Foundations of Artificial Learning for Epilepsology:

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).

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Nour El Houda FETHELLAH is a lecturer at the Institut de Formation et de Recherche en Hydrométéorologie (IHFR), with a degree in computer science (bachelor's, master's and doctorate) from the Université des Sciences et de la Technologie d'Oran (Algeria). Her research focuses on networks, information systems and AI.