CN106344006B - J wave detecting methods based on pole symmetric mode decomposition and support vector machines - Google Patents
J wave detecting methods based on pole symmetric mode decomposition and support vector machines Download PDFInfo
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Abstract
The present invention relates to a kind of algorithms based on J waves in pole symmetric mode decomposition and supporting vector machine testing electrocardiosignal, it is widely used in the electrocardiosignal of auxiliary diagnosis patient with the presence or absence of J waves, it is the important evidence of J wave syndrome diagnosis, specially a kind of J wave detecting methods based on pole symmetric mode decomposition and support vector machines.J waves terminate and the pause and transition in rhythm or melody after the binding site J points of ST sections of startings for electrocardiosignal QRS complex.The J waves syndrome that abnormal J wave causes can lead to the high-risk disease such as malignant ventricular arrhythmia, Sudden Cardiac Death, increasingly be taken seriously on clinical medicine.The method that the present invention uses pole symmetric mode decomposition, effectively solves the problems such as modal overlap existing for empirical mode decomposition, a series of intrinsic mode functions and a trend term will be decomposed into ECG signal, and instantaneous frequency feature and energy conversion feature are extracted, being input to support vector machines by principal component analysis dimensionality reduction carries out J wave detections.
Description
Technical field
The present invention relates to a kind of calculations based on J waves in pole symmetric mode decomposition and supporting vector machine testing electrocardiosignal
Method, be widely used in the electrocardiosignal of auxiliary diagnosis patient be with the presence or absence of J waves J wave syndrome diagnosis it is important according to
According to specially a kind of J wave detecting methods based on pole symmetric mode decomposition and support vector machines.
Background technology
J points terminate the binding site with ST sections of startings for QRS wave on electrocardiosignal, indicate end and the multipole of sequences of ventricular depolarization
Beginning.Pause and transition in rhythm or melody after J points is J waves, is a kind of variation of common normal ECG.The J waves caused by abnormal J wave are comprehensive
The symptoms such as malignant ventricular arrhythmia, sudden cardiac death can be led to by closing disease, increasingly by the attention of medical field and concern.Accurately
J waves are detected, can effectively reduce incidence and lethality, there is great importance to clinic.
Since electrocardiosignal is nonlinear and non local boundary value problem, in the research process of previous J waves diagnosis, mainly have in Fu a.
Leaf transformation is changed by frequency content caused by study of disease, achieves certain success, but frequency domain analysis can only obtain entirely
Office's frequency information, and electrocardiosignal is nonlinear and non local boundary value problem, the diagnosis of J waves needs to study its local frequency information.B. it is small
Wave conversion has good effect in electrocardiosignal identification classification, but the defects of wavelet transformation be it is non-adaptive, performance it is good
The bad selection for depending critically upon wavelet function.C. Hilbert-Huang transform is the more popular method of current detection J Bobbis.Its
In, empirical mode decomposition is the important part of Hilbert-Huang transform, is Huang E (N.E.Huang) in National Aeronautics and Space Administration
A kind of NEW ADAPTIVE signal time frequency processing method creatively proposed in 1998 with other people, it is according to signal itself
Fluctuation pattern by signal decomposition be different time scales component from low to high, it is more more suitable than time and frequency domain analysis method
Close the research of nonlinear and non local boundary value problem.Just because of it is such the characteristics of, Hilbert-Huang transform method is successfully applied to electrocardio
Research in terms of signal medical diagnosis on disease.But it is had the following problems using Hilbert-Huang transform to detect J waves：It is difficult to screen number
To determine, the trend function decomposited is too rough, and Hilbert spectral analysis is limited by mathematical theory.It is asked the present invention is based on above-mentioned
Topic, studies electrocardiosignal using pole symmetric mode decomposition method, and then extracts validity feature, passes through support vector machines
J waves are detected, so as to improve the Detection accuracy of J waves.
Invention content
The present invention to solve the above-mentioned problems, is provided based on pole symmetric mode decomposition and the inspection of the J waves of support vector machines
Survey method.
The present invention adopts the following technical scheme that realization：Inspection based on pole symmetric mode decomposition and support vector machines
The method of J waves, includes the following steps in thought-read electric signal：
The first step：Original electro-cardiologic signals x (t) is obtained, original electro-cardiologic signals x (t) includes normal electrocardiosignal and wave containing J
Electrocardiosignal, find out all maximum points of each original electro-cardiologic signals x (t) and minimum point, be denoted as Pi, 1≤i≤n；
Second step：With line segment by all extreme point PiIt is sequentially connected, the midpoint of every section of line segment is labeled as Ti, 1≤i≤n-
1, and connect the total segment left and right ends to be formed in line segment and replenish boundary midpoint T0And Tn；
Third walks：S interpolat curve L is built using n+1 midpoint1,…,Ls, s >=1 calculates its average value L*=(L1
+…LS)/s；
4th step：To x (t)-L*Sequence repeats above three step, until | L*|≤ε or screening number reach preset
Limiting value K, ε are allowable errors, obtain first intrinsic mode function c1(t)；
5th step：To residue sequence x (t)-c1Aforementioned four step is repeated, until residual term r is single signal or no longer
More than previously given extreme point, intrinsic mode function c can be respectively obtained2(t),c3(t)…,ci(t),cm(t), sequence is calculated
Arrange the variances sigma of x (t)-r2；
6th step：Limiting section [Kmin,Kmax] in change limiting value K, repeat above-mentioned five steps, and to σ/σ0And K
It draws, σ0It is the standard deviation of x (t), σ/σ is found out in figure0The corresponding K of minimum value0, with K0It is weighed again as restrictive condition
Multiple above-mentioned five steps, last residual term r are exactly the adaptive equal line of the overall situation of original signal x (t), that is, trend term, through excessive
Solution, original electro-cardiologic signals x (t) are represented byPole symmetric mode decomposition is utilized by electrocardiosignal
X (t) resolves into a series of intrinsic mode functions and a residual term；
7th step：(1) intrinsic mode function c is foundi(t) extreme point calculates two adjacent maximum points and two phases
Time difference between adjacent minimum point；
(2) by these time differences be considered as local period value be assigned to two adjacent maximum points and two adjacent minimum points it
Between midpoint on time shaft, drawing the time cycle corresponds to point diagram；
(3) its local frequencies is obtained, then do cubic spline interpolation and obtain instantaneous frequency by these local period values are inverted
Curve；
8th step：The corresponding analytical expression of i-th of intrinsic mode function is：
9th step：Choose instantaneous frequency F1, F2, F3, F4, F5, F6, F7 of the first seven intrinsic mode function and energy conversion
Feature E (t) composition characteristics space [F1, F2, F3, F4, F5, F6, F7, E (t)], select Principal Component Analysis to feature space into
Row dimensionality reduction chooses preceding 8 principal components of each feature, i.e., the feature after dimensionality reduction amounts to 64；
Tenth step：Features described above is input to support vector machines, support vector machines is trained, extracts the heart to be detected
Feature after the dimensionality reduction of electric signal is input to support vector machines, you can tells the type of electrocardiosignal to be detected.
The present invention is non-linear non-stationary property using electrocardiosignal, and electrocardiosignal is carried out pole symmetric mode decomposition,
Not the problems such as end condition is not known in solution empirical mode decomposition, modal overlap, the instantaneous frequency acquired and energy variation is special
Sign carries out principal component analysis dimensionality reduction, reduces operand, and then the electrocardiosignal of the wave containing J is detected using support vector machines, improves
Detection accuracy assists doctor's Accurate Diagnosis J wave syndromes.
Specific embodiment
J wave detecting methods based on pole symmetric mode decomposition and support vector machines, include the following steps：
The first step：Original electro-cardiologic signals x (t) is obtained, original electro-cardiologic signals x (t) includes normal electrocardiosignal and wave containing J
Electrocardiosignal, find out all maximum points of each original electro-cardiologic signals x (t) and minimum point, be denoted as Pi, 1≤i≤n；
Second step：With line segment by all extreme point PiIt is sequentially connected, the midpoint of every section of line segment is labeled as Ti(1≤i≤n-
1), and the total segment left and right ends to be formed are connected in line segment and replenishes boundary midpoint T0And Tn；
Third walks：S interpolat curve L is built using n+1 midpoint1,…,Ls, s >=1 calculates its average value L*=(L1
+…LS)/s；
4th step：To x (t)-L*Sequence repeats above three step, until | L*|≤ε or screening number reach preset
Limiting value K, ε are allowable errors, obtain first intrinsic mode function c1(t)；
5th step：To residue sequence x (t)-c1Aforementioned four step is repeated, until residual term r is single signal or no longer
More than previously given extreme point, intrinsic mode function c can be respectively obtained2(t),c3(t)…,ci(t),cm(t), sequence is calculated
Arrange the variances sigma of x (t)-r2；
6th step：Limiting section [Kmin,Kmax] in change limiting value K, repeat above-mentioned five steps, and to σ/σ0And K
It draws, σ0It is the standard deviation of x (t), σ/σ is found out in figure0The corresponding K of minimum value0, with K0It is weighed again as restrictive condition
Multiple above-mentioned five steps, last residual term r are exactly the adaptive equal line of the overall situation of original signal x (t), that is, trend term, through excessive
Solution, original electro-cardiologic signals x (t) can be representedPole symmetric mode decomposition is utilized by electrocardio
Signal x (t) resolves into a series of intrinsic mode functions and a residual term；Compared to Hilbert-Huang transform, electrocardio letter is decomposed
During number, pole symmetric mode decomposition will determine three rank samples of original signal outer envelope line in empirical mode decomposition
Interpolation method is improved to inherent pole Symmetric Interpolation method, and borrows " least square method " thought to optimize last remaining mode, makes
It becomes " adaptive global equal line " in entire signal sequence decomposable process, and the best screening in decomposable process is determined with this
Number.Hilbert spectral analysis method has also been abandoned, using direct differential technique, has been solved well：Period is needed relative to one
The section time defines and frequency will have instantaneous meaning.
7th step：Similar with Hilbert-Huang transform, pole symmetric mode decomposition is also classified into two parts：First part be through
Mode decomposition is tested, several intrinsic mode functions and the global equal line of an optimal self-adaptive can be generated；Second part is time frequency analysis,
Relate generally to " direct interpolation " of instantaneous frequency and energy variation problem.By intrinsic mode function each to electrocardiosignal into
Row time frequency analysis extracts instantaneous frequency feature and energy conversion feature.
(1) intrinsic mode function c is foundi(t) extreme point calculates the adjacent maximum point of any two and two consecutive roots
Time difference between small value point；
(2) by these time differences be considered as local period value be assigned to two adjacent maximum points and two adjacent minimum points it
Between midpoint on time shaft, drawing the time cycle corresponds to point diagram；
(3) its local frequencies is obtained, then do cubic spline interpolation and obtain instantaneous frequency by these local period values are inverted
Curve；
8th step：Since the frequency and amplitude of each intrinsic mode function change over time, so this method is put
The tradition spectrum analysis means (Fourier spectrum and Hilbert spectrum) used by energy premised on constant are abandoned, energy variation
Feature is as research object.The corresponding analytical expression of i-th of intrinsic mode function is：ci(t)=Ai(t)cosθi(t),(1≤
I≤m), then its amplitude function A under pole symmetrici(t) there is gradual feature,
9th step：Choose instantaneous frequency F1, F2, F3, F4, F5, F6, F7 of the first seven intrinsic mode function and energy conversion
Feature E (t) composition characteristics space [F1, F2, F3, F4, F5, F6, F7, E (t)], select Principal Component Analysis to feature space into
Row dimensionality reduction chooses preceding 8 principal components of each feature, i.e., the feature after dimensionality reduction amounts to 64；
Tenth step：Features described above is input to support vector machines, support vector machines is trained, extracts the heart to be detected
Feature after the dimensionality reduction of electric signal is input to support vector machines, you can tells the type of electrocardiosignal to be detected.
Principal Component Analysis is a kind of statistical method of dimensionality reduction, it is relevant by its component by means of an orthogonal transformation
Former random vector is converted to the incoherent new random vector of its component, this is shown as on algebraically by the covariance of former random vector
Battle array is transformed into diagonal form battle array, is geometrically showing as, by the orthogonal coordinate system of former coordinate system transformation Cheng Xin, being allowed to be directed toward sample point
The n orthogonal direction most opened is spread, dimension-reduction treatment then is carried out to multidimensional variable system, makes it to turn with a higher precision
It changes low-dimensional variable system into, then by constructing appropriate cost function, low-dimensional system is further converted to unidimensional system.
Classification and Detection module of the present invention selects support vector machines, Selection of kernel function Radial basis kernel function.Support vector machines side
Method is a kind of new method proposed in recent years.
The main thought of support vector machines may be summarized to be at 2 points：
It is linear can a point situation analyzed, in the case of linearly inseparable, reflected by using non-linear
Algorithm is penetrated, this programme uses Radial basis kernel function, and the sample of low-dimensional input space linearly inseparable is converted into high dimensional feature sky
Between make its linear separability so that high-dimensional feature space carries out linear analysis using linear algorithm to the nonlinear characteristic of sample
It is possibly realized, the present invention chooses Radial basis kernel function；
(2) it is based on structural risk minimization theory the construction optimum segmentation hyperplane in feature space so that study
Device obtains global optimization, and meets certain upper bound in the expected risk of entire sample space with some probability.
Claims (1)
1. the J wave detecting methods based on pole symmetric mode decomposition and support vector machines, it is characterised in that include the following steps：
The first step：Original electro-cardiologic signals x (t) is obtained, t is the electrocardiosignal corresponding time, and original electro-cardiologic signals x (t) includes
The electrocardiosignal of normal electrocardiosignal and the wave containing J, finds out all maximum points of each original electro-cardiologic signals x (t) and minimum point,
It is denoted as Pi, 1≤i≤n；
Second step：With line segment by all extreme point PiIt is sequentially connected, the midpoint of every section of line segment is labeled as Ti, 1≤i≤n-1, and
The total segment left and right ends to be formed, which are connected, in line segment replenishes boundary midpoint T0And Tn；
Third walks：S interpolat curve L is built using n+1 midpoint1,…,Ls, s >=1 calculates its average value L*=(L1+…LS)/
s；
4th step：To x (t)-L*Sequence repeats above three step, until | L*|≤ε or screening number reach preset limiting
Value K, ε are allowable errors, obtain first intrinsic mode function c1(t)；
5th step：To residue sequence x (t)-c1Aforementioned four step is repeated, until residual term r for single signal or is no larger than pre-
First given extreme point, can respectively obtain intrinsic mode function c2(t),c3(t)…,ci(t),cm(t), sequence of calculation x (t)-
The variances sigma of r2；
6th step：Limiting section [Kmin,Kmax] in change limiting value K, repeat above-mentioned five steps, and to σ/σ0It is painted with K
Figure, σ0It is the standard deviation of x (t), σ is the standard deviation of sequence x (t)-r, and σ/σ is found out in figure0The corresponding K of minimum value0, with K0Make
Repeat above-mentioned five steps again for restrictive condition, last residual term r is exactly the adaptive equal line of the overall situation of original signal x (t), also
That is trend term, by decomposing, original electro-cardiologic signals x (t) is represented byUtilize pole symmetric mould
State is decomposed resolves into a series of intrinsic mode functions and a residual term by electrocardiosignal x (t)；
7th step：(1) intrinsic mode function c is foundi(t) extreme point calculates two adjacent maximum points and two adjacent minimum
Time difference between value point；
(2) when being considered as local period value these time differences and being assigned between two adjacent maximum points and two adjacent minimum points
Midpoint on countershaft, drawing the time cycle corresponds to point diagram；
(3) its local frequencies is obtained, then do cubic spline interpolation and obtain instantaneous frequency profile by these local period values are inverted；
8th step：The corresponding analytical expression of i-th of intrinsic mode function is：ci(t)=Ai(t)cosθi(t), 1≤i≤m,Wherein Ai(t) it is amplitude function, θi(t) it is Amplitude ＆ Phase；
9th step：Choose instantaneous frequency F1, F2, F3, F4, F5, F6, F7 and energy conversion characteristics of the first seven intrinsic mode function
E (t) composition characteristics space [F1, F2, F3, F4, F5, F6, F7, E (t)] selects Principal Component Analysis to drop feature space
Dimension chooses preceding 8 principal components of each feature, i.e., the feature after dimensionality reduction amounts to 64；
Tenth step：Features described above is input to support vector machines, support vector machines is trained, extracts electrocardio letter to be detected
Number dimensionality reduction after feature be input to support vector machines, you can tell the type of electrocardiosignal to be detected.
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| CN107233204B (en) * | 2017-07-12 | 2019-08-06 | 李城钰 | A kind of sudden cardiac arrest first aid integrated apparatus |
| CN107729861A (en) * | 2017-10-30 | 2018-02-23 | 太原理工大学 | J wave detecting methods based on random forest |
| CN108680782B (en) * | 2018-05-04 | 2020-10-09 | 上海电力学院 | Voltage flicker parameter detection method based on extreme point symmetric mode decomposition |
| CN108615026B (en) * | 2018-05-09 | 2022-05-10 | 广东工业大学 | Device and equipment for judging malignant ventricular rhythm |
| CN109933839B (en) * | 2019-01-11 | 2023-04-14 | 青岛理工大学 | Integrated pole symmetric modal decomposition method for time-space data analysis |
| CN110619384A (en) * | 2019-08-13 | 2019-12-27 | 浙江工业大学 | PM2.5 concentration value prediction method based on neural network |
| CN110558974B (en) * | 2019-09-06 | 2020-11-03 | 江苏华康信息技术有限公司 | Electrocardiogram signal analysis method based on extreme value energy decomposition method |
| CN110558973B (en) * | 2019-09-06 | 2022-02-18 | 江苏华康信息技术有限公司 | Computer equipment for executing electrocardiogram signal quantitative analysis method based on extreme value energy decomposition method |
| CN110956136A (en) * | 2019-12-02 | 2020-04-03 | 天津市计量监督检测科学研究院 | Hydrophone signal denoising method based on ESMD and roughness penalty smoothing technology |
| CN111177211B (en) * | 2019-12-11 | 2023-05-05 | 华北电力大学 | Runoff sequence variation characteristic analysis method based on pole symmetry modal decomposition |
| CN111904417B (en) * | 2020-07-06 | 2021-12-03 | 天津大学 | Ultra-wideband microwave early breast cancer detection device based on support vector machine |
| CN113974647B (en) * | 2021-10-26 | 2023-08-04 | 福州大学 | System and method for reconstructing sudden cardiac death risk factor |
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