CN106419898A - Method removing electrocardiosignal baseline drift - Google Patents
Method removing electrocardiosignal baseline drift Download PDFInfo
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- CN106419898A CN106419898A CN201610675959.4A CN201610675959A CN106419898A CN 106419898 A CN106419898 A CN 106419898A CN 201610675959 A CN201610675959 A CN 201610675959A CN 106419898 A CN106419898 A CN 106419898A
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- 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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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
- A61B5/316—Modalities, i.e. specific diagnostic methods
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7203—Signal processing specially adapted for physiological signals or for diagnostic purposes for noise prevention, reduction or removal
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7235—Details of waveform analysis
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7235—Details of waveform analysis
- A61B5/725—Details of waveform analysis using specific filters therefor, e.g. Kalman or adaptive filters
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7235—Details of waveform analysis
- A61B5/7253—Details of waveform analysis characterised by using transforms
Abstract
The invention discloses a method removing electrocardiosignal baseline drift; the method comprises the following steps: 1, determining a wavelet decomposition layer: using a signal sampling rate and an electrocardiosignal effective frequency range to determine the wavelet decomposition layer; 2, decomposing wavelet: using wavelet analysis to carry out J layer decomposition for the electrocardiosignal according to the wavelet decomposition layer determined in the step1, wherein the wavelet high frequency coefficients of each layer are respectively CD1, CD2,... CDJ, and the lower frequency coefficient is CAJ; 3, wavelet coefficient processing and signal reconstruction: respectively processing high frequency coefficients and low frequency coefficient in the step2, and carrying out wavelet reconstruction according to the processed wavelet coefficients; 4, moving average filtering step: carrying out moving average filtering on reconstructed signal in the step 3, thus obtaining an electrocardiosignal baseline drift value; 5, removing baseline drift: using an original electrocardiosignal signal to subtract the baseline drift value in the step 4, thus obtaining the baseline drift removed signal.
Description
Technical field
The present invention relates to biomedicine signals noise management technique field, particularly relate to wavelet analysis combined window and move flat
The method that all ECG baseline drift is removed in filtering.
Background technology
Electrocardiosignal (Electrocardiogram, ECG) is that the mankind study the earliest and are applied to clinical medical biological electricity
One of signal, it reflects heart and produces in excitement, conduct and the Electrical change in recovery process, is a kind of visitor of cardiac electrical activity
Seeing and representing, reflecting the duty of heart in different aspects, clinical diagnosis and treatment for heart disease have very
Important reference value.Electrocardiosignal is a kind of faint bioelectrical signals, and its amplitude is millivolt (mV) level, it is easy to by outward
The impact of boundary's environment.During to electrocardiographic recording, the activity of the person's of being recorded health, breathing, various communication apparatus etc. all can
Producing bigger interference to recording process, these interference have a great impact for the correctness of signal detection.
Electrocardiosignal is mainly disturbed by following three types noise：
1 industrial frequency noise：Industrial frequency noise is a kind of interference being produced by electrified system, and the form presenting is similar at pure electrocardio
Sinusoidal wave and similar sinusoidal wave waveform occurs on signal.Such noise frequency is generally 50Hz or 60Hz, and its
Amplitude is not high, is about the 50% of this electro-cardiologic signal waveforms high-amplitude.
2 myoelectricity noises：Myoelectricity noise is owing to the movement of muscular tone and the person causes, the frequency of this noise like
For 0-10kHz, similar to average Gaussian noise, so not being typically it is obvious that this noise like typically shows fainter, can
Myoelectricity interference is regarded as the zero-mean band-limited noise of moment generation.
3 baseline drift noises：Baseline drift interference in ECG is common situation in ECG's data compression, by being remembered
Causing when record person breathes, having no idea to avoid, the noise amplitude ratio of baseline drift is relatively low, and baseline drift frequency arrives at 0.05Hz
Between 2Hz, this curve ratio is shallower simultaneously, with sinusoidal waveform is similar, is difficult to remove.
The noise main method removing three of the above type has：The method removing industrial frequency noise is relatively more, mainly includes：Flat
Sliding filtering, IIR filtering, FIR filtering, adaptive-filtering etc., the way of comparative maturity is to utilize notch filter.Every kind of method all has
Its respective good and bad point, selects suitable method according to actual application demand in engineer applied；Remove myoelectricity noise, for flesh
Electrical noise typically can take the low pass filter taking upper limiting frequency to be 100Hz on high-pass filtering combined with hardware to eliminate；
Remove baseline noise, typically have the methods such as traditional high-pass filtering and wavelet transformation, due to needle position misalignment noise frequency ratio relatively
Low (generally 0-0.05Hz), filter order required during design high-pass filter is big especially, computationally intensive, takies calculating money
Source is many；Equally, when the method utilizing wavelet analysis is carried out, owing to it is based on convolution algorithm, will be by when removing baseline drift
After signal decomposition to ten layers (in the case that particularly signal sampling rate is big), computationally intensive, high to calculating resource requirement, unfavorable
Process in real-time.
Content of the invention
The technical problem to be solved is to provide a kind of method removing ECG baseline drift, is used for overcoming
The technical problem that prior art exists.
It is as follows that the present invention solves the technical scheme that above-mentioned technical problem taked：
A kind of method removing ECG baseline drift, including：
Step 1) determine wavelet decomposition number of plies step, including：By the effective band of the sample rate of signal and electrocardiosignal,
Determine the number of plies of wavelet decomposition；
Step 2) carry out wavelet decomposition step, including：According to the wavelet decomposition number of plies determining in step 1, sampling small echo
Analyzing and carrying out J layer decomposition to electrocardiosignal, the small echo high frequency coefficient of every layer is respectively：CD1, CD2 ..., CDJ, low frequency coefficient
For：CAJ；
Step 3) wavelet coefficient process and signal reconstruction step, including：Respectively by the high frequency coefficient in step 2 and low frequency system
Number is processed, and carries out wavelet reconstruction according to the wavelet coefficient after processing；
Step 4) moving average filtering step, including：Average filter is moved to the signal of reconstruct in step 3, obtains
The baseline drift value of electrocardiosignal；
Step 5) remove baseline drift step, including：Deducted the needle position misalignment value in step 4 by original electro-cardiologic signals,
I.e. obtain the signal after removing baseline drift.
Preferably, step 1) in, when determining the wavelet decomposition number of plies, carry out by following formula：
J=log2(Fs/f)
Wherein：J is the wavelet decomposition number of plies, and Fs is the sample rate of electrocardiosignal, and f is that the effective band of electrocardiosignal is maximum
Value.
Preferably, in step 2 and step 3, the wavelet function carrying out wavelet function feedback employing is Haar function.
Preferably, in step 3, process to by the high frequency coefficient in step 2 and low frequency coefficient, including：
Each for small echo floor height frequency filter factor is set to 0 entirely, i.e.：CA1 '=CA2 '=... ...=CAJ '=0；
Low frequency coefficient keeps constant, CAJ '=CAJ.
Preferably, step 3) in, in signal reconstruction step, take Mallat algorithm.
Preferably, in moving average filtering step, specifically include：
Wherein x [n] is input signal, and y [n] is output signal, and n is positive integer, and the M value taking rolling average algorithm is 500.
After this invention takes such scheme, it is determined that the number of plies of wavelet transformation, need not by wavelet decomposition to ten which floor,
Greatly reduce computation complexity, process the signal of band of interest targetedly, reduce the shadow of other band noises
Ring, improve the quality of signal.
Other features and advantages of the present invention will illustrate in the following description, and, partly become from specification
Obtain it is clear that or understood by implementing the present invention.The purpose of the present invention and other advantages can be by the explanations write
Structure specifically noted in book, claims and accompanying drawing realizes and obtains.
Brief description
Below in conjunction with the accompanying drawings the present invention is described in detail, so that the above-mentioned advantage of the present invention is definitely.Its
In,
Fig. 1 is that the present invention removes in the method for ECG baseline drift s0016lrem number in PTB database
The schematic diagram of two lead signals；
Fig. 2 is that the present invention removes the schematic diagram that wavelet decomposition in the method for ECG baseline drift calculates each layer coefficients；
Fig. 3 is the signal that the present invention removes wavelet reconstruction and moving average filtering in the method for ECG baseline drift
Figure；
Fig. 4 is the schematic diagram of the signal that the present invention removes in the method for ECG baseline drift after reconstruct；
Fig. 5 present invention removes the schematic diagram of baseline drift signal in the method for ECG baseline drift；
Fig. 6 present invention removes the schematic diagram removing signal after baseline drift in the method for ECG baseline drift；
Fig. 7 is letter after the present invention removes in the method for ECG baseline drift patient's I electrocardiosignal and goes baseline drift
Number schematic diagram；
Fig. 8 is letter after the present invention removes in the method for ECG baseline drift patient's II electrocardiosignal and goes baseline drift
Number schematic diagram；
Fig. 9 is after the present invention removes in the method for ECG baseline drift patient's III electrocardiosignal and goes baseline drift
The schematic diagram of signal.
Detailed description of the invention
Describe embodiments of the present invention in detail below with reference to drawings and Examples, whereby how the present invention is applied
Technological means solves technical problem, and reach technique effect realize that process can fully understand and implement according to this.Need explanation
As long as not constituting conflict, each embodiment in the present invention and each feature in each embodiment can be combined with each other,
The technical scheme being formed is all within protection scope of the present invention.
In addition, can be in the department of computer science of such as one group of computer executable instructions in the step shown in the flow chart of accompanying drawing
System performs, and, although show logical order in flow charts, but in some cases, can be to be different from herein
Order perform shown or described step.
Specifically, the present invention is directed to problem present in ECG baseline drift denoising, provide a kind of new
Solution：A kind of method being combined with moving average filtering by wavelet transformation, removes the baseline drift noise of electrocardiosignal.
Comprise the following steps that：
A kind of method removing ECG baseline drift, including：
Step 1) determine wavelet decomposition number of plies step, including：By the effective band of the sample rate of signal and electrocardiosignal,
Determine the number of plies of wavelet decomposition；
Step 2) carry out wavelet decomposition step, including：According to the wavelet decomposition number of plies determining in step 1, sampling small echo
Analyzing and carrying out J layer decomposition to electrocardiosignal, the small echo high frequency coefficient of every layer is respectively：CD1, CD2 ..., CDJ, low frequency coefficient
For：CAJ；
Step 3) wavelet coefficient process and signal reconstruction step, including：Respectively by the high frequency coefficient in step 2 and low frequency system
Number is processed, and carries out wavelet reconstruction according to the wavelet coefficient after processing；
Step 4) moving average filtering step, including：Average filter is moved to the signal of reconstruct in step 3, obtains
The baseline drift value of electrocardiosignal；
Step 5) remove baseline drift step, including：Deducted the needle position misalignment value in step 4 by original electro-cardiologic signals,
I.e. obtain the signal after removing baseline drift.
After this invention takes such scheme, it is determined that the number of plies of wavelet transformation, need not by wavelet decomposition to ten which floor,
Greatly reduce computation complexity, process the signal of band of interest targetedly, reduce the shadow of other band noises
Ring, improve the quality of signal.
Wherein, in further embodiment, it is preferred that step 1) in, when determining the wavelet decomposition number of plies, by such as following formula
Son is carried out：
J=log2(Fs/f)
Wherein：J is the wavelet decomposition number of plies, and Fs is the sample rate of electrocardiosignal, and f is that the effective band of electrocardiosignal is maximum
Value.
Preferably, in step 2 and step 3, the wavelet function carrying out wavelet function feedback employing is Haar function.
Preferably, in step 3, process to by the high frequency coefficient in step 2 and low frequency coefficient, including：
Each for small echo floor height frequency filter factor is set to 0 entirely, i.e.：CA1 '=CA2 '=... ...=CAJ '=0；
Low frequency coefficient keeps constant, CAJ '=CAJ.
Preferably, step 3) in, in signal reconstruction step, take Mallat algorithm.
Preferably, in moving average filtering step, specifically include：
Wherein x [n] is input signal, and y [n] is output signal, and n is positive integer, and the M value taking rolling average algorithm is 500.
Wherein, below in conjunction with the accompanying drawings to the present invention be embodied as be described in further detail.
The present invention have chosen German National Weights and Measures Service ecg database (PTB) data (data number is s0016lrem,
Two lead) as experimental data, signal sampling rate is 1000Hz, sampling time 10s, as shown in Figure 1.The experimental procedure of this method
As follows：
Step 1：Determine the wavelet decomposition number of plies.By the effective band of the sample rate of signal and electrocardiosignal, determine little
The number of plies of Wave Decomposition：
J=log2(Fs/f)
Wherein：J is the wavelet decomposition number of plies, and Fs is the sample rate of electrocardiosignal, and f is that the effective band of electrocardiosignal is maximum
Value.As：In the present invention, the electrocardiosignal sample rate of research is 1000Hz, and the effective band of electrocardiosignal is 0--40Hz, according to formula
(1) can determine that wavelet decomposition layer should be 4 layers.The frequency band comprising in 4th layer of wavelet low frequency coefficient is 0--31.125Hz, contains
The useful electrocardiosignal of major part；
Step 2：Carry out wavelet decomposition.Secondly sample rate 1000Hz according to signal studied in the present invention, uses small echo
Analyzing and carrying out 4 layers of decomposition to electrocardiosignal, in order to improve computational efficiency, the base chosen in the wavelet function feedback of signal is little
Ripple is Haar small echo.The small echo high frequency coefficient of every layer is respectively：CD1, CD2, CD3, CD4, low frequency coefficient is：CA4, decomposable process
See Fig. 2；
Step 3：Wavelet coefficient is processed and signal reconstruction.Small echo High frequency filter coefficient is all set to 0, i.e.：CA1 '=CA2 '=
CA3 '=CA4 '=0.Retain low frequency coefficient, CA4 '=CA4.Calculated by Mallat and carry out small echo according to process wavelet coefficient
Reconstruct.Wherein, wavelet reconstruction is the inverse process of wavelet decomposition, and signal reconstruction flow process is as it is shown on figure 3, signal such as Fig. 4 institute after Chong Gou
Showing, in Fig. 3, target signal filter high-frequency noise, retains most electrocardiosignal information and low-frequency noise, this low-frequency noise master
The baseline drift of electrocardiosignal to be shown as.
Step 4：Moving average filtering.Filtering method the most frequently used in the method Digital Signal Processing of moving average filtering it
One, there is realization simple, the advantages such as amount of calculation is little, excellent result.Setting input signal as x [n], output signal is y [n], moves
Dynamic average filter algorithm is represented by：
Sample rate according to signal in the present invention, the M value taking rolling average algorithm is 500.Before ensureing operation efficiency
Put, be found through experiments, when M value takes 400 600, go baseline drift best results.Reconstruction signal in step 3 is carried out
Moving average filtering, obtains the baseline drift part of electrocardiosignal.
Step 5：Remove baseline drift.Deducted the needle position misalignment in step 4 by original electro-cardiologic signals, i.e. gone
Except the signal after baseline drift, as shown in Figure 5.
The real data containing needle position misalignment is finally used to verify this method.Fig. 7--the initial data in 9 is
The electrocardio of patient I, II, the III with obvious baseline drift that the IMAC1800 electrocardiograph that Zoncare company produces is gathered
Signal, sample rate is 1000Hz, sampling time 9s, passes through-Fig. 7-9 and can be seen that：This method goes baseline drift respond well,
Signal distortion is low.
The frequency band range that first present invention combines electrocardiosignal according to electrocardiosignal sample rate determines number of plies wavelet decomposition layer
Number, carries out J time and decomposes to electrocardiosignal, and the small echo high frequency coefficient of every layer is respectively：CD1, CD2 ..., CDJ, low frequency coefficient is：
CAJ；By small echo high frequency coefficient CD1, the CD2 of every layer ..., CDJ sets to 0, and retains low frequency coefficient CAJ, then carries out wavelet reconstruction, will
The signal obtaining after carrying out wavelet reconstruction, through moving average filter, obtains the baseline drift part of electrocardiosignal, finally leads to
Cross original electro-cardiologic signals and deduct needle position misalignment, i.e. obtain the signal after removing baseline drift.
The remarkable result of this algorithm is：Determine the number of plies of wavelet transformation, need not by wavelet decomposition to ten which floor, greatly
Reduce computation complexity, process the signal of band of interest targetedly, reduce the impact of other band noises, improve
The quality of signal；Additionally, the wavelet basis function chosen during carrying out the wavelet function feedback of signal is Haar small echo
Function, improves operation efficiency.Process to layer coefficients each after wavelet decomposition, it will be apparent that improve efficiency of algorithm.Carry out baseline
During the extraction of drift line, have employed moving average filter, utilize it to realize simple, the advantages such as amount of calculation is little, excellent result.Make
Obtain this method to be applicable to process in real time.Germany's electrocardiogram (ECG) data (PTB) and actual electrocardiogram (ECG) data is finally used to remove base to this method
Line drift performance is verified, result shows that this method goes baseline drift effect good, and distorted signals is low.
It should be noted that for said method embodiment, in order to be briefly described, therefore it is all expressed as a series of
Combination of actions, but those skilled in the art should know, the application is not limited by described sequence of movement because
According to the application, some step can use other orders or carry out simultaneously.Secondly, those skilled in the art also should know
Knowing, embodiment described in this description belongs to preferred embodiment, involved action and module not necessarily the application
Necessary.
Those skilled in the art are it should be appreciated that embodiments herein can be provided as method, system or computer program
Product.Therefore, the application can use complete hardware embodiment, complete software implementation or the reality in terms of combining software and hardware
Execute the form of example.
And, the application can use and can use at one or more computers wherein including computer usable program code
The upper computer program implemented of storage medium (including but not limited to magnetic disc store, CD-ROM, optical memory etc.)
Form.
Finally it should be noted that：The foregoing is only the preferred embodiments of the present invention, be not limited to the present invention,
Although being described in detail the present invention with reference to previous embodiment, for a person skilled in the art, it still may be used
Modify with the technical scheme described in foregoing embodiments, or equivalent is carried out to wherein portion of techniques feature.
All within the spirit and principles in the present invention, any modification, equivalent substitution and improvement etc. made, should be included in the present invention's
Within protection domain.
Claims (6)
1. the method removing ECG baseline drift, it is characterised in that include：
Step 1) determine wavelet decomposition number of plies step, including：By the effective band of the sample rate of signal and electrocardiosignal, determine
Go out the number of plies of wavelet decomposition；
Step 2) carry out wavelet decomposition step, including：According to the wavelet decomposition number of plies determining in step 1, sampling wavelet analysis
Carry out J layer decomposition to electrocardiosignal, the small echo high frequency coefficient of every layer is respectively：CD1, CD2 ..., CDJ, low frequency coefficient is：
CAJ；
Step 3) wavelet coefficient process and signal reconstruction step, including：Respectively the high frequency coefficient in step 2 and low frequency coefficient are entered
Row is processed, and carries out wavelet reconstruction according to the wavelet coefficient after processing；
Step 4) moving average filtering step, including：Average filter is moved to the signal of reconstruct in step 3, obtains electrocardio
The baseline drift value of signal；
Step 5) remove baseline drift step, including：Deducted the needle position misalignment value in step 4 by original electro-cardiologic signals, to obtain final product
Arrive the signal after removing baseline drift.
2. the method for removal ECG baseline drift according to claim 1, it is characterised in that step 1) in, determine
During the wavelet decomposition number of plies, carry out by following formula：
J=log2(Fs/f)
Wherein：J is the wavelet decomposition number of plies, and Fs is the sample rate of electrocardiosignal, and f is the effective band maximum of electrocardiosignal.
3. the method for removal ECG baseline drift according to claim 2, it is characterised in that step 2 and step 3
In, the wavelet function carrying out wavelet function feedback employing is Haar function.
4. the method for removal ECG baseline drift according to claim 1, it is characterised in that in step 3, to by step
High frequency coefficient in rapid 2 and low frequency coefficient are processed, including：
Each for small echo floor height frequency filter factor is set to 0 entirely, i.e.：CA1 '=CA2 '=...=CAJ '=0；
Low frequency coefficient keeps constant, CAJ '=CAJ.
5. the method for removal ECG baseline drift according to claim 1, it is characterised in that step 3) in, signal
In reconstruction step, take Mallat algorithm.
6. the method for removal ECG baseline drift according to claim 1, it is characterised in that moving average filtering walks
In Zhou, specifically include：
Wherein x [n] is input signal, and y [n] is output signal, and n is positive integer, and the M value taking rolling average algorithm is 500.
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| CN111166322A (en) * | 2020-01-17 | 2020-05-19 | 哈尔滨工业大学 | Electrocardiosignal characteristic wave extraction method |
| CN113827253A (en) * | 2020-06-04 | 2021-12-24 | 阿里巴巴集团控股有限公司 | Computing device and method for removing noise from electroencephalogram signals |
| CN113288158A (en) * | 2021-05-27 | 2021-08-24 | 河北省科学院应用数学研究所 | Method, device and equipment for removing baseline drift and high-frequency noise |
| CN113303809A (en) * | 2021-05-27 | 2021-08-27 | 河北省科学院应用数学研究所 | Method, device, equipment and storage medium for removing baseline drift and high-frequency noise |
| CN113288158B (en) * | 2021-05-27 | 2022-12-20 | 河北省科学院应用数学研究所 | Method, device and equipment for removing baseline drift and high-frequency noise |
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