Adaptive Gated Graph Convolutional Network for Explainable Diagnosis of Alzheimer’s Disease using EEG Data

Dominik Klepl, Fei He, Min Wu, Daniel J. Blackburn, Ptolemaios G. Sarrigiannis

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Abstract

Graph neural network (GNN) models are increasingly being used for the classification of electroencephalography (EEG) data. However, GNN-based diagnosis of neurological disorders, such as Alzheimer’s disease (AD), remains a relatively unexplored area of research. Previous studies have relied on functional connectivity methods to infer brain graph structures and used simple GNN architectures for the diagnosis of AD. In this work, we propose a novel adaptive gated graph convolutional network (AGGCN) that can provide explainable predictions. AGGCN adaptively learns graph structures by combining convolution-based node feature enhancement with a correlation-based measure of power spectral density similarity. Furthermore, the gated graph convolution can dynamically weigh the contribution of various spatial scales. The proposed model achieves high accuracy in both eyes-closed and eyes-open conditions, indicating the stability of learned representations. Finally, we demonstrate that the proposed AGGCN model generates consistent explanations of its predictions that might be relevant for further study of AD-related alterations of brain networks.
Original languageEnglish
Article number37792656
Pages (from-to)3978-3987
Number of pages10
JournalIEEE Transactions on Neural Systems and Rehabilitation Engineering
Volume31
DOIs
Publication statusPublished - 4 Oct 2023

Bibliographical note

This is an Open Access article distributed under the terms of the Creative
Commons Attribution License (http://creativecommons.org/licenses/by/4.0/),
which permits unrestricted use, distribution, and reproduction in any medium,
provided the original work is properly cited.

Keywords

  • Biomedical Engineering
  • General Neuroscience
  • Internal Medicine
  • Rehabilitation
  • Alzheimer’s disease
  • EEG
  • Electroencephalography
  • Graph neural networks
  • classification
  • Diseases
  • Electrodes
  • Logic gates
  • Brain modeling
  • graph neural network
  • Convolutional neural networks

ASJC Scopus subject areas

  • Rehabilitation
  • Neuroscience(all)
  • Internal Medicine
  • Biomedical Engineering

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