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Deep learning techniques for automated Alzheimer's and mild cognitive impairment disease using EEG signals: A comprehensive review of the last decade (2013 - 2024)

Acharya M 1 , Deo RC 2 , Tao X 3 , Barua PD 4 , Devi A 5 , Atmakuru A 6 Show all authors , Tan RS 7

Affiliations 

  • 1 School of Mathematics, Physics and Computing, University of Southern Queensland, Springfield Central, QLD 4300, Australia. Electronic address: madhav.acharya@unisq.edu.au
  • 2 School of Mathematics, Physics and Computing, University of Southern Queensland, Springfield Central, QLD 4300, Australia. Electronic address: ravinesh.deo@unisq.edu.au
  • 3 School of Mathematics, Physics and Computing, University of Southern Queensland, Toowoomba QLD 4350, Australia
  • 4 School of Mathematics, Physics and Computing, University of Southern Queensland, Springfield Central, QLD 4300, Australia; Cogninet AI, Sydney, NSW 2010, Australia; Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007, Australia; School of Computing and Information Science, Anglia Ruskin University Cambridge Campus, UK; Australian International Institute of Higher Education, Sydney, NSW 2000, Australia; School of Science Technology, University of New England, Australia; School of Biosciences, Taylor's University, Malaysia; School of Computing, SRM Institute of Science and Technology, India; School of Science and Technology, Kumamoto University, Japan; Sydney School of Education and Social Work, University of Sydney, Australia
  • 5 School of Education and Tertiary Access, University of the Sunshine Coast, Petrie, QLD 4502, Australia
  • 6 University of Massachusetts, Amherst, USA
  • 7 National Heart Centre Singapore, Singapore, Duke-NUS Medical School, Singapore
Comput Methods Programs Biomed, 2025 Feb;259:108506.
PMID: 39581069 DOI: 10.1016/j.cmpb.2024.108506

Abstract

BACKGROUND AND OBJECTIVES: Mild Cognitive Impairment (MCI) and Alzheimer's Disease (AD) are progressive neurological disorders that significantly impair the cognitive functions, memory, and daily activities. They affect millions of individuals worldwide, posing a significant challenge for its diagnosis and management, leading to detrimental impacts on patients' quality of lives and increased burden on caregivers. Hence, early detection of MCI and AD is crucial for timely intervention and effective disease management.

METHODS: This study presents a comprehensive systematic review focusing on the applications of deep learning in detecting MCI and AD using electroencephalogram (EEG) signals. Through a rigorous literature screening process based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, the research has investigated 74 different papers in detail to analyze the different approaches used to detect MCI and AD neurological disorders.

RESULTS: The findings of this study stand out as the first to deal with the classification of dual MCI and AD (MCI+AD) using EEG signals. This unique approach has enabled us to highlight the state-of-the-art high-performing models, specifically focusing on deep learning while examining their strengths and limitations in detecting the MCI, AD, and the MCI+AD comorbidity situations.

CONCLUSION: The present study has not only identified the current limitations in deep learning area for MCI and AD detection but also proposes specific future directions to address these neurological disorders by implement best practice deep learning approaches. Our main goal is to offer insights as references for future research encouraging the development of deep learning techniques in early detection and diagnosis of MCI and AD neurological disorders. By recommending the most effective deep learning tools, we have also provided a benchmark for future research, with clear implications for the practical use of these techniques in healthcare.

* Title and MeSH Headings from MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine.

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