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Electroencephalogram characteristic screening method for identifying addiction population, and addiction evaluation method and system
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A technology of EEG and crowd, applied in the field of medical diagnosis
Pending Publication Date: 2021-09-14
SHANGHAI MENTAL HEALTH CENT (SHANGHAI PSYCHOLOGICAL COUNSELLING TRAINING CENT)
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However, these indicators are not really used in clinical practice at present, and few studies have integrated these indicators to reflect the disease-specificity of substance dependence.
In the traditional analysis of EEG physiological data, the mining of these indicators helps us understand substance dependence. However, EEG indicators have high time resolution and can be transformed into broader indicators to reflect the characteristics of diseases. These complex and diverse Indicators are difficult to be directly understood by the human brain, but the method of big data analysis and machine learning may provide important help for the effective auxiliary diagnosis of substance addiction, and promote the application of objective indicators in clinical psychiatry
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Embodiment 1
[0071] see figure 1 with image 3 , the present embodiment provides a method for screening EEG characteristics for identifying addicted people, including:
[0072] Collect multiple samples of data from the population including addicted individuals and normal individuals, and the sample data includes raw EEG data with multiple characteristics; de-interference and filtering are performed on each raw EEG data, and the preset frequency range is extracted. EEG data: multiple effective features are extracted from each piece of EEG data, and the effective features include the characteristic indicators used to reflect the EEG trend at each lead position, and the EEG changes corresponding to different bands at each lead position Data and the weighted phase lag index on each band of any two lead positions; the feature combination scheme obtained by removing any one or more types of effective features is sequentially calculated by using the significance test method, and each feature com...
Embodiment 2
[0117] see figure 2 , the present embodiment provides a method for evaluating addiction, including:
[0118] Obtain sample data from a plurality of sample populations based on the effective feature types screened out, and divide the multiple sample data into a training set, a test set and a verification set;
[0119] According to the training set and the test set, respectively utilize a plurality of preset model structures to carry out the training of the evaluation model;
[0120] Use the verification set to verify the evaluation model of each model structure, and select the evaluation model with the highest accuracy as the addiction evaluation model;
[0121] Obtain the detection sample of the user to be tested based on the effective feature category screened out, and judge the user to be tested as an addicted individual or a normal individual by the addiction assessment model.
[0122] Compared with the prior art, the beneficial effect of the addiction assessment method ...
Embodiment 3
[0124] This embodiment provides an EEG feature screening system for identifying addicts, including:
[0125] The sample collection unit is used to collect a plurality of sample data from a crowd including addicted individuals and normal individuals, and the sample data includes raw EEG data of multiple characteristics;
[0126] The sample processing unit is used to perform de-interference and filtering processing for each original EEG data, and extract EEG data in a preset frequency range;
[0127] The feature extraction unit is used to extract a plurality of effective features from each piece of EEG data, the effective features include each lead position is used to reflect the feature index of the EEG trend, and each lead position corresponds to different bands. The EEG change data and the weighted phase lag index on each band of any two lead positions;
[0128] The feature screening unit is used to sequentially calculate the feature combination scheme obtained by removing a...
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Abstract
The invention discloses an electroencephalogram featurescreening method, an addiction evaluation method and an addictionevaluation system for identifying addicted people, relates to the technical field of medical equipment, and aims at screening out electroencephalogram features capable of identifying the addicted people by a user from numerous electroencephalogram features and improving the identification accuracy of the addicted people. The electroencephalogram featurescreening method for identifying the addicted population comprises the following steps: collecting a plurality of sample data from a population comprising an addicted individual and a normal individual; performing interference removal and filtering processing on each part of original electroencephalogram data, and extracting electroencephalogram data in a preset frequency range; extracting a plurality of effective features from each part of electroencephalogram data; sequentially calculating feature combination schemes obtained by removing any one or more types of effective features, and respectively calculating a model complexityscore corresponding to each feature combination scheme in multiple pieces of sample data, and screening out a feature combination scheme based on the model complexityscore, and taking the effective features in the feature combination scheme as electroencephalogram screening features for identifying the addictionpopulation.
Description
technical field [0001] The present invention relates to the technical field of medical diagnosis, in particular to an EEG featurescreening method for identifying addicts, a method and system for identifying addicts. Background technique [0002] The etiology and pathological mechanism of drug dependence are very complex. During the development of drug dependence, a series of neuroadaptive changes occur in the central nervous system at the molecular, cellular, and circuit levels. Different stages of the disease have different neurobiological basis. Among them, methamphetamine has strong neurotoxicity, and chronic methamphetamine use leads to cognitive impairment, brain functional structure and brain function abnormalities, etc. These pathological changes lead to drug-related attentional bias, impulsive and inhibitory function decline, etc. , so that drug addicts fall into a vicious cycle of drug use-withdrawal-relapse. In terms of treating MA dependence, there is still a la...
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