北京生物医学工程2025，Vol.44Issue(1)：26-31,48,7.DOI:10.3969/j.issn.1002-3208.2025.01.004
基于图卷积神经网络的精神分裂症识别研究
Recognition of schizophrenia based on graph convolutional neural network
摘要
Abstract
Objective Patients with schizophrenia(SZ)suffer from cognitive deficits in working memory,information processing,and selective learning,which are still clinically diagnosed by doctors assessed by scales.In this paper,we propose an auxiliary diagnosis method for schizophrenia based on brain functional connectivity and graph convolution neural network(GCN)without relying on artificial features to realize the automatic classification of schizophrenia.Methods Due to the natural similarity between brain network graphs and graph data,in this paper,we obtained event-related potential(ERP)from a reinforcement learning task with 42 schizophrenia patients and 29 healthy controls(HC),constructed functional connectivity matrices using phase lag indices with electrodes as nodes,and constructed brain network graph data by combining node features,which were inputted into a graph convolutional neural network model for training classification.Results The classification accuracy,precision,Fl score and specificity of SZ and HC when using power spectral density as node features under the GCN model were 84.21％,75％,85.71％and 70％,respectively.The accuracy was improved by 6.43％compared to choosing the original electroencephalogram(EEG)vector as the node feature.The GCN model also improved the accuracy by 3.18％compared to using the random forest classifier.Conclusions In this paper,graph neural network is used to classify EEG signals,and the experimental results show that GCN can effectively recognize SZ patients and realize automatic classification of SZ patients.And the selection of node features under the graph structure has a significant improvement on the classification accuracy relative to the traditional machine learning model,and the effect is better.关键词
脑功能连接/图神经网络/脑电图/精神分裂症Key words
brain functional connection/graph convolution neural network(GCN)/electroencephalogram(EEG)/schizophrenia分类
医药卫生引用本文复制引用
林萍,朱耿,李斌,周宇星,徐信毅,李晓欧..基于图卷积神经网络的精神分裂症识别研究[J].北京生物医学工程,2025,44(1):26-31,48,7.基金项目
上海市科委地方院校能力建设项目(22010502400)、上海市杨浦区科学技术委员会、卫生健康委员会科研项目(YPM202114)、上海健康医学院精神卫生临床研究中心项目(20MC2020005)资助 （22010502400）
