CN104161510A - Multistage lead electrocardiograph signal QRS waveform identification method - Google Patents
Multistage lead electrocardiograph signal QRS waveform identification method Download PDFInfo
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Abstract
The invention relates to a multistage lead electrocardiograph signal QRS waveform identification method. The method includes the steps that firstly, collected electrocardiograph signals are filtered, R waves of the II-stage lead electrocardiograph signals are identified, the positions of the R waves of the II-stage lead electrocardiograph signals serve as reference, R waves of other stages of lead electrocardiograph signals are identified, then Q waves and S waves of the multistage lead electrocardiograph signals are identified, and therefore multistage lead electrocardiograph signal QRS waveform identification is achieved. The method has the advantages that a new method is proposed for wavelet transformation R wave identification and matching errors of the maximal value and the minimal value in a traditional maximal value-minimal value method can be avoided. The method is particularly suitable for electrocardiograph signal QRS waveform identification.
Description
Technical field
A kind of method that the present invention relates to electrocardiosignal QRS waveform identification, relates in particular to a kind of multistage electrocardiosignal QRS waveform recognition methods of leading based on wavelet transformation.
Background technology
Because aggravation and the distribution of existing medical resource of China's aging are uneven, medical treatment & health field becomes more and more important in China.Wherein cardiovascular disease is as the healthy important diseases of harm modern, and it is particularly important that the prevention of cardiovascular disease and treatment become.The identification of electrocardiosignal waveform is as an important step for the treatment of cardiovascular disease, and its theoretical research need to provide high accuracy.The step of electrocardiosignal waveform recognition is often from QRS waveform recognition.
In traditional ECG signal sampling process, conventionally adopt ten secondarys of standard lead form, single-stage lead form and other improved forms of leading.But in the recognition methods of traditional electrocardiosignal waveform, often only identify the II level waveform that leads.But some arrhythmia signal II level is led, waveform and the normal signal II level different wave shape that leads is very little, but on other levels are led waveform, has obvious difference.Such as for ventricular premature contraction signal, the characteristic information of the signal on its characteristic information on V1 leads leads than II level is more obvious.Therefore multistage lead signals QRS waveform recognition is helpful to its follow-up characteristic signal extraction and arrhythmia signal classification.The multistage standard that refers to ten secondarys that lead of the present invention lead form or other the improved multiple lead signals that comprise II level lead signals combinations of leading in form, the MLII level combination that combination, MLII level and the V5 level of leading with V1 level are led of leading as described later.Described MLII level is led and is referred to that improving limbs II level leads.
Electrocardiosignal QRS waveform identification mainly contains following method: the method based on filtering and threshold test, the method based on wavelet transformation, and method based on neutral net.Wherein filtering and threshold detection method are the analyses based on time domain, are difficult to choose a threshold value that is applicable to all electrocardiosignaies for a large amount of electrocardiosignaies.Neural net method need to be trained in advance, and its training time is long, is difficult to practical application.Wavelet transformation is the partial transformation of time and frequency, can effectively from signal, extract time-frequency domain information, is applicable to the identification of electrocardiosignal QRS waveform.
Because user is in the time carrying out ECG signal sampling, the electrocardiosignal that cannot determine user is normal electrocardiosignal, still comprises one or more arrhythmia electrocardiosignaies.Therefore, the identification of electrocardiosignal QRS waveform method must be able to identify the QRS waveform of all arrhythmia electrocardiosignaies.Various arrhythmia electrocardiosignaies are all forwards in II level lead signals, and in other grade of lead signals, might not be forward at some.This has carried out some difficulties to the multistage electrocardiosignal QRS waveform identification tape that leads.
Traditional with wavelet transformation carry out the recognition methods of R ripple often on frequency domain passing threshold obtain maximum and minimum, then obtain the zero crossing of maximum-minimum centering according to this maximum and minimum.This zero crossing is considered as to the position that R ripple occurs.To some waveform, the maximum number obtaining in this way is not mated with minimum number, thereby cause the right zero crossing of maximum-minimum that cannot obtain.If one of them maximum and minimum matching error, may cause ensuing other maximum and minimum matching error, thereby form a large amount of right zero crossings of wrong maximum-minimum.
The situation that the identification of electrocardiosignal Q ripple S ripple exists some to be inconvenient to identify: some electrocardiosignal Q ripples or S ripple may disappear; The S ripple of ventricular premature contraction II level lead signals disappears, and it is subsequently followed by inverted T ripple.In the time of identification S ripple, inverted T ripple may be identified as a part for QRS complex wave.
Traditional use wavelet transformation carries out Q ripple S ripple knowledge method for distinguishing and often adopts the method for similar frequency domain maximum-minimum of identifying with R ripple to zero crossing.A zero crossing above of the zero crossing that R ripple is corresponding is Q ripple, and zero crossing is thereafter S ripple.Thereby but this traditional method of special circumstances more described above is not considered the identification that can make the mistake.
The identification of electrocardiosignal QRS waveform need to be identified the QRS waveform of multiple arrhythmia signal in multistage lead signals, and it is high to know method for distinguishing requirement accuracy, thereby the identification of electrocardiosignal has been improved to requirement.
Summary of the invention
Object of the present invention, is exactly for the problems referred to above, proposes one and has proposed the multistage electrocardiosignal QRS waveform recognition methods of leading.
Technical scheme of the present invention: a kind of multistage electrocardiosignal QRS waveform recognition methods of leading, it is characterized in that, comprise the following steps:
A. filtering: the electrocardiosignal collecting is carried out to filtering; Concrete grammar is:
A1. use 5 rank small echo db5 of Daubechies small echo to decompose electrocardiosignal, obtain the baseline drift noise of the corresponding electrocardiosignal of the 7th yardstick low frequency signal, thereby its filtering is reconstructed to filtering baseline drift noise again;
A2. use symlet small echo 8 rank small echo sym8 that each layer of noise estimated respectively, adjusted and carry out hard-threshold filtering, thus filtering power frequency interference noise and myoelectricity interference noise;
B. the lead R ripple of electrocardiosignal of II level is identified: use hard-threshold to carry out maximum detection, then carry out the adjustment of RR spacing, carry out afterwards time domain adjustment identification R ripple; Concrete grammar is:
B1. use sym8 small echo to carry out single reconstruct to one dimension wavelet coefficient, obtain the high-frequency signal of the 4th yardstick;
B2. set fixing amplitude threshold THR
-amplitude, search and be greater than the signaling point of this amplitude threshold and be stored as X
-i, to X
-ido difference, its difference result is the spacing X being greater than between the point of fixed threshold
-diff;
B3., RR spacing threshold value THR is set
-RR-interval, selection is greater than the signal X of this spacing threshold value
-interval, X
-intervalbe every section of position that is greater than the right side boundary point of the signal of amplitude threshold, thereby can obtain right side boundary point X
ok;
B4. basis is greater than the right side boundary position X of the signal of amplitude threshold
-intervalsearch every section of maximum that is greater than the signal of amplitude threshold, the maximum that this maximum is high-frequency signal;
B5. in time domain, carry out time domain adjustment according to maximum position, be specially: in the maximum position left and right time window of 1/12 second in filtered electrocardiosignal, search maximum, this maximum is the R ripple in time domain;
B6. in time domain, again carry out RR spacing threshold filter, be specially: the R ripple that setting RR spacing obtains step b5 carries out filtering, the R ripple that filtered R ripple obtains as identification; The scope of described RR spacing is 0.6～1 second;
C. other the multistage R of leading ripples identifications of electrocardiosignal: carry out again time domain adjustment according to the lead position of R ripple of middle identification of II level as basis, obtain the R ripple identification that other grades lead; Concrete grammar is:
C1. detect other grades lead maximum in time window and the absolute value of minima, the relatively absolute value of maximum and minima, if maximum is greater than the absolute value of minima.If so, enter step c2; If not, enter step c3:
C2. judge that R ripple is forward, find out the R ripple of corresponding maximum as identification;
C3. judge that R ripple is negative sense, find out the R ripple of corresponding minima as identification;
D. electrocardiosignal Q ripple S ripple identification: obtain the low frequency signal of the 4th yardstick by wavelet decomposition, search the trough of both sides, R wave-wave peak on low frequency signal, search maximum in the trough left and right sides, carry out afterwards time domain adjustment identification Q ripple S ripple; Concrete grammar is:
D1. use sym8 small echo to carry out single reconstruct to one dimension wavelet coefficient, obtain the low frequency signal of the 4th yardstick;
The minima of d2. searching the left and right sides, R wave-wave peak in given time window in low frequency signal is identified R wave-wave paddy;
D3. in the given time window in the R wave-wave paddy left and right sides, get maximum, judge that this maximum is whether on edge, the left and right sides, if not, enter steps d 3, if so, in time window, get maximum, described maximum is to find the position of the amplitude that is greater than both sides, front and back from a side along a direction;
D4. in filtered electrocardiosignal, carry out time domain adjustment, be specially on small time window and search maximum, if the maximum of finding is identified Q ripple S ripple by maximum; If there is not maximum, judge that Q ripple S ripple disappears, and the Q ripple S ripple of disappearance is decided to be to R wave-wave paddy.
Beneficial effect of the present invention is, the present invention carries out QRS waveform recognition to multistage electrocardiosignal of leading but not only the II level waveform that leads identified, and can provide more fully characteristic information for subsequent characteristics leaching process like this; The present invention has proposed new method at wavelet transformation identification R ripple, can avoid maximum and minimizing matching error in traditional maximum-minimum method; Disappearance and S ripple that the disappearance of Q ripple or S ripple can be effectively identified in Q ripple S ripple identification of the present invention disappear rear immediately following the special circumstances such as T ripple, the erroneous judgement of having avoided above-mentioned situation to cause waveform recognition; Method for waveform identification rate of accuracy reached provided by the present invention, to actual application level, can provide characteristic information more accurately for follow-up characteristic extraction procedure.
Brief description of the drawings
Electrocardiosignal before Fig. 1 filtering noise;
Electrocardiosignal after Fig. 2 filtering noise;
Fig. 3 uses sym8 small echo list to prop up reconstruct the 4th yardstick high-frequency signal;
Fig. 4 is greater than the signal X of amplitude threshold
i;
Fig. 5 is greater than the right side boundary boundary point X of amplitude threshold signal
ok;
The maximum of Fig. 6 high-frequency signal;
The R ripple recognition effect figure that Fig. 7 II level is led and V1 level is led;
The effect of the R ripple identification in Figure 81 00 second;
The Q ripple that the MLII level of Fig. 9 normal signal is led and V1 level is led and the identification of S ripple;
The Q ripple that Figure 10 leads to the MLII level of normal signal and V5 level is led and the identification of S ripple;
Identification that the MLII level of the multiple rhythm abnormality signal of Figure 11 is led and V1 leads.
Detailed description of the invention
Below in conjunction with accompanying drawing, the present invention is described in detail
Process of the present invention is as follows: first the electrocardiosignal collecting is carried out to filtering, again the lead R ripple of electrocardiosignal of II level is identified, position according to the R ripple of II level lead signals is identified the R ripple of other multistage electrocardiosignaies of leading as a reference, afterwards the Q ripple S ripple of multistage lead signals is identified, to realize, multi-lead electrocardiosignal QRS waveform is identified.
Filtering is described below: the electrocardiosignal collecting comprises the noises such as baseline drift noise, power frequency interference noise, myoelectricity interference noise.Baseline drift noise is low-frequency noise, and power frequency interference noise and myoelectricity interference noise belong to high-frequency noise.The present invention uses 5 rank small echo db5 of Daubechies small echo to decompose electrocardiosignal, obtains the baseline drift noise of the corresponding electrocardiosignal of the 7th yardstick low frequency signal, its filtering is reconstructed again to the effect that obtains filtering baseline drift noise.Filtering power frequency interference noise and myoelectricity interference noise are to adopt small echo to carry out the automatic noise reduction of one dimension, use symlet small echo 8 rank small echo sym8 that each layer of noise estimated respectively, adjusted here and carry out hard-threshold filtering.As shown in Figure 1, the electrocardiosignal after filtering noise as shown in Figure 2 for the original electrocardiographicdigital signal collecting.On the basis of the electrocardiosignal after filtering noise, carry out QRS waveform recognition herein, to avoid these noises to impact the identification of QRS waveform.
The electrocardiosignal II level R ripple identifying of leading: the present invention proposes to use hard-threshold to carry out maximum detection, then carries out the adjustment of RR spacing, carries out afterwards time domain adjustment.In time domain is adjusted, be mainly maximizing in given less time window, and adjust again according to RR spacing.
The present invention does not have the directly signal after filtering noise directly to search the maximum of spacing, is that the range value of corresponding waveform exists deviation because the electrocardiosignal of every record is the detection that different people is carried out.And the amplitude of the T ripple of some arrhythmia electrocardiosignal is greater than the amplitude of R ripple.Therefore be difficult to select a T ripple that is greater than all records to be less than again the threshold value of the R ripple of all records.
The present invention uses sym8 small echo to carry out single reconstruct to one dimension wavelet coefficient, obtains the high-frequency signal of the 4th yardstick.The 4th yardstick high-frequency signal as shown in Figure 3.II level lead high-frequency signal only on position corresponding to R wave-wave peak place amplitude larger, on other positions, amplitude is very little.Therefore can set a fixing amplitude threshold THR
-amplitude, search and be greater than the signaling point of this amplitude threshold and be stored as X
-i(as shown in Figure 4), to X
-ido difference, difference result is exactly the spacing X being greater than between the point of fixed threshold
-diff.According to the observation, a RR spacing threshold value THR is set
-RR-interval, selection is greater than the signal X of this spacing threshold value
-interval, X
-intervalit is every section of position that is greater than the right side boundary point of the signal of amplitude threshold.Thereby can obtain right side boundary point X
ok(as shown in Figure 5).
Concrete formula is as follows, wherein ECG
high-frequencyfor the high-frequency signal of the 4th yardstick of electrocardiosignal.
X?
i＝find(ECG?
high-frequency>THR?
amplitude)
X?
diff＝X
i+1–X?
i,i＝1…Length(X
i)-1
X?
interval＝find(X?
diff>THR?
RR?
interval)
X?
ok＝X
i(X?
interval)
According to the right side boundary position X of signal that is greater than amplitude threshold
-intervalsearch every section of maximum that is greater than the signal of amplitude threshold.This maximum is the maximum (as shown in Figure 6) of high-frequency signal, because the spacing of maximum point and maximum-minimum zero crossing is very little, can make up error by time domain adjustment completely, and can avoid obtaining like this loaded down with trivial details processing in the process of maximum-minimum zero crossing and some process the defect of bringing improperly, therefore the present invention directly adopts maximum but not maximum-minimum zero crossing.
In time domain, carry out time domain adjustment according to the maximum position in Fig. 6: in the maximum position left and right time window of 1/12 second in the electrocardiosignal after filtering noise, search maximum, this maximum is the R ripple in time domain.
Because the spacing threshold value in high-frequency signal is the point that is greater than amplitude threshold in order to choose, therefore the RR spacing threshold value is here smaller than normal RR spacing.Therefore may there is the erroneous judgement that T ripple large amplitude is identified as to R ripple, therefore in time domain, again carry out RR spacing threshold filter here.Heart rate is to represent heart number of times of beating per minute, and a R wave-wave peak is brought in the heart capital of often beating.Therefore RR spacing can be estimated from heart rate.The heart rate range of human body be 60 bats/assign to, 100 bats/point, therefore RR spacing is between 0.6 second to 1 second.Therefore setting 0.6 second is here RR spacing.R ripple to above-mentioned estimation carries out filtering.The R ripple that R ripple after filtering obtains as identification.
Other the multistage R of leading ripple identifyings of electrocardiosignal are as follows: other multistage lead signals can be identified according to identical method, but can increase like this amount of calculation.And for some special circumstances, may occur that other grades R ripple signal and the II level of identification R wave number amount of identifying of leading of leading do not mate.This can have influence on the extraction of subsequent characteristics information.Therefore, the method that the present invention adopts is that the electrocardiosignal of leading for other adopts the lead position of R ripple of middle identification of II level to carry out time domain adjustment as basis, the R ripple identification of leading to obtain other grades again.
The lead time domain key of R ripple identification of other grades of electrocardiosignal is whole similar with the time domain adjustment that the time domain of middle R ripple identification adjusts of leading of II level.There is negative wave in the R ripple only leading due to other grades of some arrhythmia electrocardiosignaies.Therefore in the time carrying out time domain adjustment, need to judge in advance: detect the maximum that other grades lead in time window, this maximum and threshold value are compared, if be greater than this threshold value, think that R ripple is forward, otherwise R ripple is negative sense.Find out again corresponding maximum or the minima R ripple as identification.
The result of the R ripple identification that final II level is led and V1 level is led as shown in Figure 7.The R ripple of the electrocardiosignal in 100 seconds is identified as shown in Figure 8.The discrimination of R ripple is very high as can be seen here, and does not occur the undetected situation due to the little electrocardiosignal of the very large amplitude causing of the deviation between amplitude maximum and amplitude minima in traditional method.There is not the unmatched situation that traditional modulus maximum-minimum is right, illustrate that accuracy rate and the robustness effect of program of method provided by the invention is fine yet.
Electrocardiosignal Q ripple S ripple identifying: use wavelet decomposition to obtain the low frequency signal of the 4th yardstick, search the trough of both sides, R wave-wave peak on low frequency signal, search maximum in the trough left and right sides, carry out afterwards time domain adjustment.
The present invention uses sym8 small echo to carry out single reconstruct to one dimension wavelet coefficient, obtains the low frequency signal of the 4th yardstick.There is no directly on filtered signal, to carry out the identification of Q ripple S ripple is that these burrs can cause erroneous judgement in the time searching R wave-wave paddy and Q ripple S ripple because the electrocardiosignal after filtering noise still comprises some burrs.
The present invention searches the minima of the left and right sides, R wave-wave peak and identifies R wave-wave paddy in low frequency signal in given time window.Here need in regular hour window, search is because likely do not deposit S ripple and follow an inverted T ripple, therefore inverted T wave-wave peak may be identified as to R wave-wave paddy by mistake.
In the given time window in the R wave-wave paddy left and right sides, get maximum, if this maximum on edge, the left and right sides, illustrates that this maximum is on the edge of P ripple/T ripple, crossed Q ripple/S ripple.In time window, get maximum for above-mentioned situation.Get maximum find from a side along a direction be greater than before and after the position of amplitude on both sides.
On low frequency signal, find after the approximate location of Q ripple S ripple, in the electrocardiosignal after filtering noise, carry out time domain adjustment, on small time window, searching maximum.Because the waveform of electrocardiosignal can be determined by R ripple, the electrocardiosignal waveform number that therefore the ecg wave form number of the electrocardiosignal of other grades leads with II level is identical.So adopt method identification Q ripple S ripple identical in II level lead signals in other grade of lead signals, repeat no more here.
Disappear for tackling aforementioned middle Q ripple S ripple, when the present invention is taken at maximum, if maximum does not exist, the Q ripple S ripple of disappearance is decided to be to R wave-wave paddy.For reply T ripple is inverted, the present invention, at above-mentioned given regular hour windows of every step operation, can not recognized on inverted T ripple the identification of S ripple by mistake.
May not there is not complete QRS ripple in the ecg wave form that some of some electrocardiosignal are led, the waveform leading as the V1 level of normal signal.Due to obtaining normal signal or arrhythmia signal before this signal and while not knowing this signal, therefore cannot make different processing to signal according to different situations.Here the present invention does not have the signal of complete QRS ripple still to take identical method to these, although there is no complete QRS waveform, can provide some characteristic informations, does not affect follow-up waveform processing.
To the MLII level of normal signal lead and V1 level lead Q ripple and S ripple identification as shown in Figure 9.To the MLII level of normal signal lead and the identification of the Q ripple S ripple that leads of V5 level as shown in figure 10.The MLII level of multiple rhythm abnormality signal is led and led identification as shown in figure 11 with V5.On these images, highs and lows represents R ripple, and R ripple both sides are R wave-wave paddy, and on the left of R ripple, the point in the left side of trough represents Q ripple, represents S ripple at the point on trough right side, R ripple right side.If five of the some less thaies in a complete electrocardiographic wave illustrate that corresponding Q ripple or S ripple disappear, using R wave-wave paddy as Q ripple or S ripple.As seen from Figure 11, be labeled as 5 ventricular premature contraction signal and T ripple be not mistaken for to S ripple.The QRS waveform recognition of the electrocardiosignal in these figures is all correct.Illustrate that the present invention can obtain good effect in QRS waveform recognition.
Claims (1)
1. the multistage electrocardiosignal QRS waveform recognition methods of leading, is characterized in that, comprises the following steps:
A. filtering: the electrocardiosignal collecting is carried out to filtering; Concrete grammar is:
A1. use 5 rank small echo db5 of Daubechies small echo to decompose electrocardiosignal, obtain the baseline drift noise of the corresponding electrocardiosignal of the 7th yardstick low frequency signal, thereby its filtering is reconstructed to filtering baseline drift noise again;
A2. use symlet small echo 8 rank small echo sym8 that each layer of noise estimated respectively, adjusted and carry out hard-threshold filtering, thus filtering power frequency interference noise and myoelectricity interference noise;
B. the lead R ripple of electrocardiosignal of II level is identified: use hard-threshold to carry out maximum detection, then carry out the adjustment of RR spacing, carry out afterwards time domain adjustment identification R ripple; Concrete grammar is:
B1. use sym8 small echo to carry out single reconstruct to one dimension wavelet coefficient, obtain the high-frequency signal of the 4th yardstick;
B2. set fixing amplitude threshold THR
-amplitude, search and be greater than the signaling point of this amplitude threshold and be stored as X
-i, to X
-ido difference, its difference result is the spacing X being greater than between the point of fixed threshold
-diff;
B3., RR spacing threshold value THR is set
-RR-interval, selection is greater than the signal X of this spacing threshold value
-interval, X
-intervalbe every section of position that is greater than the right side boundary point of the signal of amplitude threshold, thereby can obtain right side boundary point X
ok;
B4. basis is greater than the right side boundary position X of the signal of amplitude threshold
-intervalsearch every section of maximum that is greater than the signal of amplitude threshold, the maximum that this maximum is high-frequency signal;
B5. in time domain, carry out time domain adjustment according to maximum position, be specially: in the maximum position left and right time window of 1/12 second in filtered electrocardiosignal, search maximum, this maximum is the R ripple in time domain;
B6. in time domain, again carry out RR spacing threshold filter, be specially: the R ripple that setting RR spacing obtains step b5 carries out filtering, the R ripple that filtered R ripple obtains as identification; The scope of described RR spacing is 0.6～1 second;
C. other the multistage R of leading ripples identifications of electrocardiosignal: carry out again time domain adjustment according to the lead position of R ripple of middle identification of II level as basis, obtain the R ripple identification that other grades lead; Concrete grammar is:
C1. detect the maximum that other grades lead in time window, judge whether maximum is greater than threshold value; If so, enter step c2, if not, enter step c3;
C2. judge that R ripple is forward, find out the R ripple of corresponding maximum as identification;
C3. judge that R ripple is negative sense, find out the R ripple of corresponding minima as identification;
D. electrocardiosignal Q ripple S ripple identification: obtain the low frequency signal of the 4th yardstick by wavelet decomposition, search the trough of both sides, R wave-wave peak on low frequency signal, search maximum in the trough left and right sides, carry out afterwards time domain adjustment identification Q ripple S ripple; Concrete grammar is:
D1. use sym8 small echo to carry out single reconstruct to one dimension wavelet coefficient, obtain the low frequency signal of the 4th yardstick;
The minima of d2. searching the left and right sides, R wave-wave peak in given time window in low frequency signal is identified R wave-wave paddy;
D3. in the given time window in the R wave-wave paddy left and right sides, get maximum, judge that this maximum is whether on edge, the left and right sides, if not, enter steps d 3, if so, in time window, get maximum, described maximum is to find the position of the amplitude that is greater than both sides, front and back from a side along a direction;
D4. in filtered electrocardiosignal, carry out time domain adjustment, be specially on small time window and search maximum, if the maximum of finding is identified Q ripple S ripple by maximum; If there is not maximum, judge that Q ripple S ripple disappears, and the Q ripple S ripple of disappearance is decided to be to R wave-wave paddy.
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