of multiple height decision threshold to detecting the limitation caused, first make the outstanding signal intensity feature of square process and its signal intensity be attributed to same dimension to compare to first derivative, get the meansigma methods of every first three second of certain time subsequently, return-to-zero is made to lower than certain multiple average, remove some amplitudes and the less P ripple of rate of change, T ripple and noise, signal large for rate of change is come out, avoids various different self adaptation height threshold in traditional method and carry out processing and the problem judging to bring.Because the setting of zero threshold value is lower, the QRS that well prevent QRS wave amplitude and the less electrocardiosignal (signal similar with Fig. 2 (m), pathological factor causes QRS ripple slope variation rate to decline) of rate of change is undetected.The problem that setting is heavily examined in various backtrackings in traditional method is, because the QRS wave characteristic of pathology ecg wave form meets set condition not, backtracking is heavily examined and is also difficult to detect.There is the problem (as Fig. 2 (h)) that larger change makes its QRS ripple diminish gradually or increases in the electrocardiosignal simultaneously for solving same patient, the every 10 ~ 30s of this method setting upgrades an average threshold value.This establishing method is simply effective, and processing speed is fast.Treatment effect as shown in Figure 4.
In the process detecting R ripple, first the judgement in region is carried out, when detecting that the quadrature signal after process is greater than 0, maximum M1, M2 of getting first derivative within the scope of two square compare, if meet set condition, illustrate that the rate of change of the original ripple in set scope is maximum in certain hour region, meet the wave characteristics of the maximum rate of change of QRS ripple, eliminate large P ripple and noise that R wavefront may exist, efficiently solve high sharp P ripple and wave filter in Fig. 2 (d), (g) and be difficult to the problem of the high value noise of filtering.
Behind the region determining QRS ripple, be 2 classes by R wavelength-division, one class is the situation of the smaller i.e. p=1 of R ripple rate of change: the maximin found near the original filtration signal of p=1 position judges that R ripple is as normal R ripple or inverted R ripple further, if normal R ripple, then get maximum in scope, not no as the minima in inverted R ripple detection range, being tall peaked T wave for preventing its false retrieval simultaneously, adding threshold value RR and when setting p=1, itself and the previous R pitch of waves detect from just can be used as R ripple during certain multiplying power more than RR interval.Another kind of is the R ripple of GPS survey rate of change and the situation of p>1, feature according to differential signal judges: if examine R ripple as normal R ripple, then the negative sense change subsequently of the inevitable first positive change of its signal, shows that first-order difference signal is forward crest appears at the front end of swinging to crest; Be inverted R ripple then completely contrary, the first negative sense of its signal changes then positive change, show that first-order difference signal being forward crest appears at and swing to crest rear end, so undertaken judging just swinging to of R ripple by the maximum before and after differential signal minima in comparison range.The method considers the waveform of the decentraction signal of telecommunication, especially detect in Fig. 2 (g) and be inverted QRS ripple completely, positive wave falls ripple alternately and Fig. 2 (a), partial inversion QRS ripple in (i) in Fig. 2 (l), makes the detection of R ripple more accurate.Be out of shape, because the maximum rate of change feature of its R ripple still retains, so still can be detected as W shape ripple in Fig. 2 (k), (f) and Fig. 2 (b), (c) middle R ripple.Testing result is as shown in Fig. 5, Fig. 6, Fig. 7.
In threshold determination, be the problems such as the arrhythmia that prevention patient is dissimilar, reduce the limitation of multiple threshold value setting, improve operational efficiency simultaneously, the present invention reduces threshold value, the especially setting of adaptive threshold in judgement R wave process as far as possible.First, in subsequent detection, do not set various self adaptation height threshold, after process being detected, signal is greater than 0 is secondly strictly do not limit the distance between R ripple in testing process, being only R ripple to prevent tall peaked T wave false retrieval in p=1 situation, setting the judgement of a threshold value RR as R ripple.Undetected as preventing wider R ripple from occurring when R ripple judges subsequently, will judge that the time range of crest number is set to wider 0.25s detecting that first derivative quadrature signal is greater than after 0.Consider the problem of refractory stage simultaneously, in the 0.3s after R ripple being detected, do not remake the detection of R ripple, effectively prevent the false retrieval of the tall peaked T wave of similar Fig. 2 (d) (f).Threshold value RR increases with the R wave number detected and constantly updates, and when the R ripple newly measured and the previous R pitch of waves are from upgrading threshold value RR when meeting certain limit, thus ensures the reasonability of its size.The low quantity of threshold value setting and low limit also effectively ensure that the R ripple that rhythm of the heart variation oscillogram 2 (e) two connects rate and the serious ARR QRS waveform of Fig. 2 (j) correctly detects.Testing result as shown in Figure 8.
This algorithm considers the decentraction electrical waveform of different patient, accurately can detect multiple QRS waveform, improve the accuracy of detection, solve the unicity that conventional differential threshold method detects QRS ripple, reduce the limitation of its threshold value setting, heart rate is calculated more accurate, while method is simple, detection rates is fast, is applicable to the Real-Time Monitoring of QRS waveform.The method realizes on 6 data bases such as MIT-BIH data base and QT, and accuracy rate is as shown in Fig. 9,10, all higher.Fig. 9 is the accuracy of MIT-BIH data base.Figure 10 is the testing result of other 6 data bases.
The foregoing is only preferred embodiment of the present invention, not in order to limit the present invention, all any amendments done within the spirit and principles in the present invention, equivalent replacement and improvement etc., all should be included within protection scope of the present invention.
Claims (8)
1. can adapt to a R ripple method for quick for ecg wave form pathological change, it is characterized in that, comprise the steps:
(1) Filtering Processing is carried out to primary signal, remove baseline drift and higher-order noise and Hz noise;
(2) signal characteristic abstraction and strengthening: first first-order difference is carried out to the signal after processing and square, expose the variation characteristic of signal and the signal that difference changes is attributed to same dimension and compares, subsequently to square after signal make Low threshold part return-to-zero, retain the signal that rate of change is large, outstanding signal characteristic;
(3) by comparing the power of signal intensity degree after process, judge the correctness of selected scope;
(4) judge that R ripple in-scope and R ripple choose mode according to the crest number of signal in range of signal described in (3), consider the change of patient R wave characteristic be divided into normal variation rate and little rate of change two greatly class detect respectively;
(5) first-order difference signal is revert to, in R ripple span by compare sequencing that its maximum rate of change judges that waveform changes with determine detection R ripple waveform so that determine detection mode, reduce the selection range of R ripple further, finally accurate R ripple site, location on signal after the filtering simultaneously;
(6) upgrade threshold value, continue to detect.
2. signal characteristic abstraction according to claim 1 and strengthening, is characterized in that: described signal characteristic comes from the fast and large feature of R ripple place amplitude change rate, and the difference quadrature signal after utilizing differential signal and processing is to extract signal characteristic.
3. signal characteristic abstraction according to claim 1 and strengthening, is characterized in that: the threshold value of first derivative quadrature signal zero is chosen for the meansigma methods that G1 is multiplied by this signal of front end a period of time of every 15 ~ 30s, and the signal lower than this threshold value makes zero.
4. judgement according to claim 1 the method for selected scope correctness, it is characterized in that: the feature according to rate of change maximum within the scope of R ripple is worth the judgement carrying out selected scope correctness most by the rate of change within the scope of contrast adjacent time.
5. the R of determination ripple scope according to claim 1 and choose the method for R ripple, is characterized in that: vary in size according to the rate of change of R ripple is divided into the low change of Gao Bianyu in testing process: i.e. p>1 and p=1 two states.
6. the method choosing R ripple according to claim 1, is characterized in that: carry out positive wave according to R baud point and fall ripple two kinds of different modes get its determine in scope most value.
7. the R of determination ripple scope according to claim 1 and the method choosing R ripple, is characterized in that: the R ripple (high change) of normal variation rate and larger rate of change, carries out judging and detect according to the appearance position of positive change maximum; The R ripple (low change) of little rate of change, simultaneously carries out judging according to the qualitative change of maximum rate of change and crest size and detects.
8. electrocardiosignal R peak detection method according to claim 1, is characterized in that: the frequency of described sampling is 125 ~ 1000Hz, can be used for the detection of multi-lead electrocardiosignal.
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| CN109602414A (en) * | 2018-11-12 | 2019-04-12 | 安徽心之声医疗科技有限公司 | A kind of ecg signal data Enhancement Method of multi-angle of view conversion |
| CN109480826A (en) * | 2018-12-14 | 2019-03-19 | 东软集团股份有限公司 | A kind of ECG's data compression method, device and equipment |
| CN109480826B (en) * | 2018-12-14 | 2021-07-13 | 东软集团股份有限公司 | Electrocardiosignal processing method, device and equipment |
| CN111513706A (en) * | 2020-04-20 | 2020-08-11 | 重庆邮电大学 | Method and device for detecting electrocardiosignals containing abnormal R waves |
| CN111513706B (en) * | 2020-04-20 | 2022-09-27 | 重庆邮电大学 | Method and device for detecting electrocardiosignals containing abnormal R waves |
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| CN112200232B (en) * | 2020-09-29 | 2024-03-22 | 上海移视网络科技有限公司 | QRS (QRS) recognition method and electronic equipment |
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