CN111631711B - Method for analyzing and processing brain electrical data of schizophrenia and auditory hallucination symptoms - Google Patents
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Abstract
The invention relates to the field of medical signal processing, and discloses a method for analyzing and processing electroencephalogram data of schizophrenia and auditory symptoms thereof, which is based on the asymmetric rate extraction characteristics of multi-band wavelet packet entropy, a multi-classifier system is constructed on auditory symptoms of schizophrenia, non-auditory symptoms of schizophrenia, suspicion, depression, easy impulse, easy anxiety and electroencephalogram signals in a normal state, and finally probability values of auditory symptoms of the electroencephalogram signals to be detected can be output to obtain decision suggestions. The invention can predict the auditory hallucination symptom of the schizophrenia and various mental disease conditions by utilizing the brain electrical signals of the independent individuals, and has practical application significance.
Description
Technical Field
The invention belongs to the field of medical signal processing, and particularly relates to a method for analyzing and processing brain electrical data of schizophrenia and auditory hallucination thereof.
Background
Currently, brain electrical signals of mental diseases play an increasingly important role in basic research and clinical applications concerning brain activities. The current research method of the electroencephalogram signals is mainly divided into a linear method and a nonlinear analysis, wherein the linear method comprises a time domain analysis, a frequency domain analysis and a frequency domain analysis, the time domain analysis mainly takes waveform characteristics as a main factor, only the resolution of the electroencephalogram signals in a time domain can be reflected, the frequency domain analysis mainly converts the electroencephalogram signals into electroencephalogram power information through time-frequency transformation, the change condition along with the frequency and the rhythm distribution condition are explored, the typical representative of the time-frequency domain analysis method is wavelet analysis, the electroencephalogram signals can be processed in the time domain and the frequency domain at the same time, and the nonlinear analysis method of the electroencephalogram signals usually comprises a correlation dimension, a Lyapunov index, an entropy-based analysis method and the like.
Because the electroencephalogram signal is a time-varying and non-stable nonlinear dynamic signal, the linear analysis method cannot effectively extract electroencephalogram data characteristics, has certain limitation, in the electroencephalogram nonlinear analysis method based on the information theory, wavelet packet entropy is an effective characteristic extraction mode, the wavelet packet entropy is based on wavelet packet transformation, the wavelet packet transformation can extract rhythm signals and achieve better frequency resolution than the wavelet transformation, almost no requirement is met on the stability of the signals, the electroencephalogram signal is decomposed through the wavelet packet to obtain different rhythm signals, the spectral entropy of the electroencephalogram signal is calculated through the energy spectrum, the concentration or dispersion degree of the electroencephalogram signal power spectrum can be reflected, and each rhythm of the electroencephalogram is effectively extracted and the local complexity of the electroencephalogram signal is estimated.
The mental diseases comprise various subclasses, a large number of researches only aim at individual symptoms to carry out electroencephalogram analysis, the practical application is limited, and a multi-classification prediction model aiming at various represented mental diseases is built, so that the method has profound research significance and wide application value.
Disclosure of Invention
The invention aims to provide a method for analyzing and processing brain electrical data of schizophrenia and auditory hallucination symptoms thereof, so as to solve the problems in the background art.
In order to achieve the above purpose, the present invention provides the following technical solutions:
a method of analyzing and processing electroencephalogram data for schizophrenia and auditory hallucination thereof, comprising the steps of:
collecting brain electricity data;
preprocessing the acquired data;
acquiring a multi-scale wavelet energy spectrum of segmented electroencephalogram signals of all leads of an individual;
calculating the asymmetry ratio among the leads to obtain a characteristic mode vector of the individual segmented electroencephalogram signals;
constructing a classifier based on wavelet packet entropy according to the characteristic mode vector of the segmented electroencephalogram signals;
the classifier is used to analyze and predict brain electrical signals of schizophrenic patients or mental diseases with phantom hearing, non-phantom hearing.
As still further aspects of the invention: the pretreatment comprises the following steps: positioning an electrode; re-referencing; artifact correction; filtering; segmentation; deleting errors to be tested, deleting and reconstructing bad guides; removing artifacts; removing the bad section; baseline correction and normalization.
As still further aspects of the invention: the step of obtaining the multi-scale wavelet energy spectrum of the segmented electroencephalogram signals of all leads of the individual comprises the following steps:
carrying out wavelet packet decomposition on individual segmented electroencephalogram signals one by one to obtain a multi-layer decomposition tree;
and (5) calculating wavelet packet entropy of nodes in the tree to obtain a multi-scale wavelet energy spectrum.
As still further aspects of the invention: and carrying out wavelet packet decomposition on the electroencephalogram signal data to obtain information of different frequency bands.
As still further aspects of the invention: the calculating of the asymmetry ratio between the leads comprises:
the individual segmented brain electrical data comprises multi-lead signals, wherein one lead is from the left half brain, and the other lead is from the right lead;
and calculating the asymmetry ratio by using the combination of leads of the left and right half brains as characteristic values to form a characteristic mode vector of the brain electrical signal.
As still further aspects of the invention: and constructing a multi-classifier model based on the classification accuracy maximization criterion.
As still further aspects of the invention: the brain electrical characteristic pattern vector similarity or variability of different individuals can be measured by any Euclidean distance.
As still further aspects of the invention: the multi-classifier is trained and debugged based on data of schizophrenic patients, schizophrenic non-auditory patients, suspicion, depression, easy impulse, easy anxiety and normal control.
As still further aspects of the invention: and predicting and analyzing the brain electrical signal information of the test individual to obtain the characteristics of the psychotic diseases, the auditory hallucination characteristics of the schizophrenia and the classification information.
Compared with the prior art, the invention has the beneficial effects that: the system comprises a general preprocessing flow, a data extraction flow, model construction and classification prediction output, and the interactive interface can realize the functions of self-defined preprocessing, signal segmentation selection processing and data analysis.
Detailed Description
The following describes in detail the various details involved in the technical solution of the present invention. It should be noted that the described embodiments are only intended to facilitate an understanding of the invention and are not intended to be in any way limiting.
The invention collects multi-lead electroencephalogram data, and the electroencephalogram data of each individual is a 2-dimensional signal comprising lead information (1-dimensional) and time information (1-dimensional);
the acquisition of the electroencephalogram signals is completed on an electroencephalogram signal acquisition instrument with a multi-lead electrode cap;
the specific parameters are not required, the sampling time is preferably 2-4 minutes, and the head is kept as motionless as possible during the data acquisition process.
Preprocessing electroencephalogram data, wherein the method mainly comprises the following steps of: positioning an electrode; re-referencing; artifact correction is carried out on continuous data; filtering; segmentation; deleting errors to be tested, deleting and reconstructing bad guides; removing artifacts; removing the bad section; baseline correction and normalization.
The preprocessed electroencephalogram signals are segmented signals, namely, electroencephalogram data of the same individual are divided into a plurality of equal-length multi-lead signals, the data volume is amplified, and the electroencephalogram signals have dynamic characteristics;
performing wavelet packet decomposition on each lead data of the segmented signal;
the wavelet packet transformation can decompose the non-stationary signal into weighted sums of wavelets with different scale bases, and can ensure higher frequency resolution of a high frequency band;
the wavelet packet function is expressed as:
wherein, is a psi (t) mother wavelet, and h (t) and g (k) are weight coefficients.
The recurrence relation of the j and j+1 th stages is:
wavelet coefficientsThe method comprises the following steps:
thus, the electroencephalogram signal can be represented as a plurality of wavelet packet sets, which correspond to different frequency ranges and can be changed according to actual needs.
Defining brain electrical signal components using wavelet packet nodesThe energy of (a) is as follows:
the total energy of the brain electrical signals is as follows:
defining the energy of a specific frequency band (signal sub-band) as E s Which is the sum of the energies of the contained signal components, normalized to be expressed as:
the final definition wavelet packet entropy is:
S wp ＝-∑p s ln[p s ]
therefore, assuming that an electroencephalogram signal of an individual is expressed as P (number of leads) ×m (number of signals in a segment short time window) ×n (number of segments), the wavelet packet entropy thereof is characterized as P (number of leads) ×n (number of segments), the leads in each segment of data are derived from left and right half brain signals, assuming that 3 leads are derived from left half brain (A1, A2, A3), 3 leads are derived from right half brain (B1, B2, B3), the asymmetry ratio rate of the wavelet packet entropy values is found by the permutation and combination of the left and right half brains (A1, B1), (A1, B2), (A1, B3), (A2, B2), (A2, B3), (A3, B1), (A3, B2), (A3, B3):
wherein R represents a lead from the right brain half and L represents a lead from the left brain half.
The data used for modeling is 16-lead brain electrical signals, and each of left and right half brains is 8-lead, so that 64 eigenvalues can be obtained to form eigenvectors of signals in a certain frequency band of a corresponding data segment, wavelet packet entropy and asymmetry ratio are repeatedly calculated in different frequency bands, and the corresponding eigenvectors are obtained;
the segmented electroencephalogram signals of the final individual can be represented by this F (frequency band number) ×64 vector.
Constructing an optimal multi-classification model based on a classifier of pattern recognition, straightening the multi-band feature vectors into one-dimensional feature vectors in the training process, and inputting the one-dimensional feature vectors into the classifier; considering the influence of the feature dimension on the classifier, the method adopts a principal component analysis method to reduce dimension in the implementation process, and ensures that the contribution rate of the selected components reaches 99 percent; then, utilizing a Support Vector Machine (SVM) to carry out multi-category training on the segmented data of the training individuals;
the SVM kernel function can select radial basis kernel function, linear kernel function and the like to define distance measurement, and output the distance measurement as posterior probability value and classification label;
the classification result of the classifier is determined by selecting corresponding parameters and carrying out ten-fold cross validation on a data set to generate a multi-classifier, and classification labels are schizophrenic auditory data, schizophrenic non-auditory data, multi-suspicion, depression, easy impulse, easy anxiety and normal control data.
For new test data, firstly, obtaining segmented data of an electroencephalogram signal to be predicted through preprocessing, and then, predicting all the segmented data through a built multi-classifier model system in sequence;
because the test data comprises a plurality of segmentation results, the classifier also generates a plurality of segmentation prediction conditions, and statistical analysis is carried out on all the prediction results;
finally, the system outputs probability values of the categories to which the segmented signals belong and top2 decision suggestions, and in practical application, different segmented prediction results can be comprehensively considered and the psychotic symptoms of the independent individuals can be predicted.
The foregoing description is only a preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art, who is within the scope of the present invention, should make equivalent substitutions or modifications according to the technical solution of the present invention and the inventive concept thereof, and should be covered by the scope of the present invention.
Claims (6)
1. A method for analyzing and processing electroencephalogram data of schizophrenia and auditory hallucination thereof, comprising the steps of:
collecting brain electricity data;
preprocessing the acquired data, wherein the preprocessed electroencephalogram signals are segmented signals, and the electroencephalogram data of the same individual
A plurality of equal length multi-lead signals, which are divided into a plurality of equal length multi-lead signals, amplify the data volume, and have dynamic characteristics;
acquiring a multi-scale wavelet energy spectrum of segmented electroencephalogram signals of all leads of an individual;
calculating the asymmetry ratio among the leads to obtain a characteristic mode vector of the individual segmented electroencephalogram signals;
constructing a classifier based on wavelet packet entropy according to the characteristic mode vector of the segmented electroencephalogram signals;
analyzing and predicting brain electrical signals of patients with auditory hallucinations, non-auditory hallucinations, or mental diseases by using a classifier;
the step of obtaining the multi-scale wavelet energy spectrum of the segmented electroencephalogram signals of all leads of the individual comprises the following steps:
carrying out wavelet packet decomposition on individual segmented electroencephalogram signals one by one to obtain a multi-layer decomposition tree;
performing wavelet packet entropy calculation on nodes in the tree to obtain a multi-scale wavelet energy spectrum;
carrying out wavelet packet decomposition on the electroencephalogram signal data to obtain information of different frequency bands;
wherein the wavelet packet function is expressed as:
wherein, is a psi (t) mother wavelet, h (k) and g (k) are weight coefficients;
the recurrence relation of the j and j+1 th stages is:
wavelet coefficientsThe method comprises the following steps:
therefore, the electroencephalogram signals are expressed as a plurality of wavelet packet sets, and the wavelet packet sets correspond to different frequency ranges and can be changed according to actual needs;
defining brain electrical signal components using wavelet packet nodesThe energy of (a) is as follows:
the total energy of the brain electrical signals is as follows:
defining the energy of a specific frequency band as E s Which is the sum of the energies of the contained signal components, normalized to be expressed as:
the final definition wavelet packet entropy is:
S wp ＝-∑p s ln[p s ]
the calculating of the asymmetry ratio between the leads comprises:
the individual segmented brain electrical data comprises multi-lead signals, wherein one lead is from the left half brain, and one lead is from the right half brain;
the asymmetry ratio is obtained by utilizing the combination of leads of the left half brain and the right half brain to be used as a characteristic value to form a characteristic mode vector of the brain electrical signal;
the characteristic mode vector of the electroencephalogram signal is a vector formed by the asymmetric rate of wavelet packet entropy of the left brain lead and the right brain lead in the segmentation and the specific frequency band component;
assuming that an electroencephalogram signal of an individual is expressed as P multiplied by M multiplied by N, wherein P is the number of leads, M is the number of signals in a segmented short time window, and N is the number of segments; the wavelet packet entropy is characterized by p×n, the leads in each segment of data are derived from left and right brain signals, assuming that 3 leads are derived from left brain (A1, A2, A3), 3 leads are derived from right brain (B1, B2, B3), and the asymmetry ratio rate of the wavelet packet entropy values is calculated according to the permutation and combination (A1, B1), (A1, B2), (A1, B3), (A2, B2), (A2, B3), (A3, B1), (A3, B2), (A3, B3) of the left and right brain:
wherein R represents a lead from the right brain half and L represents a lead from the left brain half.
2. A method of analysing electroencephalogram data for schizophrenia and its auditory hallucinations as claimed in claim 1, wherein said pre-treatment comprises: positioning an electrode; re-referencing; artifact correction; filtering; segmentation; deleting errors to be tested, deleting and reconstructing bad guides; removing artifacts; removing the bad section; baseline correction and normalization.
3. The method of claim 2, further comprising constructing a multi-classifier model based on classification accuracy maximization criteria.
4. A method of analysing brain electrical data for schizophrenia and its auditory hallucinations according to claim 3, wherein the similarity or difference of brain electrical characteristic pattern vectors of different individuals can be measured by any euclidean distance.
5. The method of claim 4, wherein the multi-classifier is based on training and debugging of schizophrenic auditory patients, schizophrenic non-auditory patients, suspicion, depression, impulsivity, anxiety, normal control data.
6. The method for analyzing and processing the electroencephalogram data of the schizophrenia and the auditory hallucination thereof according to claim 5, further comprising predicting and analyzing the electroencephalogram information of the test individual to obtain the characteristics of the psychotic disorder, the auditory hallucination of the schizophrenia and the classification information.
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| CN102715903A (en) * | 2012-07-09 | 2012-10-10 | 天津市人民医院 | Method for extracting electroencephalogram characteristic based on quantitative electroencephalogram |
| CN110338820A (en) * | 2019-06-13 | 2019-10-18 | 四川大学 | A kind of depression and schizophrenia recognition methods |
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| CN102715903A (en) * | 2012-07-09 | 2012-10-10 | 天津市人民医院 | Method for extracting electroencephalogram characteristic based on quantitative electroencephalogram |
| CN110338820A (en) * | 2019-06-13 | 2019-10-18 | 四川大学 | A kind of depression and schizophrenia recognition methods |
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| Title |
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| 冯静雯 等.精神分裂症和抑郁症患者静息态脑电功率谱熵的对照研究.《中国生物医学工程学报》.2019,第38卷(第38期),第385-390页. * |
| 许慰玲 等.基于小波包分解的精神分裂症脑电信号分析.《电子测量与仪器学报》.2004,第18卷(第18期),第35-39页. * |
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