Using shallow neural networks with functional connectivity from EEG signals for early diagnosis of Alzheimer's and frontotemporal dementia
Résumé
Introduction: Dementia is a neurological disorder associated with aging that
can cause a loss of cognitive functions, impacting daily life. Alzheimer’s disease
(AD) is the most common cause of dementia, accounting for 50–70% of
cases, while frontotemporal dementia (FTD) affects social skills and personality.
Electroencephalography (EEG) provides an effective tool to study the effects of
AD on the brain.
Methods: In this study, we propose to use shallow neural networks applied to
two sets of features: spectral-temporal and functional connectivity using four
methods. We compare three supervised machine learning techniques to the CNN
models to classify EEG signals of AD / FTD and control cases. We also evaluate
different measures of functional connectivity from common EEG frequency bands
considering multiple thresholds.
Results and discussion: Results showed that the shallow CNN-based models
achieved the highest accuracy of 94.54% with AEC in test dataset when
considering all connections, outperforming conventional methods and providing
potentially an additional early dementia diagnosis tool.
Domaines
Neurosciences [q-bio.NC]Origine | Fichiers éditeurs autorisés sur une archive ouverte |
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