CN107951496A - Method and system based on multi-scale entropy analysis psychosoma relevance - Google Patents

Method and system based on multi-scale entropy analysis psychosoma relevance Download PDF

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CN107951496A
CN107951496A CN201711206070.2A CN201711206070A CN107951496A CN 107951496 A CN107951496 A CN 107951496A CN 201711206070 A CN201711206070 A CN 201711206070A CN 107951496 A CN107951496 A CN 107951496A
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魏玉龙
张佳蕾
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Xinyi Health Technology Co Ltd
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Abstract

The present invention provides a kind of method based on multi-scale entropy analysis psychosoma relevance, which includes：Gather the EEG signals and electrocardiosignal of subject；Pre-processed by the EEG signals to being gathered in data collection steps and electrocardiosignal, obtain accurate eeg data and heart rate data；The accurate eeg data and heart rate data that are determined in data processing step are calculated by multi-scale entropy algorithm, obtain the multiple dimensioned entropy of accurate eeg data and heart rate data；Correlation analysis is carried out by the multiple dimensioned entropy of accurate eeg data and heart rate data to being calculated in multi-scale entropy calculation procedure, determines the psychosoma degree of association of subject.The present invention determines the psychosoma degree of association by the calculating analysis to EEG signals and electrocardiosignal, is determined by comprehensively to EEG signals and electrocardiosignal calculate so that result of calculation objective science, so as to objective science assesses human health status.

Description

Method and system based on multi-scale entropy analysis psychosoma relevance
Technical field
The present invention relates to field of medical technology, and psychosoma relevance is analyzed based on multi-scale entropy in particular to a kind of Method.
Background technology
The relation of the heart and body is always just the unsolved riddle that people explore since ancient times, in Ancient Times in China theory of traditional Chinese medical science Just there is the discussion that psychosoma interacts, such as《Interior warp》：" raged impairing liver, anxiety impairs the spleen, happiness is sad, Grief may impair the lung, terror impariring kidney " etc., and " keeping a sound mind, disease peace is always ".It can be considered the blank of psychosomatic medicine theoretical thinking.In west, the relation of the heart and body is also always People are of interest.After the fast development that experienced experimental biomedical, in tumour, AIDS, chronic disease and mental illness etc. During prevention, people start gradually to recognize the importance of psychosomatic medicine.
At present, shown by research the methods of scale statistics and clinic observation, conscious psychological factor, as mood can draw Physiology, the Biochemical changes of human body are played, and the physiology of human body, Biochemical changes can also have an impact the psychological factor of people.Explore the heart Body relevance is the emphasis of psychosomatics research, however, mainly passing through experience to the research conclusion of psychosoma relevance at this stage What the mode of summary or scale statistics obtained；Such method is had a great influence by subjective factor, can not use objective quantification Science data analyze body and mind relevance.Psychological activity is the performance of brain function activity, brain be psychological activity organ and Material base, the research to brain electricity are to explore a kind of important method of psychological activity；It becomes as a kind of dynamic waveform current potential Change, can more objectively reflect the functional state of mesencephalic centre nervous system；Autonomic nerves system knows life-critical physiology Function, such as heartbeat, analysis to electrocardiosignal is to a certain degree can assessing human body physiological function.It is right at this stage The research of psychosoma relevance is less, and most of research is only detected the health status of human body from body or the one of angle of the heart Assessment；A small number of researchs to psychosoma relevance are mainly assessed by means such as scale statistics and clinic observations；People are in disease Disease treatment and aspect of improving health conditions are less for the associated consciousness of body and mind.Although traditional method also can be to the health of human body Situation is studied, but there is following deficiency：First, only body or the one of index of the heart are detected, to the health of human body Situation is studied, and have ignored the mechanism study of psychic and somatic regulation；And in fact, some diseases and body building method are mutual by psychosoma Mutually influence, under adjustment effect as a result, in the case of these are subject to the heart mutually to adjust, influences with body, using dividing for single index The analysis result that analysis method determines is more unilateral, has limitation；2nd, psychosoma relevance is carried out by scale or clinic observation The method of assessment is had a great influence by subjective factor, and the tool of the heart and body can not be explained by the measurement of the data science of objective quantification Body associate feature, causes accuracy rate of testing result low.
The content of the invention
In consideration of it, the present invention proposes a kind of method and system based on multi-scale entropy analysis psychosoma relevance, it is intended to solves Certainly existing detection human health status has the problem of limitation and low accuracy rate.
On the one hand, the present invention proposes a kind of method based on multi-scale entropy analysis psychosoma relevance, the analysis method bag Include：Data collection steps, gather the EEG signals and electrocardiosignal of subject；Data processing step, by being adopted to the data The EEG signals and the electrocardiosignal gathered in collection step are pre-processed, and obtain accurate eeg data and heart rate number According to；Multi-scale entropy calculation procedure, by multi-scale entropy algorithm to the accurate brain electricity number definite in the data processing step Calculated according to the heart rate data, obtain the multiple dimensioned entropy of the accurate eeg data and the heart rate data；Association Property analytical procedure, pass through the accurate eeg data to being calculated in the multi-scale entropy calculation procedure and the heart rate data Multiple dimensioned entropy carries out correlation analysis, determines the psychosoma degree of association of subject.
Further, the above-mentioned method based on multi-scale entropy analysis psychosoma relevance, in the multi-scale entropy calculation procedure In, the Sample Entropy of the characteristic sequence by calculating the EEG signals and/or the electrocardiosignal under different time scales, Determine the multi-scale entropy of the accurate eeg data and/or the heart rate data.
Further, the above-mentioned method based on multi-scale entropy analysis psychosoma relevance, the multi-scale entropy calculation procedure bag Include：Sub-step 1, according to the time series of the equation below construction accurate eeg data and/or the heart rate data coarseWherein, Xi is the treated i-th accurate eeg data or the heart rate number According to, N is the length of the accurate eeg data or the heart rate data,For the coarse time series { y(τ)In jth A element, 1≤j≤N/ τ, τ is scale factor；Sub-step 2, the accurate eeg data and/or institute are constructed by equation below State the m dimensional vectors X of the Sample Entropy of the characteristic sequence of heart rate datam(i)：Xm(i)={ yi+k:0≤k≤m-1}；Wherein, m is insertion Dimension, i is the accurate eeg data that is determined after above-mentioned formula is handled and/or the heart rate data characteristic sequence m tie up to The numbering of amount, yi+kFor the coarse time series { y(τ)In the i-th+k elements；Sub-step 3, institute is calculated by equation below State m dimensional vectors Xm(i) and its complement vector XmThe distance between (j) d [xm(i),xm(j)]：d[xm(i),xm(j)]=max | y(i+k)- y(j+k)|；Wherein, 0≤k≤m-1；I, j=1~M-m+1；I ≠ j, M are time series { y(τ)Sequence length, M=int (N/ τ)；Sub-step 4, determines to each i value d [xm(i),xm(j)] number of ＜ r, that is, template matches number Bm(i), and described in calculating Ratio between the number of template matches number and the distanceWherein, r is tolerance threshold；Sub-step 5, Calculate the average value of the ratio between the template matches number and the number of the distanceSub-step 6, increase dimension are m+1 to construct m+1 vectors, repeat sub-step 2 to Sub-step 5 passes throughCalculateAnd pass through Calculate the ratioAverage value, if M is finite value, sample when determining that sequence length is M by equation below Entropy estimate SampEn (m, r, M)：SampEn (m, r, M)=- ln [Cm+1(r)/Bm(r)]；Sub-step 7, repeat sub-step 1 to Sub-step 6, determines the sample entropy of the accurate eeg data and the heart rate data under different scale.
Further, the above-mentioned method based on multi-scale entropy analysis psychosoma relevance, in the correlation analysis step, The psychosoma degree of association of subject is determined by the algorithm of Mutual Information Theory.
Further, the above-mentioned method based on multi-scale entropy analysis psychosoma relevance, in the correlation analysis step, The psychosoma degree of association I (X, Y) of subject is determined by equation below：I (X, Y)=H (X)+H (Y)-H (XY)；Wherein, H (X) is The entropy of the accurate brain electricity sample Entropy sequence；H (Y) is the entropy of the heart rate samples Entropy sequence；H (XY) is the accurate brain The joint entropy of electric sample Entropy sequence and the heart rate samples Entropy sequence.
Further, the above-mentioned method based on multi-scale entropy analysis psychosoma relevance, the accurate brain electricity sample Entropy sequence Entropy H (X) byDetermine；The entropy H (Y) of the accurate heart rate samples Entropy sequence byDetermine；The combination entropy of the accurate brain electricity sample Entropy sequence and the heart rate samples Entropy sequence Value H (XY) byDetermine；Wherein,For the general of the accurate brain electricity sample Entropy sequence Rate density function；For the probability density function of the heart rate samples Entropy sequence；For the accurate brain electricity sample Entropy sequence With the joint probability density function of the heart rate samples Entropy sequence.
Further, the above-mentioned method based on multi-scale entropy analysis psychosoma relevance, in the multi-scale entropy calculation procedure, After EEG signals stabilization by pretreatment that the EEG signals are carried out for filtering process with go an electric treatment.
Further, the above-mentioned method based on multi-scale entropy analysis psychosoma relevance, the analysis method further include：Pretreatment Step, test environment is adjusted to preset requirement, and is carried out the detection of hair mass dryness fraction, electrode points exfoliating and electrode to subject and pacified Put processing.
Method provided by the invention based on multi-scale entropy analysis psychosoma relevance, by pre-treatment step S1 in collection number According to preceding, test environment and subject are handled, it is ensured that EEG signals and the electrocardio letter gathered in data collection steps S2 Number accuracy, and then ensure the accuracy of the psychosoma degree of association；The brain that data collection steps are gathered by data processing step Electric signal and electrocardiosignal are pre-processed, and to obtain more accurate accurate eeg data and heart rate data, and then are ensured The accuracy of the psychosoma degree of association；Multi-scale entropy algorithm is based on to accurate eeg data and heart rate number by multi-scale entropy calculation procedure According to the calculating for carrying out multiple dimensioned value, to understand the sequence complexity of accurate eeg data and the heart rate data, and then into Accurate eeg data and the heart rate data are screened during the correlation analysis of row EEG signals and electrocardiosignal, and then obtained The more accurate psychosoma degree of association, while the anti-interference and noise immunity that the multiple dimensioned entropy calculated has are taken, can effectively be extracted The characteristic information of EEG signals and electrocardiosignal, eliminates the uncertainty produced in calculating process, substantially increases the definite heart The accuracy of the body degree of association；Multiple dimensioned entropy is analyzed by correlation analysis step, determines the psychosoma association of subject Degree, so that the human health status determined according to the psychosoma degree of association is assessed with carrying out objective science.
Especially, the method based on multi-scale entropy analysis psychosoma relevance provided in the present embodiment, by brain telecommunications Number and the calculating analysis of electrocardiosignal determine the psychosoma degree of association, and in the prior art by being carried out to body or the one of index of the heart Detect or assessment is carried out to psychosoma relevance by scale or clinic observation and compare, which passes through comprehensively to brain electricity Signal and electrocardiosignal calculate and determined so that result of calculation objective science, so as to objective science to human health status Assessed, meanwhile, the psychosoma degree of association is calculated by multi-scale entropy algorithm and determined, further increases the definite psychosoma degree of association Accuracy, and then provide more accurate basis for the assessment of human health status.
On the other hand, the present invention proposes a kind of system based on multi-scale entropy analysis psychosoma relevance, the analysis system Including：Data acquisition module, for gathering the EEG signals and electrocardiosignal of subject；Data processing module, for by pair The EEG signals and the electrocardiosignal gathered in the data collection steps are pre-processed, and obtain accurate eeg data And heart rate data；Multi-scale entropy computing module, for by multi-scale entropy algorithm in the data processing step determine institute State accurate eeg data and the heart rate data is calculated, obtain more rulers of the accurate eeg data and the heart rate data Spend entropy；Correlation analysis module, for passing through the accurate eeg data to being calculated in the multi-scale entropy calculation procedure Correlation analysis is carried out with the multiple dimensioned entropy of the heart rate data, determines the psychosoma degree of association of subject.
Further, the above-mentioned system based on multi-scale entropy analysis psychosoma relevance, the multi-scale entropy computing module lead to The Sample Entropy for the characteristic sequence for calculating the EEG signals and/or the electrocardiosignal is crossed under different time scales, is determined The multi-scale entropy of the accurate eeg data and/or the heart rate data.
Further, the above-mentioned system based on multi-scale entropy analysis psychosoma relevance, the multi-scale entropy computing module lead to Cross the multi-scale entropy that following process calculates the accurate eeg data and/or the heart rate data：Institute is constructed according to equation below State the time series { y of accurate eeg data and/or the heart rate data coarse(τ)}：Wherein, its In, Xi is the treated i-th accurate eeg data or the heart rate data, and N is the accurate eeg data or institute The length of heart rate data is stated,For the coarse time series { y(τ)In j-th of element, 1≤j≤N/ τ, τ for scale because Son；Constructed by equation below the Sample Entropy of the characteristic sequence of the accurate eeg data and/or the heart rate data m tie up to Measure Xm(i)：Xm(i)={ yi+k:0≤k≤m-1}；Wherein, m is Embedded dimensions, i be determined after above-mentioned formula is handled described in The numbering of accurate eeg data and/or the heart rate data characteristic sequence m dimensional vectors, yi+kFor the coarse time series { y(τ)In the i-th+k elements；The m dimensional vectors X is calculated by equation belowm(i) and its complement vector XmThe distance between (j) d [xm (i),xm(j)]：d[xm(i),xm(j)]=max | y(i+k)-y(j+k)|；Wherein, 0≤k≤m-1；I, j=1~M-m+1；I ≠ j, M For time series { y(τ)Sequence length, M=int (N/ τ)；Determine to each i value d [xm(i),xm(j)] number of ＜ r is Template matches number Bm(i), and the ratio between the template matches number and the number of the distance is calculated Wherein, r is tolerance threshold；Calculate the average value of the ratio between the template matches number and the number of the distanceIncrease dimension for m+1 to construct m+1 vectors, repeat sub-step 2 to sub-step 5 Pass throughCalculateAnd pass throughCalculate institute State ratioAverage value, if M is finite value, sample entropy estimate when determining that sequence length is M by equation below Value SampEn (m, r, M)=- ln [Cm+1(r)/Bm(r)]；Above-mentioned calculating process is repeated, it is described accurate under different scale to determine The sample entropy of eeg data and the heart rate data.
Further, the above-mentioned system based on multi-scale entropy analysis psychosoma relevance, the correlation analysis module pass through The algorithm of Mutual Information Theory determines the psychosoma degree of association of subject.
Further, the above-mentioned system based on multi-scale entropy analysis psychosoma relevance, the correlation analysis module pass through Equation below determines the psychosoma degree of association I (X, Y) of subject：I (X, Y)=H (X)+H (Y)-H (XY)；Wherein, wherein, H (X) is The entropy of the accurate brain electricity sample Entropy sequence；H (Y) is the entropy of the heart rate samples Entropy sequence；H (XY) is the accurate brain The joint entropy of electric sample Entropy sequence and the heart rate samples Entropy sequence.
Further, the above-mentioned system based on multi-scale entropy analysis psychosoma relevance, the accurate brain electricity sample Entropy sequence Entropy H (X) byDetermine；The entropy H (Y) of the accurate heart rate samples Entropy sequence byDetermine；The combination entropy of the accurate brain electricity sample Entropy sequence and the heart rate samples Entropy sequence Value H (XY) byDetermine；Wherein,For the general of the accurate brain electricity sample Entropy sequence Rate density function；For the probability density function of the heart rate samples Entropy sequence；For the accurate brain electricity sample Entropy sequence With the joint probability density function of the heart rate samples Entropy sequence.
Further, the above-mentioned system based on multi-scale entropy analysis psychosoma relevance, after EEG signals stabilization, institute Multi-scale entropy computing module is stated to be filtering process by the pretreatment for carrying out the EEG signals and go an electric treatment.
Further, the above-mentioned system based on multi-scale entropy analysis psychosoma relevance, the analysis system further include：Pretreatment Module, the detection of hair mass dryness fraction, electrode points exfoliating and electricity are carried out for adjusting test environment to preset requirement, and to subject Pole placement processing.
Since analysis method embodiment has the effect above, so the analysis system embodiment also has corresponding technology effect Fruit.
Brief description of the drawings
By reading the detailed description of hereafter preferred embodiment, it is various other the advantages of and benefit it is common for this area Technical staff will be clear understanding.Attached drawing is only used for showing the purpose of preferred embodiment, and is not considered as to the present invention Limitation.And in whole attached drawing, identical component is denoted by the same reference numerals.In the accompanying drawings：
Fig. 1 is the flow diagram of the method provided in an embodiment of the present invention that psychosoma relevance is analyzed based on multi-scale entropy；
Fig. 2 is the method provided in an embodiment of the present invention that psychosoma relevance is analyzed based on multi-scale entropy, and multi-scale entropy calculates The flow diagram of step；
Fig. 3 is the structure diagram of the system provided in an embodiment of the present invention that psychosoma relevance is analyzed based on multi-scale entropy.
Embodiment
The exemplary embodiment of the disclosure is more fully described below with reference to accompanying drawings.Although the disclosure is shown in attached drawing Exemplary embodiment, if but should understand, may be realized in various forms the disclosure without the implementation that should be illustrated here Example is limited.On the contrary, these embodiments are provided to facilitate a more thoroughly understanding of the present invention, and can be by the model of the disclosure Enclose and be completely communicated to those skilled in the art.It should be noted that in the case where there is no conflict, the embodiment in the present invention And the feature in embodiment can be mutually combined.Below with reference to the accompanying drawings and the present invention will be described in detail in conjunction with the embodiments.
Multi-scale entropy is a kind of very effective nonlinear analysis method for weighing bio signal complexity.There is research at present Show, can effectively assess the heart, the body state of human body by carrying out multi-scale entropy analysis to nonlinear physiological signal, such as decline Always, all decline for showing complexity of uniformity in arrhythmia cordis and the syndrome of threat to life, and these different lifes Reason state all correspond to different multi-scale entropy curves.Can also be right in addition, carrying out analysis to EEG signals using multi-scale entropy Just, in, three classes emotion progress Classification and Identification is born.
Analysis method embodiment：
Referring to Fig. 1, it is the flow of the method provided in an embodiment of the present invention that psychosoma relevance is analyzed based on multi-scale entropy Schematic diagram, this method include：
Pre-treatment step S1, test environment is adjusted to preset requirement, and carries out the detection of hair mass dryness fraction, electrode to subject Point exfoliating and electrode placement processing.
Specifically, first, the air-conditioned room for selecting quiet sound-proofing strong is test environment, and room temperature is 22-26 DEG C, humidity Between 25-40%, and ensure that mobile phone is in off state in test environment, and adjust reference electrode and auxiliary electricity in test electrode The adjustment of pole, to meet preset requirement i.e. experiment test demand；Then, hair mass dryness fraction detection is carried out to subject, it is ensured that by The hair mass dryness fraction of examination person is that can just enter after presetting mass dryness fraction in test environment, and default mass dryness fraction can be true according to actual test demand It is fixed；Finally, electrode points exfoliating is carried out to subject and electrode placement is handled, specifically, carry out EEG signals test pretreatment： Subject follows main examiner to enter test room recoil on comfortable soft chair, main examiner with scrub cream remove auxiliary electrode point and The skin keratin of reference electrode, main examiner person are the supporting lead electrode cap of subject, determine lead face crown median line and two Relevant position is sticked at adhesive plaster after the Baihui acupoint of the intersection point of have sharp ears line, reference electrode and auxiliary electrode injection conductive paste, is adjusted After thread gluing makes subject perception comfortable, injection conductive paste between skin and electrode, make each electrode point impedance be down to 5 kilo-ohms with Under, drop impedance time is no more than 20min；Wherein, the setting position of auxiliary electrode：VEOU:1cm, VEOL on left eyebrow:Under left eye 1cm, both record vertical eye electricity；HEOR, HEOL are located at by binocular outer canthus at 1cm respectively, so as to recording level eye electricity；With reference to electricity Pole A1, A2 are located at bilateral mastoid location respectively；Meanwhile carry out electrocardiosignal test pretreatment：On the inside of subject right crus of diaphragm ankle, a left side It is at Taixi point on rear side of foot ankle, respectively pastes one piece of electrode slice at right finesse, that is, nei guan point after exfoliating, will more conductive physiograph hearts The cathode of electric acquisition module is connected to left lower extremity, and anode is connected to right upper extremity, and grounding electrode (GND) is connected to right lower extremity.
Data collection steps S2, gathers the EEG signals and electrocardiosignal of subject；
Specifically, first, carried out using brain wave acquisition device and its subsidiary EEG Processing software (Curry7) The collection of EEG signals, setting sample rate are 1000Hz, and filtering band logical is 0.05~50Hz；Then, more conductive lifes are led using more Manage instrument and its subsidiary ECG's data compression software (AcqKnowledge4.4) carries out the collection of electrocardiosignal；Wherein, brain telecommunications Number and the acquisition order of electrocardiosignal there is no tandem；To improve the accuracy of gathered data, it is preferable that start simultaneously at collection Signal is the synchronous collection for carrying out EEG signals and electrocardiosignal.
Data processing step S3, is located in advance by the EEG signals to being gathered in data collection steps and electrocardiosignal Reason, obtains accurate eeg data and heart rate data.
Specifically, first, the data collection steps S2 EEG signals gathered are pre-processed using Curry7 softwares, To remove the noise being entrained in EEG signals, and then accurate eeg data is obtained, and the accurate eeg data of acquisition is changed The .cnt formatted datas that can be imported into mathematical software such as Matlab softwares；Then, using AcqKnowledge4.4 softwares pair The electrocardiosignal of data collection steps S2 collections is pre-processed, and extracts the heart rate data of the electrocardiosignal of collection, and preserve into The .xls formatted datas that Matlab softwares can import；Preferably, the pretreatment to EEG signals can be in EEG signals it is steady By the way that EEG signals are filtered with processing and goes an electric treatment after fixed.
Multi-scale entropy calculation procedure S4, by multi-scale entropy algorithm to accurate eeg data definite in data processing step Calculated with heart rate data, obtain the multiple dimensioned entropy of accurate eeg data and heart rate data.
Specifically, data processing step is calculated based on the Matlab multi-scale entropy algorithms write by Matlab softwares The multi-scale entropy for the accurate eeg data that S3 is obtained and the multi-scale entropy for extracting heart rate data；To further improve accurate brain electricity The accuracy rate of the multiple dimensioned entropy of data and heart rate data, it is preferable that by calculating EEG signals under different time scales And/or the Sample Entropy of the characteristic sequence of electrocardiosignal determines the multi-scale entropy of accurate eeg data and/or heart rate data, so as into One step improves the accuracy of psychosoma degree of association test；The sample entropy of accurate eeg data and heart rate data that above-mentioned calculating determines Bigger, the self similarity degree of the sequence being composed of accurate eeg data and heart rate data is lower, accurate eeg data and the heart The sequence of rate data is more complicated, and the sequential nonlinear feature of accurate eeg data and heart rate data is higher, will pass through the step The more accurate eeg data of sample entropy of the accurate eeg data and heart rate data of middle calculating and the sequence of heart rate data are complicated Degree, and then accurate eeg data and heart rate data are sieved during correlation analysis to carrying out EEG signals and electrocardiosignal Choosing, and then obtain the more accurate psychosoma degree of association.
Correlation analysis step S5, passes through the accurate eeg data and heart rate data to being calculated in multi-scale entropy calculation procedure Multiple dimensioned entropy carry out correlation analysis, determine the psychosoma degree of association of subject.
Specifically, the accurate eeg data that is calculated multi-scale entropy calculation procedure S4 by the algorithm of Mutual Information Theory and The multiple dimensioned entropy of heart rate data carries out correlation analysis, determines the psychosoma degree of association of subject；Mutual information is in information theory A basic conception, commonly used in the statistic correlation between description two systems, or include another in a system The number of information in system, passes through the system between the arthmetic statement EEG signals and electrocardiosignal of Mutual Information Theory in the present embodiment The psychosoma degree of association of correlation, that is, subject is counted, is calculated simply, it is as a result objective and accurate, while the statistic correlation that the theory is definite As a result it is directly objective, the psychosoma degree of association of subject can be directly determined by statistic correlation between the two, to count accordingly Assessed according to psychosoma relevance, more objective science, to further improve the standard assessed human health status True property.
It will be readily appreciated that the method based on multi-scale entropy analysis psychosoma relevance provided in the present embodiment, leads to Pre-treatment step S1 is crossed before gathered data, test environment and subject are handled, it is ensured that in data collection steps S2 The EEG signals of collection and the accuracy of electrocardiosignal, and then ensure the accuracy of the psychosoma degree of association；Pass through data processing step S3 pre-processes the data collection steps S2 EEG signals gathered and electrocardiosignal, to obtain more accurate accurate brain Electric data and heart rate data, and then ensure the accuracy of the psychosoma degree of association；It is based on by multi-scale entropy calculation procedure S4 multiple dimensioned Entropy algorithm carries out accurate eeg data and heart rate data the calculating of multiple dimensioned value, to understand accurate eeg data and heart rate number According to sequence complexity, and then to accurate eeg data and heart rate during correlation analysis to carrying out EEG signals and electrocardiosignal Data are screened, and then obtain the more accurate psychosoma degree of association, at the same have anti-interference of the multiple dimensioned entropy calculated and Noise immunity, can effectively extract the characteristic information of EEG signals and electrocardiosignal, eliminate the uncertainty produced in calculating process, Substantially increase the accuracy of the definite psychosoma degree of association；Multiple dimensioned entropy is analyzed by correlation analysis step S5, The psychosoma degree of association of subject is determined, so that the human health status determined according to the psychosoma degree of association is commented with carrying out objective science Estimate.
Especially, the method based on multi-scale entropy analysis psychosoma relevance provided in the present embodiment, by brain telecommunications Number and the calculating analysis of electrocardiosignal determine the psychosoma degree of association, and in the prior art by being carried out to body or the one of index of the heart Detect or assessment is carried out to psychosoma relevance by scale or clinic observation and compare, which passes through comprehensively to brain electricity Signal and electrocardiosignal calculate and determined so that result of calculation objective science, so as to objective science to human health status Assessed, meanwhile, the psychosoma degree of association is calculated by multi-scale entropy algorithm and determined, further increases the definite psychosoma degree of association Accuracy, and then provide more accurate basis for the assessment of human health status.
Referring to Fig. 2, it is the method provided in an embodiment of the present invention that psychosoma relevance is analyzed based on multi-scale entropy, multiple dimensioned The flow diagram of entropy calculation procedure S4, multi-scale entropy calculation procedure S4 include following sub-step：
Sub-step 1, according to the time of the equation below construction accurate eeg data and/or the heart rate data coarse Sequence { y(τ)}：
Wherein, Xi is the accurate brain for the treated i-th accurate eeg data or the heart rate data, N The length of electric data or the heart rate data,For the coarse time series { y(τ)In j-th of element, that is to say, that J is the numbering of the coarse time series element, and 1≤j≤N/ τ, τ is scale factor.
Specifically, the one-dimensional discrete time series { x for length for N1,x2,....,xN, on multiple scales according to FormulaWherein 1≤j≤N/ τ are configured to the time series { y of coarse(τ)}。
Sub-step 2, the Sample Entropy of the characteristic sequence of accurate eeg data and/or heart rate data is constructed by equation below M dimensional vectors Xm(i)：
Xm(i)={ yi+k:0≤k≤m-1}；
Wherein, m is Embedded dimensions, and i is the accurate eeg data and/or the heart determined after above-mentioned formula is handled The numbering of rate data characteristics sequence m dimensional vectors, yi+kFor the coarse time series { y(τ)In the i-th+k elements；
Sub-step 3, m dimensional vectors X is calculated by equation belowm(i) and its complement vector XmThe distance between (j) d [xm(i), xm(j)]：
d[xm(i),xm(j)]=max | y(i+k)-y(j+k)|；
Wherein, 0≤k≤m-1；I, j=1~M-m+1；I ≠ j, M are time series { y(τ)Sequence length, M=int (N/ τ), i.e. i, j are the numbering of the accurate eeg data and/or the heart rate data characteristic sequence m dimensional vectors, and k is described The sequence number transformation parameter of accurate eeg data and/or the heart rate data characteristic sequence m dimensional vectors.
Specifically, for each i value, X is calculated by above formulam(i) and its complement vector XmThe distance between (j) d [xm (i),xm(j)]。
Sub-step 4, determines to each i value d [xm(i),xm(j)] number of ＜ r, that is, template matches number Bm(i), and calculate Ratio between template matches number and the number of distanceWherein, r is tolerance threshold.
Specifically, first, the tolerance threshold r (r ＞ 0) of matching process is set；Then, count to each i value d [xm (i),xm(j)] number of ＜ r, that is, template matches number Bm(i), finally, calculation template coupling number Bm(i) between the number of distance Ratio
Sub-step 5, the average value of the ratio between calculation template coupling number and the number of distance
Specifically, pass throughCalculate the template matches number that sub-step 4 calculates The average value C of ratio between the number of distance to all i valuesm+1(r)。
Sub-step 6, increase dimension for m+1 to construct m+1 vectors, repeat sub-step 2 to sub-step 5 and pass throughCalculateAnd pass throughRatio calculatedAverage value, if M is finite value, sample Entropy estimate when determining that sequence length is M by equation below SampEn(m,r,M)：
SampEn (m, r, M)=- ln [Cm+1(r)/Bm(r)]。
Specifically, first, it is m+1 to increase dimension, constructs m+1 vectors, repeats sub-step 2 to sub-step 5 and pass throughCalculateThen, and calculating is passed through RatioTo the average value of all i values；Finally, if M is finite value, SampEn (m, r, M)=- ln [C are passed throughm+1 (r)/Bm(r)] the sample Entropy estimate SampEn (m, r, M) when sequence length is M is determined.
Sub-step 7, repeats sub-step 1 to sub-step 6, determines accurate eeg data and heart rate data under different scale Sample entropy, i.e., the multi-scale entropy of accurate eeg data and heart rate data.
It will be readily appreciated that the multi-scale entropy calculation procedure S4 provided in the present embodiment, is calculated by above formula The sample entropy of accurate eeg data and heart rate data, on the one hand, according to accurate eeg data and the characteristic sequence of heart rate data Sample EntropyConfirm the sample entropy of accurate eeg data and heart rate data under different scale, meet the meter of multi-scale entropy Calculate and require；On the other hand, screening calculating is carried out to sample Entropy estimate under each scale, to improve accurate eeg data and the heart The accuracy of the sample entropy of rate data, and then the sample entropy for the accurate eeg data and heart rate data for passing through quantization understands standard The sequence complexity of true eeg data and heart rate data, and then during correlation analysis to carrying out EEG signals and electrocardiosignal pair Accurate eeg data and heart rate data are screened, and then obtain the more accurate psychosoma degree of association.
In correlation analysis step, the psychosoma degree of association I (X, Y) of subject is determined by equation below：
I (X, Y)=H (X)+H (Y)-H (XY)；
Wherein, H (X) is the entropy of the accurate brain electricity sample Entropy sequence；H (Y) is the entropy of the heart rate samples Entropy sequence Value；H (XY) is the joint entropy of the accurate brain electricity sample Entropy sequence and the heart rate samples Entropy sequence.
The entropy H (X) of the accurate brain electricity sample Entropy sequence byDetermine；
The entropy H (Y) of the accurate heart rate samples Entropy sequence byDetermine；
The joint entropy H (XY) of the accurate brain electricity sample Entropy sequence and the heart rate samples Entropy sequence byDetermine；
Wherein,For the probability density function of the accurate brain electricity sample Entropy sequence；For the heart rate samples entropy sequence The probability density function of row；Joint probability for the accurate brain electricity sample Entropy sequence and the heart rate samples Entropy sequence is close Spend function.
It will be readily appreciated that the present embodiment calculates the psychosoma degree of association of subject by formula, quantify to pass through Data validation subject the psychosoma degree of association, and then data assess psychosoma relevance accordingly, more objective science, from And further improve the accuracy assessed human health status.
To sum up, the method provided in this embodiment based on multi-scale entropy analysis psychosoma relevance, passes through pre-treatment step S1 Before gathered data, test environment and subject are handled, it is ensured that the EEG signals gathered in data collection steps S2 With the accuracy of electrocardiosignal, and then the accuracy of the psychosoma degree of association is ensured；Data acquisition is walked by data processing step S3 The EEG signals and electrocardiosignal of rapid S2 collections are pre-processed, to obtain more accurate accurate eeg data and heart rate number According to, and then ensure the accuracy of the psychosoma degree of association；Multi-scale entropy algorithm is based on to accurate brain by multi-scale entropy calculation procedure S4 Electric data and heart rate data carry out the calculating of multiple dimensioned value, to understand the sequence of accurate eeg data and heart rate data complexity Degree, and then accurate eeg data and heart rate data are sieved during correlation analysis to carrying out EEG signals and electrocardiosignal Choosing, and then the more accurate psychosoma degree of association is obtained, while the anti-interference and noise immunity that the multiple dimensioned entropy calculated has, can The effectively characteristic information of extraction EEG signals and electrocardiosignal, eliminates the uncertainty produced in calculating process, substantially increases The accuracy of the definite psychosoma degree of association；Multiple dimensioned entropy is analyzed by correlation analysis step S5, determines subject The psychosoma degree of association, so that the human health status that is determined according to the psychosoma degree of association is assessed with carrying out objective science.
Especially, the method based on multi-scale entropy analysis psychosoma relevance provided in the present embodiment, by brain telecommunications Number and the calculating analysis of electrocardiosignal determine the psychosoma degree of association, and in the prior art by being carried out to body or the one of index of the heart Detect or assessment is carried out to psychosoma relevance by scale or clinic observation and compare, which passes through comprehensively to brain electricity Signal and electrocardiosignal calculate and determined so that result of calculation objective science, so as to objective science to human health status Assessed, meanwhile, the psychosoma degree of association is calculated by multi-scale entropy algorithm and determined, further increases the definite psychosoma degree of association Accuracy, and then provide more accurate basis for the assessment of human health status.
Analysis system embodiment：
Referring to Fig. 3, it is the structure of the system provided in an embodiment of the present invention that psychosoma relevance is analyzed based on multi-scale entropy Schematic diagram, the system include：
Pretreatment module 100, hair mass dryness fraction inspection is carried out for adjusting test environment to preset requirement, and to subject Survey, electrode points exfoliating and electrode placement are handled.
Data acquisition module 200, for gathering the EEG signals and electrocardiosignal of subject.
Data processing module 300, for being carried out by the EEG signals to being gathered in data collection steps and electrocardiosignal Pretreatment, obtains accurate eeg data and heart rate data.
Multi-scale entropy computing module 400, for by multi-scale entropy algorithm in data processing step determine accurate brain Electric data and heart rate data are calculated, and obtain the multiple dimensioned entropy of accurate eeg data and heart rate data.
Correlation analysis module 500, for passing through the accurate eeg data and the heart to being calculated in multi-scale entropy calculation procedure The multiple dimensioned entropy of rate data carries out correlation analysis, determines the psychosoma degree of association of subject.
Preferably, multi-scale entropy computing module is believed by calculating EEG signals and/or electrocardio under different time scales Number characteristic sequence Sample Entropy, determine the multi-scale entropy of accurate eeg data and/or heart rate data.
It is further preferred that multi-scale entropy computing module calculates accurate eeg data and/or heart rate number by following process According to multi-scale entropy：
According to the equation below construction accurate eeg data and/or the time series { y of the heart rate data coarse(τ)}：
Wherein, Xi is the accurate brain for the treated i-th accurate eeg data or the heart rate data, N The length of electric data or the heart rate data,For the coarse time series { y(τ)In j-th of element, 1≤j≤N/ τ, τ are scale factor.
The m dimensional vectors of the Sample Entropy of the characteristic sequence of accurate eeg data and/or heart rate data are calculated by equation below Xm(i)：
Xm(i)={ yi+k:0≤k≤m-1}；
Wherein, m is Embedded dimensions, and i is the accurate eeg data and/or the heart determined after above-mentioned formula is handled The numbering of rate data characteristics sequence m dimensional vectors, yi+kFor the coarse time series { y(τ)In the i-th+k elements.
M dimensional vectors X is calculated by equation belowm(i) and its complement vector XmThe distance between (j) d [xm(i),xm(j)]：
d[xm(i),xm(j)]=max | y(i+k)-y(j+k)|；
Wherein, 0≤k≤m-1；I, j=1~M-m+1；I ≠ j, M are time series { y(τ)Sequence length, M=int (N/τ)。
Determine to each i value d [xm(i),xm(j)] number of ＜ r, that is, template matches number Bm(i), and calculation template matches Ratio between number and the number of distanceWherein, r is tolerance threshold；.
The average value of ratio between calculation template coupling number and the number of distance
Increase dimension for m+1 to construct m+1 vectors, repeat sub-step 2 to sub-step 5 and pass throughCalculateAnd pass throughRatio calculatedAverage value, if M is finite value, sample Entropy estimate when determining that sequence length is M by equation below SampEn(m,r,M)：
SampEn (m, r, M)=- ln [Cm+1(r)/Bm(r)]。
Above-mentioned calculating process is repeated, determines the sample entropy of the accurate eeg data and heart rate data under different scale.
Preferably, correlation analysis module determines the psychosoma degree of association of subject by the algorithm of Mutual Information Theory.
It is further preferred that correlation analysis module determines the psychosoma degree of association I (X, Y) of subject by equation below：
I (X, Y)=H (X)+H (Y)-H (XY)；
Wherein, H (X) is the entropy of the accurate brain electricity sample Entropy sequence；H (Y) is the entropy of the heart rate samples Entropy sequence Value；H (XY) is the joint entropy of the accurate brain electricity sample Entropy sequence and the heart rate samples Entropy sequence.
It is further preferred that the entropy H (X) of the accurate brain electricity sample Entropy sequence by Determine；
The entropy H (Y) of the accurate heart rate samples Entropy sequence byDetermine；
The joint entropy H (XY) of the accurate brain electricity sample Entropy sequence and the heart rate samples Entropy sequence byDetermine；
Wherein,For the probability density function of the accurate brain electricity sample Entropy sequence；For the heart rate samples entropy sequence The probability density function of row；Joint probability for the accurate brain electricity sample Entropy sequence and the heart rate samples Entropy sequence is close Spend function.
Preferably, after EEG signals stabilization, multi-scale entropy computing module is by the pretreatment carried out to EEG signals Filtering process and go an electric treatment.
Wherein, the specific implementation process of receiving module 100, receiving module 200 and confirmation module 300 is real referring to the above method Example is applied, details are not described herein for the present embodiment.
Wherein, pretreatment module 100, data acquisition module 200, data processing module 300, multi-scale entropy computing module 400 and correlation analysis module 500 specific implementation process referring to above method embodiment, the present embodiment is no longer superfluous herein State.
Since analysis method embodiment has the effect above, so the analysis system embodiment also has corresponding technology effect Fruit.
Obviously, various changes and modifications can be made to the invention without departing from essence of the invention by those skilled in the art God and scope.In this way, if these modifications and changes of the present invention belongs to the scope of the claims in the present invention and its equivalent technologies Within, then the present invention is also intended to comprising including these modification and variations.

Claims (16)

  1. A kind of 1. method based on multi-scale entropy analysis psychosoma relevance, it is characterised in that including：
    Data collection steps, gather the EEG signals and electrocardiosignal of subject；
    Data processing step, is carried out by the EEG signals to being gathered in the data collection steps and the electrocardiosignal Pretreatment, obtains accurate eeg data and heart rate data；
    Multi-scale entropy calculation procedure, by multi-scale entropy algorithm to the accurate brain electricity number definite in the data processing step Calculated according to the heart rate data, obtain the multiple dimensioned entropy of the accurate eeg data and the heart rate data；
    Correlation analysis step, by the accurate eeg data calculated in the multi-scale entropy calculation procedure and the heart The multiple dimensioned entropy of rate data carries out correlation analysis, determines the psychosoma degree of association of subject.
  2. 2. the method according to claim 1 based on multi-scale entropy analysis psychosoma relevance, it is characterised in that
    In the multi-scale entropy calculation procedure, by calculating EEG signals and/or described under different time scales The Sample Entropy of the characteristic sequence of electrocardiosignal, determines the multi-scale entropy of the accurate eeg data and/or the heart rate data.
  3. 3. the method according to claim 2 based on multi-scale entropy analysis psychosoma relevance, it is characterised in that more rulers Degree entropy calculation procedure includes：
    Sub-step 1, according to the time series of the equation below construction accurate eeg data and/or the heart rate data coarse {y(τ)}：
    <mrow> <msubsup> <mi>y</mi> <mi>j</mi> <mi>&amp;tau;</mi> </msubsup> <mo>=</mo> <mn>1</mn> <mo>/</mo> <mi>&amp;tau;</mi> <munderover> <mi>&amp;Sigma;</mi> <mrow> <mi>i</mi> <mo>=</mo> <mrow> <mo>(</mo> <mi>j</mi> <mo>-</mo> <mn>1</mn> <mo>)</mo> </mrow> <mi>&amp;tau;</mi> <mo>+</mo> <mn>1</mn> </mrow> <mrow> <mi>j</mi> <mi>&amp;tau;</mi> </mrow> </munderover> <msub> <mi>x</mi> <mi>i</mi> </msub> <mo>;</mo> </mrow>
    Wherein, Xi is the treated i-th accurate eeg data or the heart rate data, and N is the accurate brain electricity number According to or the heart rate data length,For the coarse time series { y(τ)In j-th of element, 1≤j≤N/ τ, τ be Scale factor；
    Sub-step 2, by equation below construct the accurate eeg data and/or the heart rate data characteristic sequence m tie up to Measure Xm(i)：
    Xm(i)={ yi+k:0≤k≤m-1}；
    Wherein, m is Embedded dimensions, and i is the accurate eeg data and/or the heart rate number determined after above-mentioned formula is handled According to the numbering of characteristic sequence m dimensional vectors, yi+kFor the coarse time series { y(τ)In the i-th+k elements；
    Sub-step 3, the m dimensional vectors X is calculated by equation belowm(i) and its complement vector XmThe distance between (j) d [xm(i),xm (j)]：
    d[xm(i),xm(j)]=max | y(i+k)-y(j+k)|；
    Wherein, 0≤k≤m-1；I, j=1~M-m+1；I ≠ j, M are time series { y(τ)Sequence length, M=int (N/ τ)；
    Sub-step 4, determines to each i value d [xm(i),xm(j)] number of ＜ r, that is, template matches number Bm(i), and described in calculating Ratio between the number of template matches number and the distanceWherein, r is tolerance threshold；
    Sub-step 5, calculates the average value of the ratio between the template matches number and the number of the distance
    Sub-step 6, increase dimension for m+1 to construct m+1 vectors, repeat the sub-step 2 to the sub-step 5 and pass throughCalculateAnd pass throughDescribed in calculating RatioAverage value, if M is finite value, sample Entropy estimate when determining that sequence length is M by equation below SampEn(m,r,M)：
    SampEn (m, r, M)=- ln [Cm+1(r)/Bm(r)]；
    Sub-step 7, repeats the sub-step 1 to the sub-step 6, determines the accurate eeg data and the institute under different scale State the sample entropy of heart rate data.
  4. 4. the method according to any one of claims 1 to 3 based on multi-scale entropy analysis psychosoma relevance, its feature exist In,
    In the correlation analysis step, the psychosoma degree of association of subject is determined by the algorithm of Mutual Information Theory.
  5. 5. the method according to claim 4 based on multi-scale entropy analysis psychosoma relevance, it is characterised in that in the pass In connection property analytical procedure, the psychosoma degree of association I (X, Y) of subject is determined by equation below：
    I (X, Y)=H (X)+H (Y)-H (XY)；
    Wherein, H (X) is the entropy of the accurate brain electricity sample Entropy sequence；H (Y) is the entropy of the heart rate samples Entropy sequence；H (XY) it is the accurate brain electricity sample Entropy sequence and the joint entropy of the heart rate samples Entropy sequence.
  6. 6. the method according to claim 6 based on multi-scale entropy analysis psychosoma relevance, it is characterised in that
    The entropy H (X) of the accurate brain electricity sample Entropy sequence byDetermine；
    The entropy H (Y) of the accurate heart rate samples Entropy sequence byDetermine；
    The joint entropy H (XY) of the accurate brain electricity sample Entropy sequence and the heart rate samples Entropy sequence byDetermine；
    Wherein,For the probability density function of the accurate brain electricity sample Entropy sequence；For the heart rate samples Entropy sequence Probability density function；For the accurate brain electricity sample Entropy sequence and the joint probability density letter of the heart rate samples Entropy sequence Number.
  7. 7. the method according to any one of claims 1 to 3 based on multi-scale entropy analysis psychosoma relevance, its feature exist In,
    In the multi-scale entropy calculation procedure, the pretreatment after EEG signals stabilization by being carried out to the EEG signals For filtering process and go an electric treatment.
  8. 8. the method according to any one of claims 1 to 3 based on multi-scale entropy analysis psychosoma relevance, its feature exist In further including：
    Pre-treatment step, test environment is adjusted to preset requirement, and the detection of hair mass dryness fraction, electrode points beveling are carried out to subject Matter and electrode placement processing.
  9. A kind of 9. system based on multi-scale entropy analysis psychosoma relevance, it is characterised in that including：
    Data acquisition module, for gathering the EEG signals and electrocardiosignal of subject；
    Data processing module, for passing through the EEG signals to being gathered in the data collection steps and the electrocardiosignal Pre-processed, obtain accurate eeg data and heart rate data；
    Multi-scale entropy computing module, for by multi-scale entropy algorithm in the data processing step determine the accurate brain Electric data and the heart rate data are calculated, and obtain the multiple dimensioned entropy of the accurate eeg data and the heart rate data；
    Correlation analysis module, for by the accurate eeg data calculated in the multi-scale entropy calculation procedure and institute The multiple dimensioned entropy for stating heart rate data carries out correlation analysis, determines the psychosoma degree of association of subject.
  10. 10. the method according to claim 9 based on multi-scale entropy analysis psychosoma relevance, it is characterised in that
    The multi-scale entropy computing module is believed by calculating the EEG signals and/or the electrocardio under different time scales Number characteristic sequence Sample Entropy, determine the multi-scale entropy of the accurate eeg data and/or the heart rate data.
  11. 11. the method according to claim 10 based on multi-scale entropy analysis psychosoma relevance, it is characterised in that described more Scale Entropy computing module calculates the multi-scale entropy of the accurate eeg data and/or the heart rate data by following process：
    According to the equation below construction accurate eeg data and/or the time series { y of the heart rate data coarse(τ)}：
    <mrow> <msubsup> <mi>y</mi> <mi>j</mi> <mi>&amp;tau;</mi> </msubsup> <mo>=</mo> <mn>1</mn> <mo>/</mo> <mi>&amp;tau;</mi> <munderover> <mi>&amp;Sigma;</mi> <mrow> <mi>i</mi> <mo>=</mo> <mrow> <mo>(</mo> <mi>j</mi> <mo>-</mo> <mn>1</mn> <mo>)</mo> </mrow> <mi>&amp;tau;</mi> <mo>+</mo> <mn>1</mn> </mrow> <mrow> <mi>j</mi> <mi>&amp;tau;</mi> </mrow> </munderover> <msub> <mi>x</mi> <mi>i</mi> </msub> <mo>;</mo> </mrow>
    Wherein, XiIt is the accurate eeg data for the treated i-th accurate eeg data or the heart rate data, N Or the length of the heart rate data,For the coarse time series { y(τ)In j-th of element, 1≤j≤N/ τ, τ is ruler Spend the factor；
    The m that the Sample Entropy of the characteristic sequence of the accurate eeg data and/or the heart rate data is constructed by equation below is tieed up Vectorial Xm(i)：
    Xm(i)={ yi+k:0≤k≤m-1}；
    Wherein, m is Embedded dimensions, and i is the accurate eeg data and/or the heart rate number determined after above-mentioned formula is handled According to the numbering of characteristic sequence m dimensional vectors, yi+kFor the coarse time series { y(τ)In the i-th+k elements；
    The m dimensional vectors X is calculated by equation belowm(i) and its complement vector XmThe distance between (j) d [xm(i),xm(j)]：
    d[xm(i),xm(j)]=max | y(i+k)-y(j+k)|；
    Wherein, 0≤k≤m-1；I, j=1~M-m+1；I ≠ j, M are time series { y(τ)Sequence length, M=int (N/ τ)；
    Determine to each i value d [xm(i),xm(j)] number of ＜ r, that is, template matches number Bm(i), the template matches are calculated and Several ratios between the number of the distanceWherein, r is tolerance threshold；
    Calculate the average value of the ratio between the template matches number and the number of the distance
    Increase dimension for m+1 to construct m+1 vectors, repeat sub-step 2 to sub-step 5 and pass throughMeter CalculateAnd pass throughCalculate the ratioAverage value, if M is finite value, the sample Entropy estimate SampEn (m, r, M) when determining that sequence length is M by equation below：
    SampEn (m, r, M)=- ln [Cm+1(r)/Bm(r)]；
    Above-mentioned calculating process is repeated, determines the Sample Entropy of the accurate eeg data and the heart rate data under different scale Value.
  12. 12. according to method of claim 9 to 11 any one of them based on multi-scale entropy analysis psychosoma relevance, its feature exists In,
    The correlation analysis module determines the psychosoma degree of association of subject by the algorithm of Mutual Information Theory.
  13. 13. the method according to claim 12 based on multi-scale entropy analysis psychosoma relevance, it is characterised in that the pass Connection property analysis module determines the psychosoma degree of association I (X, Y) of subject by equation below：
    I (X, Y)=H (X)+H (Y)-H (XY)；
    Wherein, H (X) is the entropy of the accurate brain electricity sample Entropy sequence；H (Y) is the entropy of the heart rate samples Entropy sequence；H (XY) it is the accurate brain electricity sample Entropy sequence and the joint entropy of the heart rate samples Entropy sequence.
  14. 14. the method according to claim 13 based on multi-scale entropy analysis psychosoma relevance, it is characterised in that
    The entropy H (X) of the accurate brain electricity sample Entropy sequence byDetermine；
    The entropy H (Y) of the accurate heart rate samples Entropy sequence byDetermine；
    The joint entropy H (XY) of the accurate brain electricity sample Entropy sequence and the heart rate samples Entropy sequence byDetermine；
    Wherein,For the probability density function of the accurate brain electricity sample Entropy sequence；For the general of the heart rate samples Entropy sequence Rate density function；For the accurate brain electricity sample Entropy sequence and the joint probability density function of the heart rate samples Entropy sequence.
  15. 15. according to method of claim 9 to 11 any one of them based on multi-scale entropy analysis psychosoma relevance, its feature exists In,
    After EEG signals stabilization, the multi-scale entropy computing module is by the pretreatment carried out to the EEG signals Filtering process and go an electric treatment.
  16. 16. according to method of claim 9 to 11 any one of them based on multi-scale entropy analysis psychosoma relevance, its feature exists In further including：
    Pretreatment module, the detection of hair mass dryness fraction, electrode points are carried out for adjusting test environment to preset requirement, and to subject Exfoliating and electrode placement processing.
CN201711206070.2A 2017-11-27 2017-11-27 Method and system based on multi-scale entropy analysis psychosoma relevance Pending CN107951496A (en)

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CN109300481A (en) * 2018-10-19 2019-02-01 武汉轻工大学 Audio attention rate calculation method and system based on comentropy and time trend analysis
CN109528187A (en) * 2018-11-06 2019-03-29 河海大学常州校区 A kind of multiple dimensioned increment entropy algorithm for time series complexity evaluations
CN109998527A (en) * 2019-04-09 2019-07-12 湖北工业大学 A kind of heart disease detection method based on multi-scale entropy
CN110412467A (en) * 2019-07-30 2019-11-05 重庆邮电大学 A kind of lithium battery fault data screening technique of normalized mutual information criterion constraint
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CN113974646A (en) * 2021-12-01 2022-01-28 湖南万脉医疗科技有限公司 Sleep evaluation method based on information coupling
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CN114224361A (en) * 2021-12-31 2022-03-25 杭州电子科技大学 Sleep stage classification method and device based on electroencephalogram signals
CN114983371A (en) * 2022-05-25 2022-09-02 佳木斯大学 Heart rate irregularity testing system and method for cardiology department based on artificial intelligence
CN117224151A (en) * 2023-11-14 2023-12-15 江西科技学院 Early warning method and system for electroencephalogram abnormal signals

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CN109528187A (en) * 2018-11-06 2019-03-29 河海大学常州校区 A kind of multiple dimensioned increment entropy algorithm for time series complexity evaluations
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CN110412467B (en) * 2019-07-30 2021-07-23 重庆邮电大学 Lithium battery fault data screening method constrained by normalized mutual information criterion
CN110412467A (en) * 2019-07-30 2019-11-05 重庆邮电大学 A kind of lithium battery fault data screening technique of normalized mutual information criterion constraint
CN111227830B (en) * 2020-02-14 2021-06-29 燕山大学 Electroencephalogram and electromyographic coupling analysis method based on complex improved multi-scale transfer entropy
CN111227830A (en) * 2020-02-14 2020-06-05 燕山大学 Electroencephalogram and electromyographic coupling analysis method based on complex improved multi-scale transfer entropy
CN113974646A (en) * 2021-12-01 2022-01-28 湖南万脉医疗科技有限公司 Sleep evaluation method based on information coupling
CN114202921A (en) * 2021-12-09 2022-03-18 洛阳师范学院 Multi-scale symbol dynamic entropy analysis method for traffic flow
CN114224361A (en) * 2021-12-31 2022-03-25 杭州电子科技大学 Sleep stage classification method and device based on electroencephalogram signals
CN114983371A (en) * 2022-05-25 2022-09-02 佳木斯大学 Heart rate irregularity testing system and method for cardiology department based on artificial intelligence
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