Identification of adolescent schizophrenia based on EEG entropy features
10.3969/j.issn.1005-202X.2025.08.017
- VernacularTitle:基于脑电熵值特征的青少年精神分裂症识别
- Author:
Xiaoqin LIAN
1
;
Zitong WANG
1
;
Chao GAO
1
;
Mohao CAI
1
;
Jin LI
1
;
Yelan WU
1
Author Information
1. 北京工商大学计算机与人工智能学院,北京 100048;北京工商大学中国轻工业工业互联网与大数据重点实验室,北京 100048
- Publication Type:Journal Article
- Keywords:
electroencephalogram signal;
adolescent;
schizophrenia;
entropy;
convolutional neural network
- From:
Chinese Journal of Medical Physics
2025;42(8):1093-1101
- CountryChina
- Language:Chinese
-
Abstract:
An automated identification method for adolescent schizophrenia based on brain electroencephalogram(EEG)entropy features is proposed for further improving the diagnostic accuracy of adolescent schizophrenia.The raw EEG signals are decomposed into 5 commonly used rhythm bands:Delta,Theta,Alpha,Beta,and Gamma.The permutation entropy,fuzzy entropy,and sample entropy are extracted from each rhythm band and then organized into a feature matrix structured by electrode location×frequency band.Finally,an ECA-CNN model integrating efficient channel attention(ECA)and convolutional neural network(CNN)is constructed for feature classification and realizing the automated identification of adolescent schizophrenia.The results demonstrate that the proposed ECA-CNN model has higher recognition accuracy than the traditional machine learning models,achieving an accuracy of 99.08%,a sensitivity of 99.27%,a specificity of 98.85%,a precision of 99.01%,a F1 score of 99.14%,and a Kappa coefficient of 0.9814.This study provides a new idea and method for the diagnosis of adolescent schizophrenia.
