EEGConvNeXt: A novel convolutional neural network model for automated detection of Alzheimer's Disease and Frontotemporal Dementia using EEG signals.

Journal: Computer methods and programs in biomedicine
PMID: 39938252

Abstract

BACKGROUND AND OBJECTIVE: Deep learning models have gained widespread adoption in healthcare for accurate diagnosis through the analysis of brain signals. Neurodegenerative disorders like Alzheimer's Disease (AD) and Frontotemporal Dementia (FD) are increasingly prevalent due to age-related brain volume reduction. Despite advances, existing models often lack comprehensive multi-class classification capabilities and are computationally expensive. This study addresses these gaps by proposing EEGConvNeXt, a novel convolutional neural network (CNN) model for detecting AD and FD using electroencephalogram (EEG) signals with high accuracy.

Authors

  • Madhav Acharya
    School of Mathematics, Physics and Computing, University of Southern Queensland, Springfield Central, QLD 4300, Australia. Electronic address: madhav.acharya@unisq.edu.au.
  • Ravinesh C Deo
    School of Agricultural Computational and Environmental Sciences, International Centre of Applied Climate Science (ICACS), University of Southern Queensland, Springfield, QLD, 4300, Australia. ravinesh.deo@usq.edu.au.
  • Prabal Datta Barua
    Cogninet Australia, Sydney, NSW 2010 Australia.
  • Aruna Devi
    School of Education and Tertiary Access, University of the Sunshine Coast, Caboolture Campus, Australia.
  • Xiaohui Tao
    School of Sciences, University of Southern Queensland, Toowoomba 4350, Australia.

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