ArticleDiagnostics (Basel, Switzerland)2024
Classification of Alzheimer's Disease and Frontotemporal Dementia Using Electroencephalography to Quantify Communication between Electrode Pairs.
Article in Diagnostics (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- The AHEPA EEG benchmark: setting the standard for machine learning in dementia diagnosis, a scoping review.Cognitive neurodynamics · 2026Article
- Multimodal Assessment of Consciousness with Brain-Computer Interfaces and Artificial Intelligence: From Acquired Brain Injury to Neurodegenerative Disease.Journal of clinical medicine · 2026Review
- Explainable EEG-based machine learning for early diagnosis of Alzheimer's disease and frontotemporal dementia.Scientific reports · 2026Article
- A Consolidated Framework for the Detection of Alzheimer's Disease Using EEG Signals and Hybrid Models.Biomimetics (Basel, Switzerland) · 2026Article
- Quantitative EEG signatures of power and functional connectivity alterations in Alzheimer's disease and frontotemporal dementia.Scientific reports · 2026Article
- Deep Learning-Based Alzheimer's Disease Detection from Multi-Channel EEG Using Fused Time-Frequency Image Grids.Diagnostics (Basel, Switzerland) · 2026Article
- Integrating attractor dynamics and connectivity features for EEG-based dementia classification.Scientific reports · 2026Article
- EEG-based classification of alzheimer's disease and frontotemporal dementia using functional connectivity.Scientific reports · 2026Article
- Clustered pattern projection for EEG dementia classification: evaluating the reliability of disorder patterns.Frontiers in neuroscience · 2026Article
- NeuroFusionNet: a hybrid EEG feature fusion framework for accurate and explainable Alzheimer's Disease detection.Scientific reports · 2025Article
- CONECT: Novel Weighted Networks Framework Leveraging Angle-Relation Connection (ARC) and Metaheuristic Algorithms for EEG-Based Dementia Classification.Sensors (Basel, Switzerland) · 2025Article
- Enhancing Alzheimer's Diagnosis with Machine Learning on EEG: A Spectral Feature-Based Comparative Analysis.Diagnostics (Basel, Switzerland) · 2025Article
- Translational approach for dementia subtype classification using convolutional neural network based on EEG connectome dynamics.Scientific reports · 2025Article
- A Novel CNN-Based Framework for Alzheimer's Disease Detection Using EEG Spectrogram Representations.Journal of personalized medicine · 2025Article
- An explainable and efficient deep learning framework for EEG-based diagnosis of Alzheimer's disease and frontotemporal dementia.Frontiers in medicine · 2025Article
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3 authors.
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Abstract
Accurate diagnosis of dementia subtypes is crucial for optimizing treatment planning and enhancing caregiving strategies. To date, the accuracy of classifying Alzheimer's disease (AD) and frontotemporal dementia (FTD) using electroencephalogram (EEG) data has been lower than that of distinguishing individuals with these diseases from healthy elderly controls (HCs). This limitation has impeded the feasibility of a cost-effective differential diagnosis for the two subtypes in clinical settings. This study addressed this issue by quantifying communication between electrode pairs in EEG data, along with demographic information, as features to train machine learning (support vector machine) models. Our focus was on refining the feature set specifically for AD-FTD classification. Using our initial feature set, we achieved classification accuracies of 76.9% for AD-HC, 90.4% for FTD-HC, and 91.5% for AD-FTD. Notably, feature importance analyses revealed that the features influencing AD-HC classification are unnecessary for distinguishing between AD and FTD. Eliminating these unnecessary features improved the classification accuracy of AD-FTD to 96.6%. We concluded that communication between electrode pairs specifically involved in the neurological pathology of FTD, but not AD, enables highly accurate EEG-based AD-FTD classification.
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