Foundations of Artificial Learning for Epilepsology: Support Vector Machines and EEG Signal Analysis

Epilepsy, a major neurological disorder, sees 30% of patients resistant to medication, requiring surgery whose success depends on precise localization of the epileptogenic zone by EEG. However, this signal is affected by artifacts, and its modeling remains complex. This work lays the foundations for the use of artificial intelligence, in particular Support Vector Machines (SVM), for seizure detection. Structured in three chapters, it presents artificial learning, SVMs (robust, interpretable, efficient) and the specificities of the epileptic EEG signal. SVMs are preferred to deep learning methods for their transparency.This work aims to establish the conceptual and methodological foundations needed to study epileptic seizures using artificial intelligence, with a particular focus on Support Vector Machines (SVMs).