CN105030232A - Baseline drift correction method for electrocardiosignal - Google Patents

Abstract

The invention discloses a baseline drift correction method for electrocardiosignal. The method includes the steps of conducting improved self-adaption noise set empirical mode decomposition on the original electrocardiosignal to obtain intrinsic mode functions and residual components, counting the zero-crossing rates of all the intrinsic mode functions and all the residual components, and removing the intrinsic mode functions with the zero-crossing rates smaller than the set threshold value and the residual components with the zero-crossing rates smaller than the set threshold value from the original electrocardiosignal to obtain the electrocardiosignal where baseline drift is removed. According to the method, by conducting improved self-adaption noise set empirical mode decomposition on the original electrocardiosignal, the mode aliasing phenomenon is eliminated, and residual noise is reduced; the step of conducting self-adaption baseline drift amount selection according to the zero-crossing rates is added, and therefore the problem of the lack of effective baseline drift component selection means in the prior art is solved. The method can be widely applied to the field of baseline drift correction of the electrocardiosignal.

Description

A kind of Base-Line Drift Correction method of electrocardiosignal

Technical field

The present invention relates to signal processing field, especially a kind of Base-Line Drift Correction method of electrocardiosignal.

Background technology

Electrocardiogram is the data record of cardiomotility, has very important effect to clinical medical cardiac health diagnosis.But in the gatherer process of human ecg signal, owing to affecting by the factor of armarium and human body self, the interference being subject to various noise cannot be avoided, as baseline drift, myoelectricity interference and Hz noise etc.Wherein, baseline drift causes primarily of factors such as the respiratory movement of human body and the slips of acquisition electrode, belongs to the intrasonic interfering signal of slowly change.This interference can raise Electrocardiographic ST wave band, causes the serious distortion of electrocardio track, thus have impact on normal medical diagnosis.Therefore, pretreatment is carried out to electrocardiogram (ECG) data, eliminate baseline drift, significant.

At present, common ECG baseline drift bearing calibration mainly comprises the Empirical mode decomposition grown up median filtering method, Wavelet Transform and recent years.Although the mode that application medium filtering removes baseline drift has the advantage of the little and speed of amount of calculation, it can produce the distortion of " stepped ", and precision is lower.Application wavelet transformation need adopt the approximation component approximate evaluation baseline drift signal of high yardstick when removing baseline drift, now need the number of plies selecting the wavelet function Sum decomposition be applicable to, and the wavelet function Sum decomposition number of plies is comparatively large on result impact, complicated operation is easy not.And application experience mode decomposition method removes the puzzlement that baseline drift is subject to modal overlap often, decomposition result is made to lose physical significance, and it lacks effective baseline drift component and chooses means, be difficult to the baseline drift component choosing needs removing after empirical mode decomposition terminates.

In sum, current existing ECG baseline drift bearing calibration all Shortcomings.

Summary of the invention

In order to solve the problems of the technologies described above, the object of the invention is: provide a kind of without modal overlap phenomenon with self adaptation can choose baseline drift component, the Base-Line Drift Correction method of electrocardiosignal.

The technical solution adopted for the present invention to solve the technical problems is:

A Base-Line Drift Correction method for electrocardiosignal, comprising:

A, the adaptive noise set empirical mode decomposition process improved original electro-cardiologic signals, obtain intrinsic mode function and residual components;

B, add up all intrinsic mode functions and the zero-crossing rate of residual components;

C, intrinsic mode function zero-crossing rate being less than setting threshold value and residual components are got rid of from original electro-cardiologic signals, obtain the electrocardiosignal after removing baseline drift.

Further, described steps A, it comprises:

A1, positive white noise n is added to primary signal ecg (t) ⁱ(t) and negative white noise-n ⁱt (), obtains signal S (t) to be decomposed, the expression formula of described signal S (t) to be decomposed is:

S(t)＝ecg(t)+(-1) ^qa ₀n ⁱ(t)，

Wherein, i=1,2 ..., M/2, M are the number of times of ensemble average, a ₀for the amplitude of institute's plus noise, n ⁱt () is added i-th noise, q gets i-th negative white noise-a that 1 interval scale adds ₀n ⁱt (), q gets i-th positive white noise a that 2 interval scales add ₀n ⁱ(t);

A2, empirical mode decomposition and ensemble average are carried out to signal S (t) to be decomposed, obtain average after the first rank intrinsic mode function and corresponding residual components;

A3, the adaptive noise positive and negative to residual components continuation interpolation, then proceed empirical mode decomposition and ensemble average to the component after the positive and negative adaptive noise of interpolation, until obtain all rank intrinsic mode function on average and final residual components.

Further, described steps A 2, it comprises:

A21, empirical mode decomposition is carried out to signal S (t) to be decomposed, obtain the first rank intrinsic mode function;

A22, ensemble average is carried out to the first rank intrinsic mode function, obtain average after the first rank intrinsic mode function;

A23, the first rank intrinsic mode function from primary signal ecg (t) after Trimmed mean, thus obtain the first rank residual components;

A24, continue as the first rank residual components and add positive and negative adaptive noise (-1) ^qa _kn ⁱ(t), wherein, k=1,2 ..., M/2, a _kfor the amplitude of kth rank residual components institute plus noise, then return steps A 21 as new signal S (t) to be decomposed, until obtain intrinsic mode function and the residual components on all rank using the signal after adding positive and negative adaptive noise.

Further, described steps A 21, it comprises:

A211, employing spline method obtain the coenvelope e of signal S (t) to be decomposed _max(t) and lower envelope e _min(t);

A212, obtain coenvelope e _max(t) and lower envelope e _minthe average M (t) of (t), then obtain residual components C (t) after rejecting average M (t) from signal S (t), the expression formula of described average M (t) and residual components C (t) is respectively: M (t)=[e _max(t)+e _min(t)]/2, C (t)=S (t)-M (t);

A213, return step B11 using residual components C (t) as new S (t) signal, until the stop condition that residual components C (t) meets setting just stops iterative process, obtain the first rank intrinsic mode function imf ₁(t).

Further, described steps A 211, it is specially:

Extract the extreme point of signal S (t) to be decomposed, then obtained the coenvelope e of signal S (t) by cubic spline interpolation _max(t) and lower envelope e _min(t), wherein, the coenvelope e of signal S (t) _maxt () is obtained by the maximum matching of signal S (t), the lower envelope of signal S (t) is obtained by the minimum matching of signal S (t).

Further, estimation function p (t) that the stop condition that described residual components C (t) meets setting refers to new S (t) signal at any time t meets p (t) < θ ₂, and p (t) < θ ₁moment account for the ratio in total moment and be more than or equal to 1-λ, the computing formula of described estimation function p (t) is:

$\left\{\begin{array}{c}p\left(t\right)=\left|\frac{M\left(t\right)}{a\left(t\right)}\right|\\ a\left(t\right)=\frac{e_{max}\left(t\right)-e_{min}\left(t\right)}{2}\end{array}\right.,$

Wherein, θ ₁, θ ₂be default threshold value with λ, a (t) is modal amplitudes.

Further, described step C, it is specially:

The intrinsic mode function and the residual components that zero-crossing rate in all intrinsic mode functions after electrocardiosignal decomposition and residual components are less than 1.5 are got rid of from original electro-cardiologic signals, obtain the electrocardiosignal after removing baseline drift.

The invention has the beneficial effects as follows: based on adaptive noise set empirical mode decomposition and zero-crossing rate self adaptation baseline drift value choosing method, to the adaptive noise set empirical mode decomposition process that original electro-cardiologic signals improves, eliminate the modal overlap phenomenon of existing set empirical mode decomposition process, reduce residual noise; Add and carry out according to zero-crossing rate the step that self adaptation baseline drift value chooses, the setting intrinsic mode function of threshold value and residual components is less than as baseline drift signal using zero-crossing rate, directly remove from former electrocardiosignal, finally realize the Base-Line Drift Correction of electrocardiosignal, solve prior art and lack the problem that effective baseline drift component chooses means.Further, primary signal be with the addition of to the positive and negative adaptive noise occurred in pairs, eliminate the modal overlap phenomenon of existing set empirical mode decomposition process, reduce residual noise.

Accompanying drawing explanation

Below in conjunction with drawings and Examples, the invention will be further described.

Fig. 1 is the overall flow figure of the Base-Line Drift Correction method of a kind of electrocardiosignal of the present invention;

Fig. 2 is the flow chart of steps A of the present invention;

Fig. 3 is the flow chart of steps A 2 of the present invention;

Fig. 4 is the flow chart of steps A 21 of the present invention;

Fig. 5 is the electrocardiosignal time domain beamformer containing baseline drift in the invention process example two;

Fig. 6 is 10 intrinsic mode functions and a residual components figure that obtain after adaptive noise set empirical mode decomposition containing the electrocardiosignal of baseline drift in the embodiment of the present invention two;

Fig. 7 is baseline drift signal and the original electro-cardiologic signals time domain waveform comparison diagram of the embodiment of the present invention two extraction;

Fig. 8 is the electrocardiosignal time domain beamformer after the embodiment of the present invention two Base-Line Drift Correction.

Detailed description of the invention

With reference to Fig. 1, a kind of Base-Line Drift Correction method of electrocardiosignal, comprising:

A, the adaptive noise set empirical mode decomposition process improved original electro-cardiologic signals, obtain intrinsic mode function and residual components;

B, add up all intrinsic mode functions and the zero-crossing rate of residual components;

C, intrinsic mode function zero-crossing rate being less than setting threshold value and residual components are got rid of from original electro-cardiologic signals, obtain the electrocardiosignal after removing baseline drift.

Wherein, zero-crossing rate refers to the zeroaxial number of times of signal amplitude in the unit interval.

With reference to Fig. 2, be further used as preferred embodiment, described steps A, it comprises:

A1, positive white noise n is added to primary signal ecg (t) ⁱ(t) and negative white noise-n ⁱt (), obtains signal S (t) to be decomposed, the expression formula of described signal S (t) to be decomposed is:

S(t)＝ecg(t)+(-1) ^qa ₀n ⁱ(t)，

Wherein, i=1,2 ..., M/2, M are the number of times of ensemble average, a ₀for the amplitude of institute's plus noise, n ⁱt () is added i-th noise, q gets i-th negative white noise-a that 1 interval scale adds ₀n ⁱt (), q gets i-th positive white noise a that 2 interval scales add ₀n ⁱ(t);

A2, empirical mode decomposition and ensemble average are carried out to signal S (t) to be decomposed, obtain average after the first rank intrinsic mode function and corresponding residual components;

A3, the adaptive noise positive and negative to residual components continuation interpolation, then proceed empirical mode decomposition and ensemble average to the component after the positive and negative adaptive noise of interpolation, until obtain all rank intrinsic mode function on average and final residual components.

With reference to Fig. 3, be further used as preferred embodiment, described steps A 2, it comprises:

A21, empirical mode decomposition is carried out to signal S (t) to be decomposed, obtain the first rank intrinsic mode function;

A22, ensemble average is carried out to the first rank intrinsic mode function, obtain average after the first rank intrinsic mode function;

A23, the first rank intrinsic mode function from primary signal ecg (t) after Trimmed mean, thus obtain the first rank residual components;

A24, continue as the first rank residual components and add positive and negative adaptive noise (-1) ^qa _kn ⁱ(t), wherein, k=1,2 ..., M/2, a _kfor the amplitude of kth rank residual components institute plus noise, then return steps A 21 as new signal S (t) to be decomposed, until obtain intrinsic mode function and the residual components on all rank using the signal after adding positive and negative adaptive noise.

With reference to Fig. 4, be further used as preferred embodiment, described steps A 21, it comprises:

A211, employing spline method obtain the coenvelope e of signal S (t) to be decomposed _max(t) and lower envelope e _min(t);

A212, obtain coenvelope e _max(t) and lower envelope e _minthe average M (t) of (t), then obtain residual components C (t) after rejecting average M (t) from signal S (t), the expression formula of described average M (t) and residual components C (t) is respectively: M (t)=[e _max(t)+e _min(t)]/2, C (t)=S (t)-M (t);

A213, return step B11 using residual components C (t) as new S (t) signal, until the stop condition that residual components C (t) meets setting just stops iterative process, obtain the first rank intrinsic mode function imf ₁(t).

Be further used as preferred embodiment, described steps A 211, it is specially:

Extract the extreme point of signal S (t) to be decomposed, then obtained the coenvelope e of signal S (t) by cubic spline interpolation _max(t) and lower envelope e _min(t), wherein, the coenvelope e of signal S (t) _maxt () is obtained by the maximum matching of signal S (t), the lower envelope of signal S (t) is obtained by the minimum matching of signal S (t).

Be further used as preferred embodiment, estimation function p (t) that the stop condition that described residual components C (t) meets setting refers to new S (t) signal at any time t meets p (t) < θ ₂, and p (t) < θ ₁moment account for the ratio in total moment and be more than or equal to 1-λ, the computing formula of described estimation function p (t) is:

$\left\{\begin{array}{c}p\left(t\right)=\left|\frac{M\left(t\right)}{a\left(t\right)}\right|\\ a\left(t\right)=\frac{e_{max}\left(t\right)-e_{min}\left(t\right)}{2}\end{array}\right.,$

Wherein, θ ₁, θ ₂be default threshold value with λ, a (t) is modal amplitudes.θ ₁generally get 0.05, θ ₂generally get 0.5, λ and generally get 0.05.When the value of p (t) meets the stop condition established by these three thresholdings, stop iterative process.

Be further used as preferred embodiment, described step C, it is specially:

The intrinsic mode function and the residual components that zero-crossing rate in all intrinsic mode functions after electrocardiosignal decomposition and residual components are less than 1.5 are got rid of from original electro-cardiologic signals, obtain the electrocardiosignal after removing baseline drift.

Below in conjunction with Figure of description and specific embodiment, the present invention is described in further detail.

Embodiment one

The present embodiment is described correlation theory involved in the present invention and specific implementation process.

When the present invention carries out empirical mode decomposition and ensemble average to original electro-cardiologic signals ECG, a series of intrinsic mode function can be obtained, wherein, the high-frequency signal of small scale is first separated, decompose out after the low frequency signal of large scale, namely the frequency of intrinsic mode function roughly reduces from high to low gradually by the order filtered out.And electrocardio baseline drift signal belongs to the low frequency signal of slowly change, Gu Qihui is broken down in last several intrinsic mode function.By the zero-crossing rate of intrinsic mode function, the frequency of intrinsic mode function can be estimated roughly.The intrinsic mode function that zero-crossing rate is less than 1.5 by the present embodiment thinks baseline drift component, directly rejects from original signal, can realize the suppression of ECG baseline drift.

Defining operation E () represents empirical mode decomposition, and the detailed process of operation E () is:

1) extract the extreme point (comprising minimum point and maximum point) of signal S (t), obtain coenvelope e with cubic spline interpolation respectively _max(t) and lower envelope e _mint (), wherein, coenvelope is obtained by maximum matching, and lower envelope is obtained by minimum matching.

2) obtain the average M (t) of lower envelope, and rejecting average M (t) obtains residual components C (t), that is: C (t)=S (t)-M (t) from signal S (t).

3) using residual components C (t) as new S (t) signal, repeat step 1), 2), until C (t) meet setting stop condition just stop iterative process, obtain the first rank intrinsic mode function imf ₁(t).Wherein, estimation function p (t) that the stop condition that residual components C (t) meets setting refers to new S (t) signal at any time t meets p (t) < θ ₂, and p (t) < θ ₁moment account for the ratio in total moment and be more than or equal to 1-λ, the computing formula of estimation function p (t) is:

$\left\{\begin{array}{c}p\left(t\right)=\left|\frac{M\left(t\right)}{a\left(t\right)}\right|\\ a\left(t\right)=\frac{e_{max}\left(t\right)-e_{min}\left(t\right)}{2}\end{array}\right.---\left(1\right)$

4) from signal S (t), the first rank intrinsic mode function imf is extracted ₁t (), even r (t)=S (t)-imf ₁t (), then to extract the residual components r (t) after the first rank intrinsic mode function as new S (t) signal, repeats step 1)-3) until extract all intrinsic mode functions.

If operation E _kthe kth rank intrinsic mode function that () representation signal obtains after empirical mode decomposition, a _kfor the amplitude of kth rank institute plus noise, a _k0.1 ~ 0.4 times of the general number of winning the confidence standard deviation, the positive and negative noise logarithm M/2 of interpolation often gets 100-500 time, suitably can adjust according to concrete applicable cases; Below specific embodiment of the invention method is described in detail:

Step 1: first positive white noise n is added to primary signal ecg (t) ⁱ(t) and negative white noise-n ⁱt (), obtains signal S (t) to be decomposed, i.e. S (t)=ecg (t)+(-1) ^qa ₀n ⁱ(t).

Then an E is carried out to signal S (t) to be decomposed ₁() operates, and to M intrinsic mode function carry out ensemble average, obtain the first rank intrinsic mode function on average

${\stackrel{\&OverBar;}{imf}}_1\left(t\right)=\frac{1}{M}\underset{i=1}{\overset{M}{\&Sigma;}}{\mathrm{imf}}_1^i\left(t\right)---\left(2\right)$

Step 2: extract the first rank intrinsic mode function on average from primary signal ecg (t) and obtain corresponding residual components r ₁(t):

$r_1\left(t\right)=ecg\left(t\right)-{\stackrel{\&OverBar;}{imf}}_1\left(t\right)---\left(3\right)$

Step 3: with residual components r ₁t () adds the positive and negative adaptive noise (-1) of empirical mode decomposition as new signal S (t) ^qa ₁e ₁(n ⁱ(t)), again perform an E ₁() operates, and to M intrinsic mode function the second-order intrinsic mode function is on average obtained after carrying out ensemble average:

${\stackrel{\&OverBar;}{imf}}_2\left(t\right)=\frac{1}{M}\underset{i=1}{\overset{M}{\&Sigma;}}{\mathrm{imf}}_2^i\left(t\right)---\left(4\right)$

Step 4: obtain second-order residual components r ₂(t):

$r_2\left(t\right)=r_1\left(t\right)-{\stackrel{\&OverBar;}{imf}}_2\left(t\right)---\left(5\right)$

Step 5: be second-order residual components r ₂t () continues the positive and negative noise component(s) noise (-1) of adding empirical mode decomposition ^qa ₂e ₂(n ⁱ(t)), again perform an E ₁() operates, and repeats the operation of step 3 and 4, can obtain kth rank residual components r _k(t), wherein k=1,2 ..., continue the positive and negative noise component(s) r adding empirical mode decomposition _k(t)+(-1) ^qa _ke _k(n ⁱ(t)), again perform an E ₁() operates, and obtains the intrinsic mode function after kth+1 ensemble average:

${\stackrel{\&OverBar;}{imf}}_{k+1}\left(t\right)=\frac{1}{M}\underset{i=1}{\overset{M}{\&Sigma;}}{\mathrm{imf}}_{k+1}^i\left(t\right)---\left(6\right)$

Step 6: circulation step 5, obtains residual components R (t) after extracting all intrinsic mode functions.

Step 7: the zero-crossing rate adding up all intrinsic mode functions and residual components, and the intrinsic mode function that zero-crossing rate is less than threshold value 1.5 is rejected from original signal ecg (t).

Embodiment two

With reference to Fig. 5-8, the second embodiment of the present invention:

The present embodiment selects the electrocardiogram (ECG) data of in MIT-BIHNormalSinusRhythmDatabase data base No. 18,177 first, as shown in Figure 5.From time its domain waveform figure can find out this signal obviously containing baseline drift.The specific implementation process of this signal being carried out to Base-Line Drift Correction is as follows:

The step 1-step 7 of the application embodiment of the present invention one, carries out empirical mode decomposition and ensemble average to this signal and removes baseline drift.Wherein, added noise amplitude a _kbe 0.4 times of signal standards difference to be decomposed, adding positive and negative noise logarithm is 500, and obtain 10 modal components (IMF, i.e. intrinsic mode function) and a residual components (RES) after decomposition, result as shown in Figure 6.Calculate zero-crossing rate (ZCR) important in accompanying drawing 6, acquired results is as shown in table 1.

The statistics of each component zero-crossing rate of table 1

IMF	IMF1	IMF2	IMF3	IMF4	IMF5	IMF6
							ZCR	73.4	50	27.8	14.8	11.2	4.5
IMF	IMF7	IMF8	IMF9	IMF10	RES
							ZCR	3.7	0.8	0.5	0.2	0.1

As can be seen from Table 1, modal components IMF8-IMF10 and residual components (RES) zero-crossing rate are all less than 1.5, therefore these four components are baseline drift signal, as shown in dotted line in accompanying drawing 7.Remove these four baseline drift signals from primary signal ECG after, obtain the ECG signal after correcting, as shown in Figure 8.

The present invention eliminates modal overlap by adding the positive and negative adaptive noise occurred in pairs and reduces residual noise in catabolic process.Electrocardiosignal obtains a series of intrinsic mode function and a residual components after adaptive noise set empirical mode decomposition, then the zero-crossing rate of each intrinsic mode function and residual components is calculated, zero-crossing rate is less than the component of threshold value 1.5 as baseline drift signal, directly remove from former electrocardiosignal, finally realize ECG baseline drift and correct.The present invention can realize the adaptive decomposition of electrocardiosignal by adding positive and negative adaptive noise, solve the modal overlap phenomenon existed in Conventional wisdom mode decomposition; And choose intrinsic mode function by zero-crossing rate self adaptation, extract baseline drift signal adaptively, effectively achieve the correction of ECG baseline drift.

More than that better enforcement of the present invention is illustrated, but the invention is not limited to described embodiment, those of ordinary skill in the art also can make all equivalent variations or replacement under the prerequisite without prejudice to spirit of the present invention, and these equivalent distortion or replacement are all included in the application's claim limited range.

Claims (7)

1. a Base-Line Drift Correction method for electrocardiosignal, is characterized in that: comprising:

A, the adaptive noise set empirical mode decomposition process improved original electro-cardiologic signals, obtain intrinsic mode function and residual components;

B, add up all intrinsic mode functions and the zero-crossing rate of residual components;

C, intrinsic mode function zero-crossing rate being less than setting threshold value and residual components are got rid of from original electro-cardiologic signals, obtain the electrocardiosignal after removing baseline drift.

2. the Base-Line Drift Correction method of a kind of electrocardiosignal according to claim 1, it is characterized in that: described steps A, it comprises:

A1, positive white noise n is added to primary signal ecg (t) ⁱ(t) and negative white noise-n ⁱt (), obtains signal S (t) to be decomposed, the expression formula of described signal S (t) to be decomposed is:

S(t)＝ecg(t)+(-1) ^qa ₀n ⁱ(t)，

Wherein, i=1,2 ..., M/2, M are the number of times of ensemble average, a ₀for the amplitude of institute's plus noise, n ⁱt () is added i-th noise, q gets i-th negative white noise-a that 1 interval scale adds ₀n ⁱt (), q gets i-th positive white noise a that 2 interval scales add ₀n ⁱ(t);

A2, empirical mode decomposition and ensemble average are carried out to signal S (t) to be decomposed, obtain average after the first rank intrinsic mode function and corresponding residual components;

A3, the adaptive noise positive and negative to residual components continuation interpolation, then proceed empirical mode decomposition and ensemble average to the component after the positive and negative adaptive noise of interpolation, until obtain all rank intrinsic mode function on average and final residual components.

3. the Base-Line Drift Correction method of a kind of electrocardiosignal according to claim 2, is characterized in that: described steps A 2, and it comprises:

A21, empirical mode decomposition is carried out to signal S (t) to be decomposed, obtain the first rank intrinsic mode function;

A22, ensemble average is carried out to the first rank intrinsic mode function, obtain average after the first rank intrinsic mode function;

A23, the first rank intrinsic mode function from primary signal ecg (t) after Trimmed mean, thus obtain the first rank residual components;

A24, continue as the first rank residual components and add positive and negative adaptive noise (-1) ^qa _kn ⁱ(t), wherein, k=1,2 ..., M/2, a _kfor the amplitude of kth rank residual components institute plus noise, then return steps A 21 as new signal S (t) to be decomposed, until obtain intrinsic mode function and the residual components on all rank using the signal after adding positive and negative adaptive noise.

4. the Base-Line Drift Correction method of a kind of electrocardiosignal according to claim 3, is characterized in that: described steps A 21, and it comprises:

A211, employing spline method obtain the coenvelope e of signal S (t) to be decomposed _max(t) and lower envelope e _min(t);

A212, obtain coenvelope e _max(t) and lower envelope e _minthe average M (t) of (t), then obtain residual components C (t) after rejecting average M (t) from signal S (t), the expression formula of described average M (t) and residual components C (t) is respectively: M (t)=[e _max(t)+e _min(t)]/2, C (t)=S (t)-M (t);

A213, return step B11 using residual components C (t) as new S (t) signal, until the stop condition that residual components C (t) meets setting just stops iterative process, obtain the first rank intrinsic mode function imf ₁(t).

5. the Base-Line Drift Correction method of a kind of electrocardiosignal according to claim 4, is characterized in that: described steps A 211, and it is specially:

Extract the extreme point of signal S (t) to be decomposed, then obtained the coenvelope e of signal S (t) by cubic spline interpolation _max(t) and lower envelope e _min(t), wherein, the coenvelope e of signal S (t) _maxt () is obtained by the maximum matching of signal S (t), the lower envelope of signal S (t) is obtained by the minimum matching of signal S (t).

6. the Base-Line Drift Correction method of a kind of electrocardiosignal according to claim 5, is characterized in that: estimation function p (t) that the stop condition that described residual components C (t) meets setting refers to new S (t) signal at any time t meets p (t) < θ ₂, and p (t) < θ ₁moment account for the ratio in total moment and be more than or equal to 1-λ, the computing formula of described estimation function p (t) is:

$\left\{\begin{array}{c}p\left(t\right)=\left|\frac{M\left(t\right)}{a\left(t\right)}\right|\\ a\left(t\right)=\frac{e_{max}\left(t\right)-e_{min}\left(t\right)}{2}\end{array}\right.$

Wherein, θ ₁, θ ₂be default threshold value with λ, a (t) is modal amplitudes.

7. the Base-Line Drift Correction method of a kind of electrocardiosignal according to any one of claim 1-6, is characterized in that: described step C, and it is specially:

The intrinsic mode function and the residual components that zero-crossing rate in all intrinsic mode functions after electrocardiosignal decomposition and residual components are less than 1.5 are got rid of from original electro-cardiologic signals, obtain the electrocardiosignal after removing baseline drift.