CN102824171A - Method for extracting electroencephalogram features of patients suffering from PSD (post-stroke depression) - Google Patents
Method for extracting electroencephalogram features of patients suffering from PSD (post-stroke depression) Download PDFInfo
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Abstract
The invention relates to a medical rehabilitation instrument. In order to objectively detect whether post-stroke patients suffer from depression or not and classify the order of severity of depression, a method for extracting electroencephalogram features of patients suffering from PSD (post-stroke depression) comprises the following steps of: acquiring electroencephalogram signals, pre-processing data, extracting asymmetric parameters of a power spectrum, and classifying and identifying, wherein the step of extracting asymmetric parameters of a power spectrum comprises a process of extracting asymmetric parameters IHAI among alpha-frequency band hemispheres and a process of extracting asymmetric parameters SASI of high and low frequency band spectra. The method provided by the invention is mainly used for designing and manufacturing medical rehabilitation instruments.
Description
Technical field
The present invention relates to the medical rehabilitation apparatus, relate in particular to the post-stroke depression disease PSD patient brain electrical feature method for distilling that adopts in the medical rehabilitation apparatus.
Background technology
Apoplexy is claimed apoplexy again, is the 3rd cause of death that is only second to coronary heart disease and cancer in the global range, accounts for 12% of all causes of death.In China, apoplexy is the disease of present disability rate first, fatality rate second.Along with the obvious rising of stroke onset rate, consequent spiritual problem is also increasing.The English PSD that is called for short of post-stroke depression, PSD is as one of complication of apoplexy, and people's physical and mental health in serious threat, brings great financial burden and mental burden for society and family, also so received more and more researchers' concern.Diagnosis to PSD does not still have unified standard at present, and Chinese scholars has adopted various diagnostic criterias, the scale of functional depression basically.Diagnostic criteria has external DSM (Diagnostic and Statistical Manual of Mental Disorders) III-R, DSM IV and the domestic CCMD-3 (Chinese mental sickness diagnostic criteria) that generally adopts.Use commonplace diagnosis scale Hamilton depression scale, depression in old age scale etc. are arranged.But because there is cognitive disorder in the post-stroke patient; Disturbance of consciousness; Aphasis and otherwise reason, the change that some patients can't the various emotions of accurate description oneself, even need accompany and serve generation and tell; Thereby influenced the doctor to the state of an illness comprehensively, accurately grasp and treat, this makes PSD have higher mistaken diagnosis rate of missed diagnosis.Therefore find the method for a kind of objective appraisal PSD to be very important.
The EEG signal is spontaneous, the rhythmicity electrical activity of noting through scalp electrode of brain cell crowd.The electroencephalography of brain function state has safety, convenient, cheap, noninvasive characteristics, and good temporal resolution is arranged, and can in real time, dynamically observe the brain function situation of change, and the characteristics of at present a lot of EEG have obtained clinical diagnosis doctor's approval.PSD patient EEG signal exists the characteristic that is different from healthy subjects in parameters such as the rhythm and pace of moving things, wave-shape amplitude and power spectrum, so we are necessary it is analyzed and handles, and is beneficial to our research and the clinical diagnosis to PSD.Research shows that there is the power spectrum unsymmetry between the two cerebral hemispheres in patients with depression EEG signal alpha frequency band, and patients with depression EEG signal spectrum unsymmetry (high band and low-frequency range) is relevant with the depressed order of severity.
Summary of the invention
The present invention is intended to overcome the deficiency of prior art; Objective detection post-stroke patient has or not depressive symptom and the depressed order of severity is carried out classification; For achieving the above object; The technical scheme that the present invention takes is, post-stroke depression disease PSD patient brain electrical feature method for distilling comprises the steps: collection, data pretreatment, power spectrum unsymmetry parameter extraction, the Classification and Identification of EEG signals; Wherein power spectrum unsymmetry parameter extraction comprises unsymmetry parameter I HAI and height frequency range frequency spectrum unsymmetry parameter S ASI leaching process between α frequency range hemisphere.
The eeg signal acquisition concrete steps are:
The collecting device of EEG signals uses NicoletOne 32 passage digital video electroencephalographs, and lay according to the international standard ten-twenty electrode system that leads the position of electrode, writes down 16 top guide skin EEG signals; Fp1, Fp2, F3, F4, C3, C4, P3, P4, O1, O2, F7, F8, T3, T4, T5, T6 are 16 conductive electrode; Electrode Cz is electrode as a reference, forehead as a reference, sample rate is 250Hz; Filter pass band is 0.5Hz ~ 70Hz, and electrode impedance is less than 10K Ω; Gather the EEG signals under the quiescent condition, gather environmental requirement, gather the chamber half-light, and keep gathering the peace and quiet of environment away from powerful electrostatic field and electromagnetic field; Require in the gatherer process to be closed order by picker's peace and quiet, mood is loosened, record 5min eeg data.
Data pretreatment concrete steps are:
At first signal is carried out 0.5Hz ~ 48Hz bandpass filtering, secondly remove eye electricity and myoelectricity interference with the method for principal component analysis PCA.
Power spectrum unsymmetry parameter extraction concrete steps are:
Unsymmetry parameter I HAI extracts between α frequency range hemisphere:
The unsymmetry calculation of parameter is carried out in the corresponding brain interval of the two cerebral hemispheres between α frequency range hemisphere; The 16 EEG signals that lead can obtain IHAI value Fp1-Fp2, F3-F4, C3-C4, P3-P4, O1-O2, F7-F8, T3-T4, the T5-T6 of 8 correspondences, and calculation procedure is following:
(1) with each power spectral density S that leads of EEG signal after figure method calculating pretreatment average period
m, i.e. the m power spectral density of leading.
(2) the relative power value of calculating the two cerebral hemispheres α frequency range
Left hemisphere α frequency range relative power value
(S
LmThe power spectral density of leading for left hemisphere)
Right hemisphere α frequency range relative power value
(S
RmThe power spectral density of leading for right hemisphere)
(3) unsymmetry parameter I HAI value between calculation of alpha frequency range the two cerebral hemispheres:
The extraction of height frequency range frequency spectrum unsymmetry parameter S ASI:
Low-frequency range is chosen 4Hz, and high band is chosen 24Hz, and concrete calculation procedure is following:
(1) with each power spectral density S that leads of EEG signal after figure method calculating pretreatment average period
m, i.e. the m power spectral density of leading.
(2) calculate the just marginal frequency of frequency range
At first, find out the i.e. maximum frequency values f of 8 ~ 13Hz power spectral density of α frequency range
Max, to f
Max± BHz, B=2, the power spectral density plot of frequency band is made fitting of parabola, and with the frequency at parabola summit place after the match mid frequency fc as intermediate bands, two marginal frequencies of low-frequency range are: F1=fc-B-4, F2=fc-B, unit: Hz;
Two marginal frequencies of high band are: F3=fc+B, F4=fc+B+24, unit: Hz;
(3) calculate the just performance number of frequency range
Low frequency power value
high frequency power value
(4) calculate height frequency range frequency spectrum unsymmetry parameter S ASI value
The Classification and Identification concrete steps are:
8 IHAI values and 16 SASI values as characteristic vector, are input to and carry out pattern recognition in the double-deck SVM UNE:, depressed and non-depressed patient are carried out Classification and Identification through ground floor SVM network; Through second layer SVM network the depressed degree of patients with depression is classified, identify slight, moderate, severe patient.
Technical characterstic of the present invention and effect:
The present invention proposes feature extracting method to PSD patient EEG signal; EEG signal to collecting carries out pretreatment; After obtaining the EEG signal of relative high s/n ratio; Utilize the method for the invention to carry out calculation of characteristic parameters, draw 16 lead unsymmetry parameter (IHAI) and height frequency range frequency spectrum unsymmetry parameters (SASI) between the α frequency range hemisphere of EEG signal respectively.Two types of parameters utilizing this method to obtain are carried out pattern recognition as characteristic use SVM, can obtain higher classification accuracy rate.Therefore significant based on the objective evaluation etalon of this parameter study PSD.
Description of drawings
Fig. 1 power spectrum unsymmetry parameter extraction process block diagram.
16 distribution schematic diagrams that lead that Fig. 2 brain wave acquisition is used, the left side is a side view, the right side is a vertical view.
Patients with cerebral apoplexy EEG signals under Fig. 3 quiescent condition.
Fig. 4 power spectral density plot.
The specific embodiment
Proposed post-stroke depression (PSD) patient's EEG signals (EEG) to be carried out new feature extraction method, for the clinical diagnosis of PSD provides objective basis according to two power spectrum unsymmetry parameters.Its techniqueflow is: obtaining between α frequency band hemisphere unsymmetry parameter and EEG height frequency range frequency spectrum unsymmetry parameter through experimenter EEG signal power spectrum density, is that characteristic is judged whether depression and the order of severity of depression carried out classification of post-stroke patient with these two parameters.
Feature extracting method based on PSD patient EEG power spectrum signal unsymmetry comprises following four parts: the collection of EEG signals, data pretreatment, power spectrum unsymmetry parameter extraction, Classification and Identification.Characteristic extraction procedure is shown in accompanying drawing 1.
1 eeg signal acquisition
The collecting device of EEG signals uses NicoletOne 32 passage digital video electroencephalographs; Lay according to the international standard ten-twenty electrode system that leads the position of electrode, writes down 16 top guide skin EEG signals (Fp1, Fp2, F3, F4, C3, C4, P3, P4, O1, O2, F7, F8, T3, T4, T5, T6), and electrode Cz is electrode as a reference; Forehead as a reference; Sample rate is 250Hz, and filter pass band is 0.5Hz ~ 70Hz, and electrode impedance is less than 10K Ω.Accompanying drawing 2 has provided 16 distribution schematic diagrams that lead.
Gather the EEG signals under the quiescent condition.Gather environmental requirement away from powerful electrostatic field and electromagnetic field, gather the chamber half-light, and keep gathering the peace and quiet of environment.Require in the gatherer process to be closed order by picker's peace and quiet, mood is loosened, record 5min eeg data.
2 data pretreatment
It is that the power frequency that as far as possible reduces in the gatherer process to be introduced is disturbed and noise signal that the EEG signal that collects is carried out pretreated purpose, improves signal to noise ratio, makes signal keep its verity.At first signal is carried out 0.5Hz ~ 48Hz bandpass filtering during pretreatment, secondly remove eye electricity and myoelectricity interference with the method for principal component analysis (PCA).
3 power spectrum unsymmetry parameter extractions
The EEG signal is divided into 4 frequency bands usually: and the δ ripple (0.5Hz ~ 4Hz), θ (4Hz ~ 8Hz), α (8Hz ~ 13Hz), β (13Hz ~ 30Hz).The present invention proposes two power spectrum unsymmetry parameters: unsymmetry parameter (IHAI) and height frequency range frequency spectrum unsymmetry parameter (SASI) between α frequency range hemisphere.Below introduce its leaching process respectively.
3.1 unsymmetry parameter (IHAI) between α frequency range hemisphere
IHAI calculates in the corresponding brain interval of the two cerebral hemispheres and carries out, and the 16 EEG signals that lead can obtain the IHAI value (Fp1-Fp2, F3-F4, C3-C4, P3-P4, O1-O2, F7-F8, T3-T4, T5-T6) of 8 correspondences, and calculation procedure is following:
(1) with each power spectral density S that leads of EEG signal after figure method calculating pretreatment average period
m, i.e. the m power spectral density of leading.
(2) the relative power value of calculating the two cerebral hemispheres α frequency range
Left hemisphere α frequency range relative power value
(S
LmThe power spectral density of leading for left hemisphere)
Right hemisphere α frequency range relative power value
(S
RmThe power spectral density of leading for right hemisphere)
(3) unsymmetry parameter I HAI value between calculation of alpha frequency range the two cerebral hemispheres:
5.3.2 height frequency range frequency spectrum unsymmetry parameter (SASI)
SASI ignores the power spectrum characteristic of intermediate bands (α frequency range) when calculating; The power spectral density of EEG signal low-frequency range has comparability much larger than the high band power spectral density in order to make two frequency band power spectrums, and the design's low-frequency range is chosen 4Hz; High band is chosen 24Hz, and concrete calculation procedure is following:
(1) with each power spectral density S that leads of EEG signal after figure method calculating pretreatment average period
m, i.e. the m power spectral density of leading.
(2) calculate the just marginal frequency of frequency range
At first, find out α frequency range (the frequency values f that 8 ~ 13Hz) power spectral densities are maximum
Max, to f
Max± BHz, B=2, the power spectral density plot of frequency band is made fitting of parabola, and with the frequency at parabola summit place after the match mid frequency (fc) as intermediate bands, power spectral density plot is shown in accompanying drawing 4 after the match.
The marginal frequency of low-frequency range is: F1=fc-B-4 (Hz)
F2=fc-B(Hz)
The marginal frequency of high band is: F3=fc+B (Hz)
F4=fc+B+24(Hz)(Hz)
(3) calculate the just performance number of frequency range
Low frequency power value
high frequency power value
(4) calculate height frequency range frequency spectrum unsymmetry parameter S ASI value
4 Classification and Identification
5.3 8 IHAI values of every patient of obtaining and 16 SASI values as characteristic vector, are input in the double-deck SVM UNE and carry out pattern recognition.Through ground floor SVM network, depressed and non-depressed patient are carried out Classification and Identification; Through second layer SVM network the depressed degree of patients with depression is classified, identify slight, moderate, severe patient.
Beneficial effect
The present invention proposes feature extracting method to PSD patient EEG signal; EEG signal to collecting carries out pretreatment; After obtaining the EEG signal of relative high s/n ratio; Utilize the method for the invention to carry out calculation of characteristic parameters, draw 16 lead unsymmetry parameter (IHAI) and height frequency range frequency spectrum unsymmetry parameters (SASI) between the α frequency range hemisphere of EEG signal respectively.Two types of parameters utilizing this method to obtain are carried out pattern recognition as characteristic use SVM, can obtain higher classification accuracy rate.Therefore significant based on the objective diagnosis etalon of this parameter study PSD.
High to PSD mistaken diagnosis rate of missed diagnosis, the present situation of diagnosis scale internalise has proposed to utilize PSD patient EEG signal to carry out the new method of objective diagnosis.Obtain unsymmetry parameter (IHAI) and height frequency range frequency spectrum unsymmetry parameter (SASI) between α frequency range hemisphere based on EEG power spectrum signal unsymmetry; With this input parameter as the EEG pattern recognition; Realization has the important social meaning to effective identification of the depressed degree of PSD patient.
Claims (5)
1. a post-stroke depression disease PSD patient brain electrical feature method for distilling is characterized in that, comprises the steps: collection, data pretreatment, power spectrum unsymmetry parameter extraction, the Classification and Identification of EEG signals; Wherein power spectrum unsymmetry parameter extraction comprises unsymmetry parameter I HAI and height frequency range frequency spectrum unsymmetry parameter S ASI leaching process between α frequency range hemisphere.
2. post-stroke depression disease PSD patient brain electrical feature method for distilling as claimed in claim 1 is characterized in that the eeg signal acquisition concrete steps are:
The collecting device of EEG signals uses NicoletOne 32 passage digital video electroencephalographs, and lay according to the international standard ten-twenty electrode system that leads the position of electrode, writes down 16 top guide skin EEG signals; Fp1, Fp2, F3, F4, C3, C4, P3, P4, O1, O2, F7, F8, T3, T4, T5, T6 are 16 conductive electrode; Electrode Cz is electrode as a reference, forehead as a reference, sample rate is 250Hz; Filter pass band is 0.5Hz ~ 70Hz, and electrode impedance is less than 10K Ω; Gather the EEG signals under the quiescent condition, gather environmental requirement, gather the chamber half-light, and keep gathering the peace and quiet of environment away from powerful electrostatic field and electromagnetic field; Require in the gatherer process to be closed order by picker's peace and quiet, mood is loosened, record 5min eeg data.
3. post-stroke depression disease PSD patient brain electrical feature method for distilling as claimed in claim 1; It is characterized in that; Data pretreatment concrete steps are: at first signal is carried out 0.5Hz ~ 48Hz bandpass filtering, secondly remove eye electricity and myoelectricity interference with the method for principal component analysis PCA.
4. post-stroke depression disease PSD patient brain electrical feature method for distilling as claimed in claim 1 is characterized in that power spectrum unsymmetry parameter extraction concrete steps are:
Unsymmetry parameter I HAI extracts between α frequency range hemisphere:
IHAI calculates in the corresponding brain interval of the two cerebral hemispheres and carries out, and the 16 EEG signals that lead can obtain IHAI value Fp1-Fp2, F3-F4, C3-C4, P3-P4, O1-O2, F7-F8, T3-T4, the T5-T6 of 8 correspondences, and calculation procedure is following:
(1) with each power spectral density S that leads of EEG signal after figure method calculating pretreatment average period
m, i.e. the m power spectral density of leading;
(2) the relative power value of calculating the two cerebral hemispheres α frequency range
Left hemisphere α frequency range relative power value
S
LmThe power spectral density of leading for left hemisphere;
Right hemisphere α frequency range relative power value
S
RmThe power spectral density of leading for right hemisphere;
(3) unsymmetry parameter I HAI value between calculation of alpha frequency range the two cerebral hemispheres:
The extraction of height frequency range frequency spectrum unsymmetry parameter S ASI:
Low-frequency range is chosen 4Hz, and high band is chosen 24Hz, and concrete calculation procedure is following:
(1) with each power spectral density S that leads of EEG signal after figure method calculating pretreatment average period
m, i.e. the m power spectral density of leading;
(2) calculate the just marginal frequency of frequency range
At first, find out the i.e. maximum frequency values f of 8 ~ 13Hz power spectral density of α frequency range
Max, to f
Max± BHz, B=2, the power spectral density plot of frequency band is made fitting of parabola, and with the frequency at parabola summit place after the match mid frequency fc as intermediate bands, two marginal frequencies of low-frequency range are: F1=fc-B-4, F2=fc-B, unit: Hz;
Two marginal frequencies of high band are: F3=fc+B, F4=fc+B+24, unit: Hz;
(3) calculate the just performance number of frequency range
Low frequency power value
high frequency power value
(4) calculate height frequency range frequency spectrum unsymmetry parameter S ASI value
5. post-stroke depression disease PSD patient brain electrical feature method for distilling as claimed in claim 1; It is characterized in that; The Classification and Identification concrete steps are: with 8 IHAI values and 16 SASI values as characteristic vector; Be input to and carry out pattern recognition in the double-deck SVM UNE:, depressed and non-depressed patient are carried out Classification and Identification through ground floor SVM network; Through second layer SVM network the depressed degree of patients with depression is classified, identify slight, moderate, severe patient.
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| CN111000557A (en) * | 2019-12-06 | 2020-04-14 | 天津大学 | Noninvasive electroencephalogram signal analysis system applied to decompression skull operation |
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| CN113545789A (en) * | 2021-08-24 | 2021-10-26 | 南京邮电大学 | Electroencephalogram analysis model construction method based on CSP algorithm and PSD algorithm, electroencephalogram analysis method and system |
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| CN112568912A (en) * | 2019-09-12 | 2021-03-30 | 陈盛博 | Depression biomarker identification method based on non-invasive electroencephalogram signals |
| CN111000557A (en) * | 2019-12-06 | 2020-04-14 | 天津大学 | Noninvasive electroencephalogram signal analysis system applied to decompression skull operation |
| CN113180660A (en) * | 2021-04-06 | 2021-07-30 | 北京脑陆科技有限公司 | Method and system for detecting depression state based on EEG signal |
| CN113208628A (en) * | 2021-04-06 | 2021-08-06 | 北京脑陆科技有限公司 | Method and system for detecting depression state based on EEG signal |
| CN113545789A (en) * | 2021-08-24 | 2021-10-26 | 南京邮电大学 | Electroencephalogram analysis model construction method based on CSP algorithm and PSD algorithm, electroencephalogram analysis method and system |
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