CN110141206A - A kind of electro-physiological signals analysis method based on set empirical mode decomposition - Google Patents
A kind of electro-physiological signals analysis method based on set empirical mode decomposition Download PDFInfo
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Abstract
The invention discloses a kind of electro-physiological signals analysis methods based on set empirical mode decomposition, heart rate variability signals are carried out set empirical mode decomposition first by this method, obtain the intrinsic mode functions of signal different frequency, the few intrinsic mode functions of contained signal message amount are rejected, next the cardinal scales entropy of signal difference intrinsic mode functions is calculated, an index for distinguishing the apparent intrinsic mode functions layer of effect as Diagnosing Cardiac lesion is found, provides strong theoretical foundation for clinical medical research.This method can be very sharp from shorter time series the complexity for disclosing signal, can be provided for actual application conveniently, and random noise be added in the signal, this method is still effective；This method is not limited by the specific waveform of signal, can be used to analyze the signal of random waveform.
Description
Technical field
The present invention relates to signal processing technology field more particularly to a kind of physiology telecommunications based on set empirical mode decomposition
Number analysis method.
Background technique
China includes that the patient numbers of various types of cardiovascular disease disease have been up to 2.9 hundred million at present, aobvious according to latest survey
Show, there are about 4,000,000 people to die of all kinds of disease of cardiovascular systems every year.Cardiovascular disease, which has become China, must currently cause
The Major health problems of whole people's concern.Therefore for cardiac system complexity and cardiac system disease prediction, diagnose, control
The research tool for the treatment of has very great significance.Two major classes, the first kind are broadly divided into the output signal research of cardiac system at present
It is analysis electrocardiographic wave, this method is simple to operation, and can intuitively obtain analysis result.But due to heart system
It unites complexity itself, leads to the randomness of cardiac system clearly.Electrocardiogram is also easy by it during acquisition
The interference of his frequency signal.Second class is heart rate variability signals, and heart rate variability signals are extracted from ECG signal
RR interphase signal, heart rate variability signals are a large amount of discrete data points, and the information contained can react gradually heartbeat week
The situation of change of phase fine difference.
At present to the analysis of heart rate variability signals mainly from signal entirety, while needing a large amount of continuous number
Strong point, this scheme need to put into a large amount of manpower and financial resources during handling signal, and due to chaotic signal itself
Complicated composition can generate certain influence to analysis result so that analysis result is inaccurate, it is therefore desirable to propose a kind of new
Technical solution solve the above problems.
Summary of the invention
In view of the above-mentioned problems, the invention proposes a kind of electro-physiological signals analysis sides based on set empirical mode decomposition
Method, can be very sharp from shorter time series the complexity for disclosing signal, can mention for actual application
For convenient.
The present invention through the following technical solutions to achieve the above objectives:
A kind of electro-physiological signals analysis method based on set empirical mode decomposition, comprising the following steps:
Choose suitable signal sequence；The healthy young man of selection from database, healthy elderly, heart failure patient
Heart rate variability signals, and pretreatment is carried out to the data of database and rejects the ingredients such as ectopic pacemaker and artifact, it is of the invention
All data come from Physionet, which is by National Institutes of Health and Boston University and Boston medicine
Center establishes that be that medical signals researcher from all parts of the world provides convenient jointly.
Signal sequence is pre-processed: in the RR interphase signal extracted there are ectopic pacemaker and artifact ingredient,
Before being tested, it is necessary to pre-process to data, be retained meeting with the signal of lower inequality, be removed other ungratified
Point:
500 < RRi< 1500；
Wherein RRiIt is RR interval series,It is the average value of RR interval series；
Carry out set empirical mode decomposition to signal: in order to ensure the accuracy of test result, the selection of parameter is as follows:
Nstd=0.3, NE=200, wherein Nstd, which is represented, is added the standard deviation of white Gaussian noise and the mark of original signal in original signal
The ratio of quasi- difference, NE are the decomposition number for gathering empirical modal, the intrinsic mode functions component of the different frequency after being decomposed.Collection
It closes empirical mode decomposition and uses noise auxiliary signal processing method, in order to reduce in original signal noise to experimental result bring
White Gaussian noise is added to original signal in interference, because of the characteristics of it is zero that white Gaussian noise, which has mean value, and variance is constant, because
When this time frequency space locating for the original signal is covered with equally distributed white Gaussian noise, all can in all frequencies of original signal
In addition white noise, the characteristics of due to white noise itself, by it is multiple it is average after, the white noise of addition can also cancel out each other.
The intrinsic mode functions component of the different frequency of signal after being decomposed.
Further scheme is the intrinsic mode functions that comparison heart rate variability signals obtain after gathering empirical mode decomposition
The map of magnitudes of IMF component time change therewith, from the signal graph after decomposition it can be found that the higher IMF component of frequency contains
The most information of electrocardiosignal, we term it high frequency section, retain the signal radio-frequency component of IMF1-IMF7 component；
It calculates the cardinal scales entropy of different intrinsic mode functions IMF components: the heart rate variability that data length is N is believed
Number its embedding sequence is embedded into m dimension phase space for each signal, then is taken out m point and is configured to a m n dimensional vector n,
Calculate separately the cardinal scales entropy of each m n dimensional vector n；
It calculates cardinal scales and selects symbol, the criteria for classifying is α × BS, each m n dimensional vector n is converted into m dimension arrow respectively
Quantity symbol sequence Si(X (i))=and s (i), s (i+1) ... s (i+m-1), s ∈ A (A=0,1,2,3), wherein 0,1,2,3
As just the symbol for dividing each region, there is no actual meanings for the selection of specific value.α is a parameter, it
Value interval be 0.1-2；
Count m n dimensional vector n symbol sebolic addressing SiDistribution probability P (Si)；M n dimensional vector n symbol comprising symbol in 0,1,2,3 four
Sequence SiShare 4mThe different combining form π of kind, the cardinal scales entropy of different intrinsic mode functions is calculated according to distribution probability；
According to cardinal scales entropy choose can be completely separated by three kinds of signaling zones intrinsic mode functions layer IMF4, as area
Divide an index of heart rate variability signals.
The beneficial effects of the present invention are:
Heart rate variability signals are carried out set empirical mode decomposition first by method of the invention, obtain signal different frequency
Intrinsic mode functions, the few intrinsic mode functions of contained signal message amount are rejected, next calculate signal difference eigen mode
The cardinal scales entropy of function finds an index for distinguishing the apparent intrinsic mode functions layer of effect as Diagnosing Cardiac lesion,
Strong theoretical foundation is provided for clinical medical research.
Method used in the present invention can be very sharp from shorter time series the complexity for disclosing signal
Degree can provide conveniently for actual application, and random noise is added in the signal, and this method is still effective.
Method used in the present invention is not limited by the specific waveform of signal, can be used to analyze the signal of random waveform,
Other entropy measure methods of this point are difficult to realize.Therefore the available more accurate analysis of this method is as a result, to heart rate variability
Signal carries out classification analysis.
Detailed description of the invention
It to describe the technical solutions in the embodiments of the present invention more clearly, below will be to embodiment or description of the prior art
In required practical attached drawing be briefly described, it should be apparent that, the accompanying drawings in the following description is only the one of the present embodiment
A little embodiments for those of ordinary skill in the art without creative efforts, can also be according to these
Attached drawing obtains other attached drawings.
Fig. 1 is the flow chart for gathering empirical modal algorithm；
Fig. 2 is the algorithm flow chart for calculating the cardinal scales entropy of different intrinsic mode functions components.
Specific embodiment
To make the object, technical solutions and advantages of the present invention clearer, technical solution of the present invention will be carried out below
Detailed description.Obviously, the described embodiment is only a part of the embodiment of the present invention, instead of all the embodiments.It is based on
Embodiment in the present invention, those of ordinary skill in the art without making creative work it is obtained it is all its
Its embodiment belongs to the range that the present invention is protected.
Analysis method process of the invention is divided into two parts, and first part is using set empirical modal algorithm to signal
It is decomposed, obtains the intrinsic mode functions component of the different frequency of signal, select the higher eigen mode letter of contained signal message amount
Number；Second part is the cardinal scales entropy for calculating the intrinsic mode functions selected, and finds and distinguishes the apparent intrinsic mode functions of effect
An index of the layer as Diagnosing Cardiac lesion.
Present invention is further described in detail with reference to the accompanying drawing, and referring to Fig. 1, one kind of the invention is based on set experience
The electro-physiological signals analysis method of mode decomposition carries out set empirical mode decomposition to pretreated heart rate variability signals,
Specific step is as follows:
(1) assume that original signal sequence is x (t), mkIndicate after original signal progress is averaged for k times as a result, then
IMF component can be defined as the difference between x (t) and mx (t), we use c1To indicate this difference i.e.: c1=x (t)-mx (t)
(2) again by x (t)-c after first time decomposes1Continue the decomposition in step (1) as original signal,
When by signal decomposition to n-th, the signal after being decomposed is expressed as follows:
cn=mn-1x(t)-mn(t)；
(3) after n is decomposed, signal can be indicated are as follows:
(4) assume the parsing letter under the different frequency IMF component obtained after set empirical mode decomposition method decomposes
Number with zk(t) (k=1,2 ..., n) indicate then have:
(5) then the analytical expression of original signal can indicate are as follows:
(6) the instantaneous frequency w of each IMF component is obtainedk(t).Original signal has also indicated are as follows:
(7) white Gaussian noise is added into original signal, in the signal y (t) after obtaining neotectonics=x (t)+n (t) above formula
What n (t) was represented is white Gaussian noise；
(8) the white Gaussian noise signal of addition is also subjected to n times set empirical mode decomposition, and it is each to calculate white Gaussian noise
The mean value of group IMF, then the IMF component of white noise after decomposing is added with the IMF component of original signal x (t), it obtains
The IMF component of reproducing sequence y (t).Specific mathematic(al) representation is as follows:
What is represented is the number of plies of IMF；
(9) step (7) are repeated, new white noise sequence is added in (8) every time；
(10) remainder after decomposing n times is averaged, and the remainder of y (t) is obtained, it may be assumed that
(11) IMF obtained every time is integrated into mean value as final result.
In above formula, what R was represented is remainder function of the original signal after EEMD is decomposed, which can represent original
The trend of beginning signal.
Referring to fig. 2, a kind of electro-physiological signals analysis method based on set empirical mode decomposition of the invention, calculating are selected
Intrinsic mode functions cardinal scales entropy, being chosen according to cardinal scales entropy can be completely by separated intrinsic of three kinds of signaling zones
Modular function layer IMF4, specific as follows as an index for distinguishing heart rate variability signals:
Two: taking IMF1-IMF7 component, calculate the cardinal scales entropy of different layers intrinsic mode functions, the specific steps are as follows:
(1) it is each of these u (i) of the heart rate variability signals sequence of N for data length, is embedded into m dimension phase
Space, then be taken out m point and be configured to a m n dimensional vector n:
X (i)=[u (i), u (i+L) ..., u (i+ (m-1) L)]；
What wherein m was represented is Embedded dimensions, and what L was represented is delay time.L=1 is taken, then shared N-m+i m dimension arrow
Amount.Calculate separately the cardinal scales BS of each m n dimensional vector n；
(2) criteria for classifying of cardinal scales selection symbol is α × BS, each m n dimensional vector n is converted into m n dimensional vector n respectively
Symbol sebolic addressing Si(X (i))=and s (i), s (i+1) ... ..s (i+m-1), s ∈ A (A=0,1,2,3)；
In above formula, i=1,2,3 ... N-m+1, k=0,1,2 ... m-1.What is represented is i-th of m n dimensional vector n
Average value, BS represent be i-th of m n dimensional vector n cardinal scales, 0,1,2,3 as just divide each region symbol
Number, there is no actual meanings for the selection of specific value.α is a parameter, its value interval is 0.1-2；
(3) calculate cardinal scales entropy firstly the need of statistics m n dimensional vector n symbol sebolic addressing SiDistribution probability P (Si).Include
The m n dimensional vector n symbol sebolic addressing S of symbol in 0,1,2,3 fouriShare 4mThe different combining form π of kind.We count each not respectively
Same combining form probability shared by entire N-m+1 m n dimensional vector n:
Wherein # indicates number；
After calculating, the base-scale entropy of m n dimensional vector n is defined as:
H (m)=- ∑ p (π) log2p(π)；
Cardinal scales entropy can describe the numerical characteristic of the nonlinear kinetics of HRV signal, and it is a large amount of not need measurement
Data point is obtained with ideal analysis as a result, the calculation method of base-scale entropy is by the thought and dynamics of phase space reconfiguration
Symbolism theory combines the chaos characteristic that can be very good in analysis nonlinear properties, and original signal sequence is moved
Mechanics symbol coding not only can also can more easily be analyzed signal simple abstract with the chaotic characteristic of stick signal essence
Processing.
The above description is merely a specific embodiment, but scope of protection of the present invention is not limited thereto, any
Those familiar with the art in the technical scope disclosed by the present invention, can easily think of the change or the replacement, and should all contain
Lid is within protection scope of the present invention.Specific technical features described in the above specific embodiments, in not lance
In the case where shield, can be combined in any appropriate way, in order to avoid unnecessary repetition, the present invention to it is various can
No further explanation will be given for the combination of energy.Various embodiments of the present invention can be combined randomly, only
Want it without prejudice to thought of the invention, it should also be regarded as the disclosure of the present invention.
Claims (2)
1. a kind of electro-physiological signals analysis method based on set empirical mode decomposition, which comprises the following steps:
Choose suitable signal sequence；
Pre-process to signal sequence: in the RR interphase signal extracted, there are ectopic pacemakers and artifact ingredient, are carrying out
Before experiment, data are pre-processed, are retained meeting with the signal of lower inequality, other ungratified points are removed:
500 < RRi< 1500；
Wherein RRiIt is RR interval series,It is the average value of RR interval series；
Carry out set empirical mode decomposition to signal: in order to ensure the accuracy of test result, the selection of parameter is as follows: Nstd=
0.3, NE=200, wherein Nstd, which is represented, is added the standard deviation of the standard deviation and original signal of white Gaussian noise in original signal
Ratio, NE are the decomposition number for gathering empirical modal；
The intrinsic mode functions component of the different frequency of signal after being decomposed.
2. a kind of electro-physiological signals analysis method based on set empirical mode decomposition as described in claim 1, which is characterized in that
The width that the intrinsic mode functions IMF component that comparison heart rate variability signals obtain after gathering empirical mode decomposition converts at any time
Degree figure, retains the signal radio-frequency component of IMF1-IMF7 component；
Calculate the cardinal scales entropy of different intrinsic mode functions IMF components: for data length be N heart rate variability signals its
Embedding sequence is embedded into m dimension phase space for each signal, then is taken out m point and is configured to a m n dimensional vector n, respectively
Calculate the cardinal scales entropy of each m n dimensional vector n；
It calculates cardinal scales and selects symbol, the criteria for classifying is α × BS, each m n dimensional vector n is converted into m n dimensional vector n symbol respectively
Number sequence Si(X (i))={ s (i), s (i+1) ... s (i+m-1), } s ∈ A (A=0,1,2,3), wherein 0,1,2,3 only
As the symbol for dividing each region, there is no actual meaning, α is a parameter for the selection of specific value, it takes
Value section is 0.1-2；
Count m n dimensional vector n symbol sebolic addressing SiDistribution probability P (Si)；M n dimensional vector n symbol sebolic addressing comprising symbol in 0,1,2,3 four
SiShare 4mThe different combining form π of kind, the cardinal scales entropy of different intrinsic mode functions is calculated according to distribution probability；
According to cardinal scales entropy choose can be completely separated by three kinds of signaling zones intrinsic mode functions layer IMF4, as distinguish the heart
One index of rate Variability Signals.
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| CN110490257A (en) * | 2019-08-21 | 2019-11-22 | 四川长虹电器股份有限公司 | It is a kind of based on the electro-physiological signals entropy analysis method for removing trend term |
| WO2021042592A1 (en) * | 2019-09-06 | 2021-03-11 | 江苏华康信息技术有限公司 | Meditation training hrv signal analysis method based on extremum energy decomposition method |
| CN112716503A (en) * | 2020-12-25 | 2021-04-30 | 四川长虹电器股份有限公司 | Nonlinear trend item elimination method of Logistic mapping sequence under different trends |
| CN112949524A (en) * | 2021-03-12 | 2021-06-11 | 中国民用航空飞行学院 | Engine fault detection method based on empirical mode decomposition and multi-core learning |
| CN113283289A (en) * | 2021-04-13 | 2021-08-20 | 上海电力大学 | CEEMD-MFE and t-SNE based partial discharge mode identification method |
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN110490257A (en) * | 2019-08-21 | 2019-11-22 | 四川长虹电器股份有限公司 | It is a kind of based on the electro-physiological signals entropy analysis method for removing trend term |
| WO2021042592A1 (en) * | 2019-09-06 | 2021-03-11 | 江苏华康信息技术有限公司 | Meditation training hrv signal analysis method based on extremum energy decomposition method |
| CN112716503A (en) * | 2020-12-25 | 2021-04-30 | 四川长虹电器股份有限公司 | Nonlinear trend item elimination method of Logistic mapping sequence under different trends |
| CN112949524A (en) * | 2021-03-12 | 2021-06-11 | 中国民用航空飞行学院 | Engine fault detection method based on empirical mode decomposition and multi-core learning |
| CN113283289A (en) * | 2021-04-13 | 2021-08-20 | 上海电力大学 | CEEMD-MFE and t-SNE based partial discharge mode identification method |
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