Abstract

Info

Title:: Spatial-temporal graph convolutional neural network for schizophrenia recognition

Author(s):: XU Xinyi1; LI Bin2; ZHU Geng3; ZHOU Yuxing1; LIN Ping1; LI Xiaoou1; 2; 3; 1. School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China 2. Shanghai Yangpu District Mental Health Center, Shanghai 200093, China 3. School of Medical Instruments, Shanghai University of Medicine and Health Sciences, Shanghai 201318, China

Keywords:: Keywords: schizophrenia temporal-frequency characteristic spatial-frequency characteristic graph neural network

Abstract:: 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.