ArticleDiagnostics (Basel, Switzerland)2021
Alzheimer's Disease and Frontotemporal Dementia: A Robust Classification Method of EEG Signals and a Comparison of Validation Methods.
Article in Diagnostics (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 papers, 1 of them a synthesis that pooled it.
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Who cites it
53 citing papers in PubMed, 1 synthesis or guideline pooled 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
- A CNN-transformer fusion for EEG-based discrimination of Alzheimer's and frontotemporal dementia.Physical and engineering sciences in medicine · 2026Article
- Cross-Modal Alignment and Rectified Flow-Based Latent Representation Synthesis for Enhanced Speech-Driven Alzheimer's Disease Detection.Bioengineering (Basel, Switzerland) · 2026Article
- Quantitative EEG signatures of power and functional connectivity alterations in Alzheimer's disease and frontotemporal dementia.Scientific reports · 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
- Robust Motor Imagery-Brain-Computer Interface Classification in Signal Degradation: A Multi-Window Ensemble Approach.Biomimetics (Basel, Switzerland) · 2025Article
- FHESA: fourier decomposition and hilbert transform based EEG signal analysis for Alzheimer's disease detection.Physical and engineering sciences in medicine · 2025Article
- Predicting future risk of developing cognitive impairment using ambulatory sleep EEG: Integrating univariate analysis and multivariate information theory approach.Journal of Alzheimer's disease : JAD · 2025Article
- Nanomedicine-Driven Modulation of the Gut-Brain Axis: Innovative Approaches to Managing Chronic Inflammation in Alzheimer's and Parkinson's Disease.International journal of molecular sciences · 2025Review
- Transparent EEG Analysis: Leveraging Autoencoders, Bi-LSTMs, and SHAP for Improved Neurodegenerative Diseases Detection.Sensors (Basel, Switzerland) · 2025Article
- A dual path graph neural network framework for dementia diagnosis.Scientific reports · 2025Article
- Connectogram-COH: A Coherence-Based Time-Graph Representation for EEG-Based Alzheimer's Disease Detection.Diagnostics (Basel, Switzerland) · 2025Article
- Machine Learning Algorithms in EEG Analysis of Kleefstra Syndrome: Current Evidence and Future Directions.Sensors (Basel, Switzerland) · 2025Review
- 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
- Sink-index: a network-based EEG marker for frontotemporal dementia and Alzheimer's disease.Brain communications · 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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7 authors.
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Abstract
Dementia is the clinical syndrome characterized by progressive loss of cognitive and emotional abilities to a degree severe enough to interfere with daily functioning. Alzheimer's disease (AD) is the most common neurogenerative disorder, making up 50-70% of total dementia cases. Another dementia type is frontotemporal dementia (FTD), which is associated with circumscribed degeneration of the prefrontal and anterior temporal cortex and mainly affects personality and social skills. With the rapid advancement in electroencephalogram (EEG) sensors, the EEG has become a suitable, accurate, and highly sensitive biomarker for the identification of neuronal and cognitive dynamics in most cases of dementia, such as AD and FTD, through EEG signal analysis and processing techniques. In this study, six supervised machine-learning techniques were compared on categorizing processed EEG signals of AD and FTD cases, to provide an insight for future methods on early dementia diagnosis. K-fold cross validation and leave-one-patient-out cross validation were also compared as validation methods to evaluate their performance for this classification problem. The proposed methodology accuracy scores were 78.5% for AD detection with decision trees and 86.3% for FTD detection with random forests.
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