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

Madhav Acharya; Ravinesh C Deo; Xiaohui Tao; Prabal Datta Barua; Aruna Devi; Anirudh Atmakuru; Ru-San Tan

doi:10.1016/j.cmpb.2024.108506

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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)

Journal article

Open access

Peer reviewed

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

Madhav Acharya, Ravinesh C Deo, Xiaohui Tao, Prabal Datta Barua, Aruna Devi, Anirudh Atmakuru and Ru-San Tan

Computer Methods and Programs in Biomedicine, Vol.259, pp.1-18

2025

DOI: https://doi.org/10.1016/j.cmpb.2024.108506

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Abstract

Alzheimer's disease (AD)

Artificial intelligence

Deep learning

EEG signals

Mild cognitive impairment (MCI)

neurological diseases

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.

Details

Title: Deep learning techniques for automated Alzheimer's and mild cognitive impairment disease using EEG signals: A comprehensive review of the last decade (2013 - 2024)
Authors: Madhav Acharya (Corresponding Author) - University of Southern Queensland
Ravinesh C Deo (Corresponding Author) - University of Southern Queensland
Xiaohui Tao - University of Southern Queensland
Prabal Datta Barua - University of Southern Queensland
Aruna Devi - University of the Sunshine Coast, Queensland, School of Education and Tertiary Access
Anirudh Atmakuru - University of Massachusetts Amherst
Ru-San Tan - Duke-NUS Medical School
Publication details: Computer Methods and Programs in Biomedicine, Vol.259, pp.1-18
Publisher: Elsevier Ireland Ltd.
Date published: 2025
DOI: 10.1016/j.cmpb.2024.108506
ISSN: 1872-7565; 0169-2607
Grant note: The first author has received the University of Southern Queensland (UniSQ) Domestic PhD Research Scholarship (2023-2026) and the Research and Training Program (RTP) Scholarship funded by the Australian Government.
Organisation Unit: Indigenous and Transcultural Research Centre; School of Education and Tertiary Access
Language: English
Record Identifier: 991081498302621
Output Type: Journal article

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Web Of Science research areas: Computer Science, Interdisciplinary Applications; Computer Science, Theory & Methods; Engineering, Biomedical; Medical Informatics

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