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

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dc.contributor.authorShan, Xiaocai
dc.contributor.authorCao, Jun
dc.contributor.authorHuo, Shoudong
dc.contributor.authorChen, Liangyu
dc.contributor.authorSarrigiannis, Ptolemaios Georgios
dc.contributor.authorZhao, Yifan
dc.date.accessioned2022-06-30T10:44:17Z
dc.date.available2022-06-30T10:44:17Z
dc.date.issued2022-06-25
dc.description.abstractFunctional 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.en_UK
dc.identifier.citationShan X, Cao J, Huo S, et al., (2022) Spatial–temporal graph convolutional network for Alzheimer classification based on brain functional connectivity imaging of electroencephalogram. Human Brain Mapping, Volume 43, Issue 17, 1 December 2022, pp. 5194-5209en_UK
dc.identifier.issn1065-9471
dc.identifier.urihttps://doi.org/10.1002/hbm.25994
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/18103
dc.language.isoenen_UK
dc.publisherWileyen_UK
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectartificial intelligenceen_UK
dc.subjectbrain associationen_UK
dc.subjectelectroencephalogramen_UK
dc.subjectgraph convolutional neural networken_UK
dc.subjectmachine learningen_UK
dc.titleSpatial–temporal graph convolutional network for Alzheimer classification based on brain functional connectivity imaging of electroencephalogramen_UK
dc.typeArticleen_UK

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