A support vector machine framework for alzheimers diasease diagnosis using EEG data

Alzheimer’s disease is a progressive neurodegenerative disorder that significantly affects memory, cognition, and daily functioning. Early and accurate diagnosis is essential for effective treatment planning and disease management. Electroencephalography (EEG) has emerged as a non-invasive and cost-effective technique for detecting abnormal brain activity associated with Alzheimer’s disease. This study proposes a Support Vector Machine (SVM)-based framework for the automated diagnosis of Alzheimer’s disease using EEG signals.

The proposed framework involves EEG signal acquisition, preprocessing, feature extraction, and classification. Noise and artifacts are removed from raw EEG recordings using filtering techniques to improve signal quality. Statistical, spectral, and temporal features are then extracted from the processed EEG signals to capture distinctive neurological patterns. These features are subsequently fed into a Support Vector Machine classifier to differentiate between healthy individuals and patients affected by Alzheimer’s disease.