[1]廉小亲,王梓桐,高超,等.基于脑电熵值特征的青少年精神分裂症识别[J].中国医学物理学杂志,2025,42(8):1093-1101.[doi:DOI:10.3969/j.issn.1005-202X.2025.08.017]
　LIAN Xiaoqin,WANG Zitong,et al.Identification of adolescent schizophrenia based on EEG entropy features[J].Chinese Journal of Medical Physics,2025,42(8):1093-1101.[doi:DOI:10.3969/j.issn.1005-202X.2025.08.017]
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基于脑电熵值特征的青少年精神分裂症识别()

《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
42
期数:
2025年第8期
页码:
1093-1101
栏目:
医学信号处理与医学仪器
出版日期:
2025-08-30

文章信息/Info

Title:
Identification of adolescent schizophrenia based on EEG entropy features
文章编号:
1005-202X（2025）08-1093-09
作者:
廉小亲1; 2; 王梓桐1; 2; 高超1; 2; 蔡沫豪1; 2; 李进1; 2; 吴叶兰1; 2
1.北京工商大学计算机与人工智能学院， 北京 100048； 2.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
Author(s):
LIAN Xiaoqin1;  2;  WANG Zitong1;  2;  GAO Chao1;  2;  CAI Mohao1;  2;  LI Jin1;  2;  WU Yelan1;  2
1. School of Computer and Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China 2. Key Laboratory of Industrial Internet and Big Data, China National Light Industry, Beijing Technology and Business University, Beijing 100048, China
关键词:
脑电信号; 青少年; 精神分裂症; 熵; 卷积神经网络
Keywords:
Keywords: electroencephalogram signal adolescent schizophrenia entropy convolutional neural network
分类号:
R318；TP391.9
DOI:
DOI:10.3969/j.issn.1005-202X.2025.08.017
文献标志码:
A
摘要:
为进一步提升对青少年精神分裂症的识别准确率，提出一种基于脑电熵值特征的青少年精神分裂症自动识别方法。首先将脑电信号分解为Delta、Theta、Alpha、Beta、Gamma 5个常用的节律波段，其次分别提取脑电信号每个节律波段的排列熵、模糊熵和样本熵作为特征，并将脑电熵值特征按照电极和频段顺序构建为特征矩阵，最后设计一种基于高效通道注意力机制模块（ECA）和卷积神经网络（CNN）的ECA-CNN模型对特征矩阵进行分类，完成对青少年精神分裂症的自动识别。结果表明，ECA-CNN模型对病症的识别准确率、敏感性、特异性、精确率、F1分数和Kappa系数分别可以达到99.08%、99.27%、98.85%、99.01%、99.14%和0.981 4，相较于传统的机器学习模型具有更高的识别准确率，为青少年精神分裂症的诊断提供一种新的思路和方法。
Abstract:
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 0.981 4. This study provides a new idea and method for the diagnosis of adolescent schizophrenia.

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备注/Memo

备注/Memo:
【收稿日期】2025-02-21 【基金项目】北京市自然科学基金（6214034）；北京市教育委员会科学研究计划项目（KM202310011002） 【作者简介】廉小亲，博士，教授，研究生导师，研究方向：智能信息处理技术，E-mail: lianxq@263.net 【通信作者】高超，博士，副教授，研究生导师，研究方向：智能检测与数据挖掘，E-mail: gaochao9158@btbu.edu.cn
更新日期/Last Update: 2025-09-15