Spatial-temporal graph convolutional neural network for schizophrenia recognition
10.3969/j.issn.1005-202X.2024.02.016
- VernacularTitle:基于时空图卷积神经网络的精神分裂症识别
- Author:
Xinyi XU
1
;
Bin LI
;
Geng ZHU
;
Yuxing ZHOU
;
Ping LIN
;
Xiao'ou LI
Author Information
1. 上海理工大学健康与科学工程学院,上海 200093
- Keywords:
schizophrenia;
temporal-frequency characteristic;
spatial-frequency characteristic;
graph neural network
- From:
Chinese Journal of Medical Physics
2024;41(2):227-232
- CountryChina
- Language:Chinese
-
Abstract:
A spatial-temporal convolutional neural network-based method is proposed for schizophrenia classification.Unlike the mainstream methods that only analyze the temporal frequency features in EEG and ignore the spatial features between brain regions,the model mainly obtains the spatial-frequency features by convolving the adjacency matrix composed of wavelet coherence coefficients between different channels and EEG sequences,and then extracts the temporal-frequency features through one-dimensional temporal convolution.The processed matrix is flattened after multiple convolutions and input to the classification model.Experimental results show that the method has a classification accuracy of 96.32%on the publicly available dataset Zenodo,demonstrating its effectiveness and exhibiting the advantages of fusing temporal-frequency and spatial-frequency features for schizophrenia diagnosis.
