ArticleBrain sciences2024
A Comparative Study on Feature Extraction Techniques for the Discrimination of Frontotemporal Dementia and Alzheimer's Disease with Electroencephalography in Resting-State Adults.
Article in Brain sciences, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 2 of them syntheses that pooled it.
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Who cites it
21 citing papers in PubMed, 2 syntheses or guidelines pooled it, 38 citations in OpenAlex.
- Frontotemporal dementia: a systematic review of artificial intelligence approaches in differential diagnosis.Frontiers in aging neuroscience · 2025Pooled it
- The EEG analysis and identification of Alzheimer's disease: a review.Frontiers in aging neuroscience · 2025Pooled it
- Dynamic Global and Local Enhancement of Theta-Band Functional Networks as Neural Markers of Spinal Cord Stimulation Efficacy in Disorders of Consciousness.CNS neuroscience & therapeutics · 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
- Alzheimer's Disease Detection Using Combined EEG Source Connectivity and Microstate Features.Brain sciences · 2026Article
- Explainable EEG-based machine learning for early diagnosis of Alzheimer's disease and frontotemporal dementia.Scientific reports · 2026Article
- A CNN-transformer fusion for EEG-based discrimination of Alzheimer's and frontotemporal dementia.Physical and engineering sciences in medicine · 2026Article
- A deep-SVM hybrid framework with enhanced EEG feature engineering and SHAP-based explainability for Alzheimer's classification.Scientific reports · 2026Article
- Integrating attractor dynamics and connectivity features for EEG-based dementia classification.Scientific reports · 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
- EEG-based classification of alzheimer's disease and frontotemporal dementia using functional connectivity.Scientific reports · 2026Article
- Integrating acoustic, prosodic, and phonological features for automatic Alzheimer's detection.Frontiers in aging neuroscience · 2026Article
- Integrating anatomical priors and clinical semantics for MRI-based diagnosis and care support in Alzheimer's disease.Frontiers in neuroscience · 2026Article
- Electroencephalography for early Alzheimer's disease diagnosis: from advanced feature engineering to interpretable ai and clinical translation.Frontiers in psychiatry · 2026Review
- CONECT: Novel Weighted Networks Framework Leveraging Angle-Relation Connection (ARC) and Metaheuristic Algorithms for EEG-Based Dementia Classification.Sensors (Basel, Switzerland) · 2025Article
- Transparent EEG Analysis: Leveraging Autoencoders, Bi-LSTMs, and SHAP for Improved Neurodegenerative Diseases Detection.Sensors (Basel, Switzerland) · 2025Article
- Detection of Alzheimer and mild cognitive impairment patients by Poincare and Entropy methods based on electroencephalography signals.Biomedical engineering online · 2025Article
- A multi-graph convolutional network method for Alzheimer's disease diagnosis based on multi-frequency EEG data with dual-mode connectivity.Frontiers in neuroscience · 2025Article
- Privacy-preserving dementia classification from EEG via hybrid-fusion EEGNetv4 and federated learning.Frontiers in computational neuroscience · 2025Article
- Classification of Alzheimer's Disease and Frontotemporal Dementia Using Electroencephalography to Quantify Communication between Electrode Pairs.Diagnostics (Basel, Switzerland) · 2024Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Early-stage Alzheimer's disease (AD) and frontotemporal dementia (FTD) share similar symptoms, complicating their diagnosis and the development of specific treatment strategies. Our study evaluated multiple feature extraction techniques for identifying AD and FTD biomarkers from electroencephalographic (EEG) signals. We developed an optimised machine learning architecture that integrates sliding windowing, feature extraction, and supervised learning to distinguish between AD and FTD patients, as well as from healthy controls (HCs). Our model, with a 90% overlap for sliding windowing, SVD entropy for feature extraction, and K-Nearest Neighbors (KNN) for supervised learning, achieved a mean F1-score and accuracy of 93% and 91%, 92.5% and 93%, and 91.5% and 91% for discriminating AD and HC, FTD and HC, and AD and FTD, respectively. The feature importance array, an explainable AI feature, highlighted the brain lobes that contributed to identifying and distinguishing AD and FTD biomarkers. This research introduces a novel framework for detecting and discriminating AD and FTD using EEG signals, addressing the need for accurate early-stage diagnostics. Furthermore, a comparative evaluation of sliding windowing, multiple feature extraction, and machine learning methods on AD/FTD detection and discrimination is documented.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.