ArticleCognitive neurodynamics2024
EEG-based classification of Alzheimer's disease and frontotemporal dementia: a comprehensive analysis of discriminative features.
Article in Cognitive neurodynamics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 3 of them syntheses that pooled it.
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Who cites it
20 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- From Single-Modal to Multi-Modal Artificial Intelligence in Alzheimer's Disease: A Systematic Review of Databases, Modalities, Diagnostic Performance, and Clinical Translation Challenges.Sensors (Basel, Switzerland) · 2026Pooled it
- The EEG analysis and identification of Alzheimer's disease: a review.Frontiers in aging neuroscience · 2025Pooled it
- Frontotemporal dementia: a systematic review of artificial intelligence approaches in differential diagnosis.Frontiers in aging neuroscience · 2025Pooled it
- The AHEPA EEG benchmark: setting the standard for machine learning in dementia diagnosis, a scoping review.Cognitive neurodynamics · 2026Article
- Spectral and Directed Connectivity EEG Markers for Classifying Alzheimer's Disease, Frontotemporal Dementia, and Healthy Controls with Exploratory Photobiomodulation Case-Study Projection.Sensors (Basel, Switzerland) · 2026Article
- Generalizability of EEG-Based DEMENTIA Classifiers: A Multicenter study of Alzheimer's, MCI, and FTD.medRxiv : the preprint server for health sciences · 2026Article
- A Consolidated Framework for the Detection of Alzheimer's Disease Using EEG Signals and Hybrid Models.Biomimetics (Basel, Switzerland) · 2026Article
- Integrating attractor dynamics and connectivity features for EEG-based dementia classification.Scientific reports · 2026Article
- Deep Learning-Based Alzheimer's Disease Detection from Multi-Channel EEG Using Fused Time-Frequency Image Grids.Diagnostics (Basel, Switzerland) · 2026Article
- Multi-Entropy Feature Concatenation for Data-Efficient Cross-Subject Classification of Alzheimer's Disease and Frontotemporal Dementia from Single-Channel EEG.Entropy (Basel, Switzerland) · 2026Article
- Multi-dimensional EEG analysis reveals distinct neurophysiological patterns in Alzheimer's and frontotemporal dementia.Journal of neuroengineering and rehabilitation · 2026Article
- EEG-based classification of alzheimer's disease and frontotemporal dementia using functional connectivity.Scientific reports · 2026Article
- Interpretable AI for neural signal decoding in dementia: an EEG ensemble approach to differential diagnosis.Frontiers in neuroinformatics · 2026Article
- Enhancing Alzheimer's Diagnosis with Machine Learning on EEG: A Spectral Feature-Based Comparative Analysis.Diagnostics (Basel, Switzerland) · 2025Article
- A Deep Learning Approach to Alzheimer's Diagnosis Using EEG Data: Dual-Attention and Optuna-Optimized SVM.Biomedicines · 2025Article
- Translational approach for dementia subtype classification using convolutional neural network based on EEG connectome dynamics.Scientific reports · 2025Article
- Deep learning-based classification of dementia using image representation of subcortical signals.BMC medical informatics and decision making · 2025Article
- A Novel CNN-Based Framework for Alzheimer's Disease Detection Using EEG Spectrogram Representations.Journal of personalized medicine · 2025Article
- From early symptoms to EEG silence: tracking the neurodegenerative course of sporadic Creutzfeldt-Jakob disease.Frontiers in human neuroscience · 2025Article
- Privacy-preserving dementia classification from EEG via hybrid-fusion EEGNetv4 and federated learning.Frontiers in computational neuroscience · 2025Article
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3 authors.
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Abstract
Alzheimer's disease (AD) and frontotemporal dementia (FTD) are two main types of dementia. These diseases have similar symptoms, and they both may be considered as AD. Early detection of dementia and differential diagnosis between AD and FTD can lead to more effective management of the disease and contributes to the advancement of knowledge and potential treatments. In this approach, several features were extracted from electroencephalogram (EEG) signals of 36 subjects diagnosed with AD, 23 FTD subjects, and 29 healthy controls (HC). Mann-Whitney U-test and t-test methods were employed for the selection of the best discriminative features. The Fp1 channel for FTD patients exhibited the most significant differences compared to AD. In addition, connectivity features in the delta and alpha subbands indicated promising discrimination among these two groups. Moreover, for dementia diagnosis (AD + FTD vs. HC), central brain regions including Cz and Pz channels proved to be determining for the extracted features. Finally, four machine learning (ML) algorithms were utilized for the classification purpose. For differentiating between AD and FTD, and dementia diagnosis, an accuracy of 87.8% and 93.5% were achieved respectively, using the tenfold cross-validation technique and employing support vector machines (SVM) as the classifier.
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