Spatial–temporal graph convolutional network for Alzheimer classification based on brain functional connectivity imaging of electroencephalogram

Xiaocai Shan, Jun Cao, Shoudong Huo, Liangyu Chen, Ptolemaios Georgios Sarrigiannis, Yifan Zhao*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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Abstract

Functional connectivity of the human brain, representing statistical dependence of information flow between cortical regions, significantly contributes to the study of the intrinsic brain network and its functional mechanism. To fully explore its potential in the early diagnosis of Alzheimer's disease (AD) using electroencephalogram (EEG) recordings, this article introduces a novel dynamical spatial–temporal graph convolutional neural network (ST-GCN) for better classification performance. Different from existing studies that are based on either topological brain function characteristics or temporal features of EEG, the proposed ST-GCN considers both the adjacency matrix of functional connectivity from multiple EEG channels and corresponding dynamics of signal EEG channel simultaneously. Different from the traditional graph convolutional neural networks, the proposed ST-GCN makes full use of the constrained spatial topology of functional connectivity and the discriminative dynamic temporal information represented by the 1D convolution. We conducted extensive experiments on the clinical EEG data set of AD patients and Healthy Controls. The results demonstrate that the proposed method achieves better classification performance (92.3%) than the state-of-the-art methods. This approach can not only help diagnose AD but also better understand the effect of normal ageing on brain network characteristics before we can accurately diagnose the condition based on resting-state EEG.
Original languageEnglish
Pages (from-to)5194-5209
Number of pages16
JournalHuman Brain Mapping
Volume43
Issue number17
Early online date25 Jun 2022
DOIs
Publication statusPublished - 1 Dec 2022

Bibliographical note

Acknowledgment:
This research is based on data provided by the National Institute for Health Research (NIHR) Sheffield Biomedical Research Centre (Translational Neuroscience)/NIHR Sheffield Clinical Research Facility.

Keywords

  • artificial intelligence
  • brain association
  • electroencephalogram
  • graph convolutional neural network
  • machine learning

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