阅读说明：本技术 一种对精神***症及其幻听症状的脑电数据进行分析处理的方法 (Method for analyzing and processing electroencephalogram data of schizophrenia and auditory hallucinations thereof ) 是由 林小东 陈策 禚传君 于 2020-06-22 设计创作，主要内容包括：本发明涉及医学信号处理领域,公开了一种对精神分裂症及其幻听症状的脑电数据进行分析处理的方法,基于多频段小波包熵的不对称率提取特征,对精神分裂症幻听症状、精神分裂症非幻听症状、多疑、抑郁、易冲动、易焦虑、正常状态下脑电信号构建多分类器系统,并最终可以输出待检测脑电信号的幻听症状所属概率值,得到决策建议,该系统包含一般预处理流程、数据提取流程、模型搭建和分类预测输出,交互界面可以实现自定义预处理、信号分段选择处理和数据分析的功能。本发明能够利用独立个体的脑电信号预测精神分裂症幻听症状和多种精神性疾病状况,具有实际应用意义。(The invention relates to the field of medical signal processing, and discloses a method for analyzing and processing electroencephalogram data of schizophrenia and auditory hallucinations thereof. The method can predict the auditory hallucination symptoms of schizophrenia and various psychogenic disease conditions by utilizing the electroencephalogram signals of independent individuals, and has practical application significance.)

1. A method for analyzing and processing electroencephalogram data of schizophrenia and auditory hallucinations thereof comprises the following steps:

acquiring electroencephalogram data;

preprocessing the acquired data;

acquiring multi-scale wavelet energy spectrums of segmented electroencephalogram signals of all leads of an individual;

solving the asymmetry ratio among all leads to obtain the 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 signal;

and (3) analyzing and predicting the electroencephalogram signals of the schizophrenia patients with auditory hallucinations and non-auditory hallucinations or the psychogenic diseases by using a classifier.

2. The method for analyzing and processing EEG data of schizophrenia and auditory hallucinations thereof according to claim 1, wherein the preprocessing comprises: positioning an electrode; re-referencing; correcting the artifact; filtering; segmenting; deleting errors, tried, deleted and rebuilding bad leads; removing the artifacts; removing the bad section; baseline correction and normalization.

3. The method for analyzing and processing brain electrical data of schizophrenia and auditory hallucinations thereof according to claim 1, wherein the acquiring the multi-scale wavelet energy spectrum of segmented brain electrical signals of all leads of individuals comprises:

performing wavelet packet decomposition on the individual segmented electroencephalogram signals one by one to obtain a multilayer decomposition tree;

and (4) calculating the wavelet packet entropy of the nodes in the tree to obtain the multi-scale wavelet energy spectrum.

4. The method for analyzing and processing EEG data of schizophrenia and auditory hallucinations thereof as set forth in claim 3, wherein the EEG data is subjected to wavelet packet decomposition to obtain information of different frequency bands.

5. The method of claim 1, wherein the determining the asymmetry ratio between the leads comprises:

the individual segmented electroencephalogram data comprises multi-lead signals, wherein half of the leads are from the left half brain, and half of the leads are from the right lead;

and (3) solving the asymmetric rate as a characteristic value by utilizing the combination of the leads of the left half brain and the right half brain to form a characteristic mode vector of the electroencephalogram signal.

6. The method for analyzing and processing EEG data of schizophrenia and auditory hallucinations thereof according to claim 5, wherein the characteristic pattern vector of EEG is a vector composed of asymmetric rate of wavelet packet entropy of left and right brain leads in segmented and specific frequency band components.

7. The method for analyzing and processing EEG data of schizophrenia and auditory hallucinations thereof according to claim 1, wherein said multi-classifier model is constructed based on a classification accuracy maximization criterion.

8. The method for analyzing and processing EEG data of schizophrenia and auditory hallucinations thereof according to claim 7, wherein the similarity or difference of EEG feature pattern vectors of different individuals can be measured by any Euclidean distance.

9. The method for analyzing and processing the EEG data of schizophrenia and auditory hallucinations thereof according to claim 7, wherein the multiple classifiers are trained and debugged based on schizophrenia auditory hallucinations, schizophrenia non-auditory hallucinations, suspicion, depression, impulsivity, anxiety liability and normal control data.

10. The method for analyzing and processing the electroencephalogram data of schizophrenia and auditory hallucinations thereof according to claim 1, wherein the electroencephalogram signal information of the tested individual is subjected to predictive analysis to obtain characteristics of psychotic diseases, auditory hallucinations of schizophrenia and classification information.

Technical Field

The invention belongs to the field of medical signal processing, and particularly relates to a method for analyzing and processing electroencephalogram data of schizophrenia and auditory hallucinations thereof.

Background

At present, electroencephalogram signals of mental diseases play more and more important roles in basic research and clinical application about brain activities. The current electroencephalogram signal research method is mainly divided into a linear method and a nonlinear analysis, the linear method comprises time domain analysis, frequency domain analysis and frequency domain analysis, the time domain analysis mainly takes waveform characteristics as the leading factor and can only reflect the resolution of the electroencephalogram signal in a time domain, the frequency domain analysis mainly converts the electroencephalogram signal into electroencephalogram power information through time-frequency transformation and explores the change condition and rhythm distribution condition of the electroencephalogram power information along with frequency, the typical representative of the time-frequency domain analysis method is wavelet analysis which can process the electroencephalogram signal in the time domain and the frequency domain, and the nonlinear analysis method of the electroencephalogram signal is often associated with dimension, an Delinum index, an entropy-based analysis method and the like.

The electroencephalogram signal is a time-varying and non-stationary nonlinear dynamic signal, the linear analysis method cannot effectively extract electroencephalogram data characteristics and 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, rhythm signals can be extracted through the wavelet packet transformation, frequency resolution better than that of wavelet transformation is achieved, the stability of the signals is almost not required, the electroencephalogram signal is decomposed through wavelet packets to obtain different rhythm signals, the spectrum entropy of the electroencephalogram signal is calculated through an energy spectrum, the concentration or dispersion degree of a power spectrum of the electroencephalogram signal can be reflected, and each rhythm of the electroencephalogram signal can be effectively extracted and the local complexity of the electroencephalogram signal can be estimated.

The psychotic diseases comprise various subclasses, a large amount of research only aims at performing electroencephalogram analysis on individual symptoms, the method has practical application limitation, a multi-classification prediction model aiming at various manifestations of the psychotic diseases is built, and the method has deep research significance and wide application value.

Disclosure of Invention

The invention aims to provide a method for analyzing and processing electroencephalogram data of schizophrenia and auditory hallucinations thereof, so as to solve the problems in the background technology.

In order to achieve the purpose, the invention provides the following technical scheme:

a method for analyzing and processing electroencephalogram data of schizophrenia and auditory hallucinations thereof comprises the following steps:

acquiring electroencephalogram data;

preprocessing the acquired data;

acquiring multi-scale wavelet energy spectrums of segmented electroencephalogram signals of all leads of an individual;

solving the asymmetry ratio among all leads to obtain the 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 signal;

and (3) analyzing and predicting the electroencephalogram signals of the schizophrenia patients with auditory hallucinations and non-auditory hallucinations or the psychogenic diseases by using a classifier.

As a still further scheme of the invention: the pretreatment comprises the following steps: positioning an electrode; re-referencing; correcting the artifact; filtering; segmenting; deleting errors, tried, deleted and rebuilding bad leads; removing the artifacts; removing the bad section; baseline correction and normalization.

As a still further scheme of the invention: the acquiring of the multi-scale wavelet energy spectrum of the segmented electroencephalogram signals of all leads of an individual comprises the following steps:

performing wavelet packet decomposition on the individual segmented electroencephalogram signals one by one to obtain a multilayer decomposition tree;

and (4) calculating the wavelet packet entropy of the nodes in the tree to obtain the multi-scale wavelet energy spectrum.

As a still further scheme of the invention: and carrying out wavelet packet decomposition on the electroencephalogram signal data to acquire information of different frequency bands.

As a still further scheme of the invention: the step of calculating the asymmetry ratio among the leads comprises the following steps:

the individual segmented electroencephalogram data comprises multi-lead signals, wherein half of the leads are from the left half brain, and half of the leads are from the right lead;

and (3) solving the asymmetric rate as a characteristic value by utilizing the combination of the leads of the left half brain and the right half brain to form a characteristic mode vector of the electroencephalogram signal.

As a still further scheme of the invention: the multi-classifier model is constructed based on the classification accuracy maximization criterion.

As a still further scheme of the invention: the similarity or difference of the electroencephalogram feature pattern vectors of different individuals can be measured by any Euclidean distance.

As a still further scheme of the invention: the multi-classifier is trained and debugged based on schizophrenia auditory hallucinations, schizophrenia non-auditory hallucinations, suspiciousness, depression, impulsivity, anxiety and normal control data.

As a still further scheme of the invention: and (3) carrying out predictive analysis on the electroencephalogram information of the tested individual to obtain the characteristics of the psychotic diseases and the auditory characteristics and classification information of schizophrenia.

Compared with the prior art, the invention has the beneficial effects that: the system comprises a general preprocessing flow, a data extraction flow, model building and classified prediction output, an interactive interface can realize the functions of self-defined preprocessing, signal segmentation selection processing and data analysis, and the system can predict the psychotropic symptoms of schizophrenia and various psychogenic diseases by utilizing the electroencephalogram signals of independent individuals and has practical application significance.

Detailed Description

The following describes in detail various problems involved in the technical solutions of the present invention. It should be noted that the described embodiments are only intended to facilitate the understanding of the invention and do not serve any limiting purpose.

The method collects multi-lead electroencephalogram signal data, and each individual electroencephalogram data is a 2-dimensional signal comprising lead information (1-dimensional) and time information (1-dimensional);

the collection of the EEG signals is completed on an EEG signal collecting instrument with a multi-lead electrode cap;

the specific parameters of the acquisition have no special requirements, the sampling time is preferably 2-4 minutes, and the head is kept still as much as possible in the data acquisition process.

Preprocessing electroencephalogram data, which mainly comprises the following steps: positioning an electrode; re-referencing; carrying out artifact correction on the continuous data; filtering; segmenting; deleting errors, tried, deleted and rebuilding bad leads; removing the 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 multi-lead signals with equal length, the data volume is amplified, and the electroencephalogram signals have dynamic characteristics;

performing wavelet packet decomposition on each lead data of the segmented signals;

wavelet packet transformation can decompose non-stationary signals into weighted sums of base wavelets with different scales, and can ensure higher frequency resolution of a high frequency band;

the wavelet packet function is expressed as:

where ψ (t) mother wavelet is used, and h (t), g (k) are weight coefficients.

The recursive relationships at levels j and j +1 are:

wavelet coefficientComprises the following steps:

thus, the electroencephalogram signal can be expressed as a plurality of wavelet packet sets, which correspond to different frequency ranges and can be changed as needed.

Defining electroencephalogram signal components using wavelet packet nodes

The energy in (1) is:

the total energy of the electroencephalogram signals is as follows:

defining the energy of a particular frequency band (signal subband) as E_sWhich is the sum of the energies of the contained signal components, normalized to be expressed as:

finally defining the wavelet packet entropy as:

S_wp＝-∑p_sln[p_s]

therefore, assuming that the electroencephalogram signal of an individual is represented by P (number of leads) × M (number of signals in a segmented short time window) × N (number of segments), the wavelet packet entropy characteristic thereof is P (number of leads) × N (number of segments), and the leads in each piece of data are from the left and right half brain signals, and assuming that 3 of them are from the left half brain (a1, a2, A3), and 3 of them are from the right half brain (B1, B2, B3), the combinations of the arrangements of the left and right half brains (a1, B1), (a1, B2), (a1, B3), (a2, B1), (a2, B2), (a2, B3), (A3, B1), (A3, B2), (A3, B3), the asymmetry ratio e of the wavelet packet entropy values is found as:

where R represents leads from the right semi-brain and L represents leads from the left semi-brain.

The data used for modeling is 16-lead electroencephalogram signals, and the left half brain and the right half brain are respectively 8-lead, so that 64 characteristic values can be obtained to form a characteristic vector of a signal in a certain frequency band corresponding to a data segment, and wavelet packet entropy and asymmetry rate are repeatedly calculated in different frequency bands to obtain a corresponding characteristic vector;

the segmented brain electrical signals of the final independent individual can be represented by the F (frequency segment number) multiplied by 64 vector.

Constructing an optimal multi-classification model based on a mode recognition classifier, straightening a multi-band feature vector into a one-dimensional feature vector in a training process, and inputting the one-dimensional feature vector into the classifier; considering the influence of the feature dimension on the classifier, the invention adopts a principal component analysis method to reduce the dimension in the implementation process and ensures that the contribution rate of the selected components reaches 99 percent; then, carrying out multi-class training on the segmented data of the training individual by using a Support Vector Machine (SVM);

the SVM kernel function can select a radial basis kernel function, a linear kernel function and the like to define distance measurement, and the distance measurement is output as a posterior probability value and a classification label;

and selecting corresponding parameters according to the classification result of the classifier, and performing cross validation determination on the data set by ten folds to generate a plurality of classifiers, wherein the classification labels are schizophrenia auditory hallucination data, schizophrenia non-auditory hallucination data, suspicion, depression, impulsivity, anxiety and normal control data.

For new test data, firstly, preprocessing to obtain segment data of the electroencephalogram signal to be predicted, and then sequentially predicting all the segment data through the built multi-classifier model system;

because the test data comprises a plurality of segmented results, the classifier also generates a plurality of segmented prediction conditions, and all prediction results are subjected to statistical analysis;

finally, the system outputs probability values of all categories to which the segmented signals belong and top2 decision suggestions, different segmented prediction results can be comprehensively considered in practical application, and the psychotic symptoms of independent individuals can be predicted.

The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art should be considered to be within the technical scope of the present invention, and the technical solutions and the inventive concepts thereof according to the present invention are equivalent to or changed within the technical scope of the present invention.