An explainable and efficient deep learning framework for EEG-based diagnosis of Alzheimer's disease and frontotemporal dementia
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...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-07-01
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| Series: | Frontiers in Medicine |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmed.2025.1590201/full |
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| author | Waqar Khan Muhammad Shahbaz Khan Sultan Noman Qasem Sultan Noman Qasem Wad Ghaban Faisal Saeed Muhammad Hanif Jawad Ahmad |
| author_facet | Waqar Khan Muhammad Shahbaz Khan Sultan Noman Qasem Sultan Noman Qasem Wad Ghaban Faisal Saeed Muhammad Hanif Jawad Ahmad |
| author_sort | Waqar Khan |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-1d1c9be5de934476ad79aee29de4e820 |
| institution | DOAJ |
| issn | 2296-858X |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Medicine |
| spelling | doaj-art-1d1c9be5de934476ad79aee29de4e8202025-08-20T02:39:25ZengFrontiers Media S.A.Frontiers in Medicine2296-858X2025-07-011210.3389/fmed.2025.15902011590201An explainable and efficient deep learning framework for EEG-based diagnosis of Alzheimer's disease and frontotemporal dementiaWaqar Khan0Muhammad Shahbaz Khan1Sultan Noman Qasem2Sultan Noman Qasem3Wad Ghaban4Faisal Saeed5Muhammad Hanif6Jawad Ahmad7Department of Cybersecurity, Pakistan Navy Engineering College, National University of Sciences and Technology, Karachi, PakistanSchool of Computing, Engineering and the Built Environment, Edinburgh Napier University, Edinburgh, United KingdomComputer Science Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi ArabiaKing Salman Center for Disability Research, Riyadh, Saudi ArabiaApplied College, University of Tabuk, Tabuk, Saudi ArabiaCollege of Computing, Birmingham City University, Birmingham, United KingdomDepartment of Informatics, School of Business, Örebro Universitet, Örebro, SwedenCybersecurity Center, Prince Mohammad Bin Fahd University, Al-Khobar, Saudi ArabiaThe 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.https://www.frontiersin.org/articles/10.3389/fmed.2025.1590201/fullexplainable AIXAIAlzheimer's diseasetemporal convolutional networkslong short-term memoryfrontotemporal dementia |
| spellingShingle | Waqar Khan Muhammad Shahbaz Khan Sultan Noman Qasem Sultan Noman Qasem Wad Ghaban Faisal Saeed Muhammad Hanif Jawad Ahmad An explainable and efficient deep learning framework for EEG-based diagnosis of Alzheimer's disease and frontotemporal dementia Frontiers in Medicine explainable AI XAI Alzheimer's disease temporal convolutional networks long short-term memory frontotemporal dementia |
| title | An explainable and efficient deep learning framework for EEG-based diagnosis of Alzheimer's disease and frontotemporal dementia |
| title_full | An explainable and efficient deep learning framework for EEG-based diagnosis of Alzheimer's disease and frontotemporal dementia |
| title_fullStr | An explainable and efficient deep learning framework for EEG-based diagnosis of Alzheimer's disease and frontotemporal dementia |
| title_full_unstemmed | An explainable and efficient deep learning framework for EEG-based diagnosis of Alzheimer's disease and frontotemporal dementia |
| title_short | An explainable and efficient deep learning framework for EEG-based diagnosis of Alzheimer's disease and frontotemporal dementia |
| title_sort | explainable and efficient deep learning framework for eeg based diagnosis of alzheimer s disease and frontotemporal dementia |
| topic | explainable AI XAI Alzheimer's disease temporal convolutional networks long short-term memory frontotemporal dementia |
| url | https://www.frontiersin.org/articles/10.3389/fmed.2025.1590201/full |
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