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

Topic: Amyloid, tau and blood-test diagnosis

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.

Key findings

• 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.

Why it matters

A deep learning framework classified Alzheimer's disease, frontotemporal dementia, and healthy controls from EEG data with accuracies of 99.7% for binary tasks and 80.34% for multi-class classification. This is a diagnostic classification method and does not treat, cure, or prevent Alzheimer's disease.

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Study details

• Effect size99.7%; 80.34%
• Evidence strengthLow

Read the original

• DOI: 10.3389/fmed.2025.1590201
• PubMed: 40735445
• Original source →
• Free full text: Frontiers in Medicine →

Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).

@article{khan2025-40735445,
  author  = {Khan, Waqar Ali and Khan, Muhammad Shahbaz and Qasem, Sultan Noman and Ghaban, Wad and Saeed, Faisal and Hanif, Muhammad Fainan and Ahmad, Jawad Elsayed},
  title   = {{An explainable and efficient deep learning framework for EEG-based diagnosis of Alzheimer's disease and frontotemporal dementia}},
  journal = {Frontiers in Medicine},
  year    = {2025},
  doi     = {10.3389/fmed.2025.1590201},
  pmid    = {40735445},
  url     = {https://doi.org/10.3389/fmed.2025.1590201}
}

Studies this one cites

Referenced by this study and also in the alzhub library, so you can read them here.

• 2013 Alzheimer's disease facts and figures Alzheimer's & dementia : the journal of the Alzheimer's Association · 2013-03-01 · usefulness score 62/100 · How common it is
• Frontotemporal dementia Lancet (London, England) · 2015-10-01 · usefulness score 59/100 · Getting a diagnosis
• A clinicopathological approach to the diagnosis of dementia Nature reviews. Neurology · 2017-07-14 · usefulness score 50/100 · Getting a diagnosis
• Alzheimer's Disease and Frontotemporal Dementia: A Robust Classification Method of EEG Signals and a Comparison of Validation Methods Diagnostics (Basel, Switzerland) · 2021-08-09 · usefulness score 50/100 · Digital health tools

In the news

1 news article on alzhub's news page cites this paper.

• New AI technology can provide rapid and reliable dementia diagnosis Medical Xpress · November 28, 2025

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