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An explainable and efficient deep learning framework for EEG-based diagnosis of Alzheimer's disease and frontotemporal dementia

Khan, Waqar; Khan, Muhammad Shahbaz; Qasem, Sultan Noman; Ghaban, Wad; Saeed, Faisal; Hanif, Muhammad; Ahmad, Jawad

Authors

Waqar Khan

Sultan Noman Qasem

Wad Ghaban

Faisal Saeed

Muhammad Hanif



Abstract

The early and accurate diagnosis of Alzheimer's Disease and Frontotemporal Dementia remains a critical challenge, particularly with traditional machine learning models which often fail to provide transparency in their predictions, reducing user confidence and treatment effectiveness. To address these limitations, this paper introduces an explainable and lightweight deep learning framework comprising temporal convolutional networks and long short-term memory networks that efficiently classifies Frontotemporal dementia (FTD), Alzheimer's Disease (AD), and healthy controls using electroencephalogram (EEG) data. Feature engineering has been conducted using modified Relative Band Power (RBP) analysis, leveraging six EEG frequency bands extracted through power spectrum density (PSD) calculations. The model achieves high classification accuracies of 99.7% for binary tasks and 80.34% for multi-class classification. Furthermore, to enhance the transparency and interpretability of the framework, SHAP (SHapley Additive exPlanations) has been utilized as an explainable artificial intelligence technique that provides insights into feature contributions.

Citation

Khan, W., Khan, M. S., Qasem, S. N., Ghaban, W., Saeed, F., Hanif, M., & Ahmad, J. (2025). An explainable and efficient deep learning framework for EEG-based diagnosis of Alzheimer's disease and frontotemporal dementia. Frontiers in Medicine, 12, Article 1590201. https://doi.org/10.3389/fmed.2025.1590201

Journal Article Type Article
Acceptance Date Jun 20, 2025
Online Publication Date Jul 15, 2025
Publication Date 2025
Deposit Date Aug 4, 2025
Publicly Available Date Aug 4, 2025
Journal Frontiers in Medicine
Electronic ISSN 2296-858X
Publisher Frontiers Media
Peer Reviewed Peer Reviewed
Volume 12
Article Number 1590201
DOI https://doi.org/10.3389/fmed.2025.1590201
Keywords mental disorders, long short-term memory, EEG, Alzheimer's disease, temporal convolutional networks, frontotemporal dementia, XAI, explainable AI

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Publisher Licence URL
http://creativecommons.org/licenses/by/4.0/

Copyright Statement
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.





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