Classification des Signaux EEG basée sur l’Apprentissage automatique et l'apprentissage profond

Abstract

Approximately 50 million people worldwide suffer from epilepsy, a chronic neurological disorder. The automatic detection of epileptic seizures from EEG (electroencephalogram) signals remains a major challenge for researchers. This study proposes a methodology for classifying EEG signals using machine learning techniques, including Support Vector Machines (SVM), k-Nearest Neighbors (KNN), the Naive Bayes (NB) algorithm, and a Convolutional Neural Network (CNN). To evaluate model performance, metrics such as Accuracy (AC), Sensitivity (SE), Specificity (SP), and Receiver Operating Characteristic (ROC) curves were used. The results demonstrate the effectiveness of the applied models, particularly the CNN, in the automated detection of epilepsy from EEG signals.