Soutenance de thèse de Monsieur Mohamad EL SAYED HUSSEIN JOMAA

14h00 | POLYTECH ANGERS | AMPHI E | 62, avenue Notre-Dame du Lac | ANGERS

Le 3 décembre 2019

Sujet : Signal processing of electroencephalograms with 256 sensors in epileptic children

Directeur de thèse : Madame Anne HUMEAU-HEURTIER

Abstract

In this thesis, our focus is to develop signal processing methods to be used on electroencephalography (EEG) signals recorded from epileptic patients. The aim of these methods is to be able to quantify the state of the patient with epilepsy and to study the progress of the neurological disorder over time. The methods we developed are based on entropy. From previous permutation entropy methods we introduce the multivariate Improved Weighted Multi-scale Permutation Entropy (mvIWMPE). This method is applied on EEG signals of both healthy and epileptic children and gives promising results. We also introduce a new multivariate approach for sample entropy and, when tested and compared with the existing multivariate approach, we find that the introduced approach is much better in handling a larger numbers of channels. We also introduce a time-varying time-frequency complexity measure based on Singular Value Decomposition and Rényi Entropy. These measures are applied on EEG of epileptic children before and after 4-6 weeks of treatment. The results come in correspondence with the clinical diagnosis from the hospital on whether the patients improve or not. The final part of the thesis focuses on functional connectivity measures. We introduce a new functional connectivity method based on mvIWMPE and Mutual Information. The method is applied on EEG signals of healthy children at rest. Using network measures, we are able to identify regions in the brain that are active in networks previously found using functional magnetic resonance imaging. The method is also used to study the networks of epileptic children at several points throughout the treatment.