CN106889984B - A kind of automatic noise-reduction method of electrocardiosignal - Google Patents
A kind of automatic noise-reduction method of electrocardiosignal Download PDFInfo
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Abstract
The present invention relates to a kind of automatic noise-reduction methods of electrocardiosignal, and realization process is: a) detecting the R wave crest location of electrocardiosignal, and tentatively filtered；B) heart for selecting several continuous hearts to clap from preliminary filtered electrocardiosignal claps section and does mean value, obtains average template；C) section is clapped with the acquired corresponding heart of electrocardiosignal of average template replacement, obtains instructing signal；D) several windows are successively set on instructing signal, then in each window, are exported using instructing Filtering Model that signal will be instructed to obtain filtering by linear transformation；E) after to the complete linear coefficient of all window calculations, substitution is instructed to acquire q in Filtering ModeliValue as final result.The present invention can retain the most amplitude characteristic of electrocardiosignal using the method for average template in certain degree, then signal is instructed to construct by substitution electrocardiosignal major part, what is obtained in this way instructs signal to retain the most amplitude Characteristics of electrocardiosignal.
Description
Technical field
Automatic detection and analytical technology the present invention relates to electrocardiosignal, specifically a kind of automatic noise reduction of electrocardiosignal
Method.
Background technique
In recent years, cardiovascular disease has become the number one killer for threatening human life and health.Cardiovascular disease has prominent
Hair property is fallen ill just in a flash.Often because symptom is of short duration, when patient rushes to hospital's progress electrocardiogram acquisition, symptom has disappeared,
Electrocardiogram at this time is often difficult to capture effective diagnosis basis, cannot reflect the real heart health status of patient, because
Patient is caused not treated accordingly to lack the electrocardiogram (ECG) data during disease incidence.Therefore, most for cardiovascular disease
Good method is exactly the Holter that cardiovascular patient carries out 24 hours in hospital.It is prominent winged in the telecommunication technique of modernization
Under the social background to push ahead vigorously, remote electrocardiogram monitor instrument is undoubtedly best solution.Remote electrocardiogram monitor just needs patient real
When dress detection device, under the support of internet, data can be real-time transmitted to fixed hospital.Maximum problem among these
It is that the equipment of wearing and the electrocardiograph of hospital are different, data are acquire in various schedule lives in patient, and are being cured
The electrocardiogram (ECG) data of institute's static state acquisition, which is compared, has very big interference signal, such as the most common electrode contact noise (EM), muscle
Baseline drift (BW) caused by myoelectricity interference (MA) caused by trembleing, human body respiration movement etc..These are largely influenced
The normal morphology of electrocardiosignal, and existing algorithm is easily lost certain important features of electrocardiosignal in noise reduction process, it is right
The diagnosis of doctor causes very big deviation, and misdiagnosis rate improves.Therefore the original form for restoring electrocardiosignal has ten to medical diagnosis on disease
Divide important meaning.
Summary of the invention
The object of the present invention is to provide a kind of methods of the automatic noise reduction of electrocardiosignal, to solve existing algorithm in noise reduction process
The problem of middle loss electrocardiosignal important feature.
The object of the present invention is achieved like this:
A kind of automatic noise-reduction method of electrocardiosignal comprising following steps:
A) electrocardiosignal for obtaining one section of human body, first detects the R wave crest location of acquired electrocardiosignal, then carries out
Preliminary filtering removes baseline drift to filter out low frequency signal；
B) heart for selecting several continuous hearts to clap from preliminary filtered electrocardiosignal claps section and does mean value to get average
Template；The length that section is clapped from the heart selected in the bat of each heart includes 270 sampled points, and 270 sampled points are by R wave wave crest
180 sampled points after 1 sampled point and R wave wave crest at 89 sampled points before, R wave wave crest are constituted；
C) with each heart in human ecg signal acquired in the obtained average template replacement step a) of step b)
It claps the corresponding heart and claps section, obtain instructing signal；
When being replaced, if the length of a RR interphase is less than the length of the average template, fractional-sample point is had
The sampled point of overlapping is carried out average value processing at this time by overlapping；
D) it instructs successively to set several window ω on signal described, then in each window, be filtered using guidance
Model instructs signal to obtain filtering output by linear transformation for described:
Wherein 1: in each window for instructing successively to set on signal, (a, b) is kept not in a window
A series of linear coefficients become, value be filtering output and the corresponding signal difference to be filtered of each sampled point in window most
Linear coefficient value corresponding to hour；
Wherein 2:IiIndicate the value of the ith sample point in place window that instructs signal；qiIt indicates described and instructs signal
The filtering output value of the ith sample point of window at place；
The signal to be filtered is human ecg signal acquired in step a)；
E) after linear coefficient (a, b) having been calculated to all window ω, by a, I in each windowiSubstitute into guidance filtering respectively with b
Q is acquired in modeliValue as final result, if should during the case where different windows is related to the same sampled point occur
When, the filtering output value of the sampled point is determined according to the average value of its calculated result in different windows.
In step d), the value of (a, b) is calculated using least square method for the automatic noise-reduction method of the electrocardiosignal.
The biggest advantage is to the Weak characteristic of electrocardiosignal, electrocardio letter can be maintained during filtering for this method
Number amplitude characteristic is very faint, this is just determined is easily lost faint feature during denoising, and guidance filtering is just very
Good solves this problem, we can give the feature lost in filtering for change under the guidance for instructing signal, we
The most amplitude characteristic of electrocardiosignal can be retained in certain degree using the method for average template, then by replacing
Constructed for electrocardiosignal major part and instruct signal, obtain in this way to instruct signal to retain electrocardiosignal most
Amplitude Characteristics, while those of loss energy can be given for change according to original signal to be filtered during carrying out guidance filtering
The Weak characteristic of very little, such as P wave and T wave etc..
Detailed description of the invention
Human ecg signal acquired in the step of Fig. 1 is embodiment 1 (1).
The step of Fig. 2 is embodiment 1 (5) is obtained to instruct signal.
Fig. 3 is the final filtering output result of embodiment 1.
Fig. 4 is the filtering output result of comparative example 1.
Fig. 5 is the maximum point for choose after butterworth high pass filter processing to electrocardiosignal.
Fig. 6 is optimization maximum point flow chart.
Specific embodiment
Technical solution of the present invention is further explained and is illustrated below by embodiment and comparative example.
Embodiment 1
(1) obtain human ecg signal: acquisition equipment is the MedSun18 lead Holter long-time of Beijing Peng Yangfeng industry
The electrocardiosignal of human body is acquired, sampling output frequency is 360Hz, and acquisition electrocardiogram (ECG) data is stored in the form of TXT.It is collected
Electrocardiosignal can easily read in Matlab environment and be shown, the human ecg signal packet intercepted in the present embodiment
It is clapped containing general 10 hearts, totally 3000 sampled points, as shown in Figure 1.
(2) electrocardio original signal data collected is filtered: is filtered out using butterworth high pass filter
Baseline drift noise, the cutoff frequency of stopband are 1Hz, the minimum 30dB of stopband attenuation, and the decaying of passband is up to 15dB.
(3) energy window transformation is carried out to by the electrocardiosignal of butterworth high pass filter, and chooses maximum point:
The transformation of (3-1) energy window: as the following formula, will be divided by the electrocardiosignal p of butterworth high pass filter by time-domain
Analysis transforms to energy domain analysis, obtains electrocardiosignal energy curve:
Wherein, EnIndicate the energy value of n-th of sampled point；N is selected length of window, value 26；M is total sampled point
Number；pnIndicate it is described denoised using butterworth high pass filter after electrocardiosignal p n-th of sampled point；
(3-2) chooses maximum point: obtained electrocardiosignal energy curve is carried out hard -threshold processing, it may be assumed that
Wherein, ThFor selected threshold value, T is takenh=0.3*median (En),
Then it selects by the crest location of hard -thresholdization treated electrocardiosignal energy curve as maximum point, such as
Fig. 5；
(3-3) optimizes maximum point: it presses existing algorithm (reference can be made to CN103156599A), the flow chart as given by Fig. 6,
Set 2 time threshold t1And t2, and t1< t2When the time interval of any two maximum point is less than t1When, just remove the two
Between maximum point amplitude it is lesser that；When the time interval of any two maximum point is greater than t2When, just in the two poles
Another unrecognized extreme point is found between big value point；As the time interval of two maximum points is both greater than t1, and it is less than t2,
Then two maximum points retain, and such finally obtained optimized each maximum point corresponds to a QRS complex.
In Fig. 6, EtIndicate the average value of the time interval of step (4-2) obtained all maximum points, t1=0.5 ×
Et, t2=1.5 × Et。
(3-4) according to the time point where each maximum point in step (3-3), the human body heart acquired in step (1)
Search letter in electric signal and step (2) on filtered electrocardiosignal in the range of each 7 sampled points of or so corresponding time point
The point of number amplitude maximum, as the R wave wave crest detected.
(4) average template is constructed after obtaining R wave crest location, the average template is by the preliminary filtered of the same person
The heart that 1000 continuous hearts are clapped in electrocardiosignal claps section and mean value is taken to obtain；
It includes 270 sampled points that the heart intercepted, which claps section, which is according to the R wave wave crest detected, by R
Sampled point where wave wave crest, on the right of 89 sampled points and R wave wave crest on the R wave wave crest left side (i.e. before it) (i.e. after it)
180 sampled points are constituted.
(5) each heart in human ecg signal acquired in average template replacement step (1) obtained above is clapped
In the corresponding heart clap the position of section, obtain instructing signal, as shown in Figure 2；
Since electrocardiosignal is a kind of cyclical signal, so average template obtained above is replaced signal to be filtered
The heart on corresponding position in each electrocardiosignal period of (acquired human ecg signal original waveform i.e. in step (1))
When clapping section, it may appear that two kinds of situations:
1. when the length of a RR interphase (i.e. an electrocardiosignal period) is less than the length of average template obtained above:
Since the length of average template is greater than the period of an electrocardiosignal, will result in fractional-sample point in this way can be overlapped, at this time
The sampled point of overlapping need to be subjected to average value processing again, to obtain reconstruction signal identical with original signal strength.
2. the length when a RR interphase (i.e. an electrocardiosignal period) is greater than or equal to average template obtained above
Length: since the length of average template is less than or equal to the period of an electrocardiosignal, it is possible to normal to complete replacement.
(6) it is filtered out using instructing signal to treat the noise in filtering signal:
The model of guidance filtering is a local linear transformation model by instructing signal I to export to filtering q, in this example,
Length of window is set as | ω |=401, so the 201st sampled point is first window for this instructs signal I
ω1Central point, the 202nd sampled point is second window ω2Central point, and so on, jth (j=201,202 ...,
3000) a sampled point is (j-200) a window ωk(k=j-200) central point, in the window ω of settingk(k=1,2,
3 ..., j-200) in, qiBy IiIt is obtained by linear transformation:
Formula 1:
In formula 1, (ak,bk) it is in window ωkIn the linear coefficient that remains unchanged；IiExpression instructs signal in place window
The amplitude of the ith sample point of mouth；qiExpression instructs signal in the filtering output amplitude of the ith sample point of place window.
For the linear coefficient (a of each window of determinationk,bk) value, one group for needing to find out formula 1 makes qiWith input signal
pi(signal i.e. to be filtered) differs least solution, we are in window ω thuskIt is middle to be calculated using following least square method:
Formula 2:
Here pkIndicate window ωkIn original signal to be filtered in the amplitude of corresponding sampled point, ε is regularization coefficient, prevent
Only akBecome excessive, value ε=0.22, the solution of formula 2 is obtained by the method for linear regression:
Wherein: μkWithRespectively in window ωkIn each sampled point for instructing signal I mean value and variance, | ω | be
ωkIn number of sampling points,For in window ωkIn input signal pkEach sampled point mean value.
(7) to instructing all window ω in signalk(a has been calculatedk, bk) after, to all parts of entire electrocardiosignal
The linear model of window application formula 1, but problem appear to is that all window ω comprising sampled point ikIt will involve and adopt
Sampling point i leads to the q in formula 1iCalculated result in different windows is different, and the strategy taken at this time is all calculating
Obtained qiIt is averaged as final result, it may be assumed that
Formula 3:
Herein is in seek qiAverage value, have multiple windows can be comprising sampled point i, the i inside different windows is corresponding not
Same coefficient (ak, bk), therefore, we seek (a in multiple windowsk, bk) average value, because of these windows ωkAll contain the inside
Sampled point i and coefficient (ak, bk), therefore have k | i ∈ ωk,
Wherein
Here k ∈ ωiIn, k indicates that the number of all windows comprising sampled point i, value are exactly equal to length of window
401, ωiIndicate the window comprising the same sampled point i；Indicate the coefficient of all windows comprising the same sampled point
Average value, according to calculating, the window comprising sampled point i just with setting window equal length.
Filter result is obtained according to the above processing step, as shown in Figure 3.
Comparative example 1
It is selected in same Example to obtain the original wave band of electrocardiosignal, select db6 as basic wavelet basis first, to this
Electrocardio original signal carries out 8 layers of wavelet decomposition, obtains the wavelet coefficient on each scale
Here i is layer label.
Reservation useful signal as much as possible while in order to guarantee to remove noise, this research is using with dimension self-adaption
The threshold method of property:
Wherein, TiFor the threshold value of setting, NiFor the number of the i-th layer coefficients, e is natural constant,
Wavelet coefficient on each scale is handled using soft threshold method:
To thresholding, treated that wavelet coefficient is reconstructed, and obtains filter result, as shown in Figure 4.
1 detailed process steps of comparative example can refer to Reddy, G.U., Muralidhar, M., &Varadarajan, S.
(2009).Ecg denoising using improved thresholding based on wavelet
transforms.International Journal of Computer Science&Network Security,253-
257.。
Observation original electro-cardiologic signals can be seen that after the inversion of T wave occurs for electrocardiosignal, and the electrocardiogram of acquired original can quilt
Noise severe jamming causes T wave to be inverted and severe deviations occurs.Comparison diagram 3 and Fig. 4 can be seen that, the output result of the method for the present invention
(Fig. 3) has maintained the fine feature of electrocardiosignal well, it is evident that as can be seen that the inversion of T wave is completely identified, simultaneously
Most of noise is removed, has restored the original morphological feature of electrocardiosignal, method of the invention provides signal according to instructing signal
General form, the minutia of comprehensive input noise signal perfectly recovers T wave and P wave is this kind of in conjunction with the feature of the two
The morphological feature of the extremely faint signal of energy has been given the information of great medical reference value for change, is provided more for the diagnosis of doctor
Good help.Result (Fig. 4) must be exported using existing common filtering method as can be seen that making an uproar using small echo removal electrocardiosignal
Sound, the baseline drift noise for causing signal to deviate baseline is substantially filtered out, so that signal returns near baseline, but is still had a large amount of
Noise jamming waveform, the typical P wave of electrocardiosignal, QRS complex and T wave are all seriously affected by noise and lead to not find out original
There is normal form, that is a large amount of useful ecg information is lost for this, or perhaps is not resumed, such filtering
As a result highly desirable removal noise is not achieved the purpose that.
Claims (2)
1. a kind of automatic noise-reduction method of electrocardiosignal, characterized in that the following steps are included:
A) electrocardiosignal for obtaining one section of human body, first detects the R wave crest location of acquired electrocardiosignal, then carries out preliminary
Filtering removes baseline drift to filter out low frequency signal；
B) heart for selecting 1000 continuous hearts to clap from the preliminary filtered electrocardiosignal of the same person claps section and does mean value, i.e.,
Obtain average template；The length that the selected heart claps section includes 270 sampled points, before 270 sampled points are by R wave wave crest
180 sampled points after 1 sampled point and R wave wave crest at 89 sampled points, R wave wave crest are constituted；
C) phase is clapped with each heart in human ecg signal acquired in the obtained average template replacement step a) of step b)
The corresponding heart claps section, obtains instructing signal；
When being replaced, if the length of a RR interphase is less than the length of the average template, the overlapping of fractional-sample point is had,
The sampled point of overlapping is subjected to average value processing at this time；
D) it instructs successively to set several window ω on signal described, then in each window, using instructing Filtering Model
Signal is instructed to obtain filtering output by linear transformation by described:
Wherein 1: in each window for instructing successively to set on signal, (a, b) is remained unchanged in a window
A series of linear coefficients, when value is that the filtering of each sampled point in a window is exported with corresponding signal difference minimum to be filtered
Corresponding linear coefficient value；
Wherein 2:IiIndicate the value of the ith sample point in place window that instructs signal；qiIndicating described instructs signal in institute
In the filtering output value of the ith sample point of window；
The signal to be filtered is human ecg signal acquired in step a)；
E) after linear coefficient (a, b) having been calculated to all window ω, by a, I in each windowiIt is substituted into respectively with b and instructs Filtering Model
In acquire qiValue as final result, if occurring different windows in this step calculating process is related to the same sampled point
When situation, the filtering output value of the sampled point is determined according to the average value of its calculated result in different windows.
2. the automatic noise-reduction method of electrocardiosignal according to claim 1, characterized in that in step d), using minimum two
Multiplication calculates the value of (a, b).
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| CN108158573B (en) * | 2017-12-26 | 2020-10-30 | 智慧康源（厦门）科技有限公司 | Electrocardiosignal noise reduction method based on adaptive threshold wavelet transformation |
| CN108201437B (en) * | 2017-12-28 | 2020-07-28 | 北京怡和嘉业医疗科技股份有限公司 | Signal processing method and device |
| CN109567788B (en) * | 2018-11-29 | 2021-08-20 | 武汉中旗生物医疗电子有限公司 | Electrocardiosignal filtering method for removing ringing |
| CN109497994B (en) * | 2019-01-16 | 2021-08-24 | 上海掌门科技有限公司 | Electrocardiosignal R wave detection method, equipment and computer readable medium |
| CN109497995B (en) * | 2019-01-16 | 2021-08-24 | 上海掌门科技有限公司 | Electrocardiosignal R wave detection method, electrocardiosignal R wave detection equipment and computer readable medium |
| CN110101383B (en) * | 2019-04-19 | 2021-09-21 | 长沙理工大学 | Wavelet energy-based electrocardiosignal denoising algorithm |
| CN110141215B (en) * | 2019-05-14 | 2020-12-15 | 清华大学 | Training method of noise reduction self-encoder, noise reduction method of electrocardiosignal, related device and equipment |
| CN110169767B (en) * | 2019-07-08 | 2021-09-21 | 河北大学 | Retrieval method of electrocardiosignals |
| CN112535455A (en) * | 2019-09-20 | 2021-03-23 | 深圳市理邦精密仪器股份有限公司 | Filtering method, terminal device and computer storage medium |
| CN117770758A (en) * | 2022-09-29 | 2024-03-29 | 荣耀终端有限公司 | Signal denoising method and electronic equipment |
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| CN102793539B (en) * | 2012-08-31 | 2014-07-09 | 深圳市理邦精密仪器股份有限公司 | Vector cardiogram detection and analysis method and system |
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