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

Khan, Waqar and Khan, Muhammad Shahbaz and Qasem, Sultan Noman and Ghaban, Wad and Saeed, Faisal and Hanif, Muhammad and Ahmad, Jawad (2025) An explainable and efficient deep learning framework for EEG-based diagnosis of Alzheimer's disease and frontotemporal dementia. Frontiers in Medicine, 12. ISSN 2296-858X

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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.

Item Type:	Article
Identification Number:	10.3389/fmed.2025.1590201
Dates:	Date Event 20 June 2025 Accepted 15 July 2025 Published Online
Uncontrolled Keywords:	explainable AI, XAI, Alzheimer's disease, temporal convolutional networks, long short-term memory, frontotemporal dementia, EEG, mental disorders
Subjects:	CAH11 - computing > CAH11-01 - computing > CAH11-01-01 - computer science
Divisions:	Architecture, Built Environment, Computing and Engineering > Computer Science
Depositing User:	Gemma Tonks
Date Deposited:	20 Aug 2025 08:50
Last Modified:	20 Aug 2025 08:50
URI:	https://www.open-access.bcu.ac.uk/id/eprint/16610

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