Seizure Onset Detection in EEG Signals Based on Entropy from Generalized Gaussian PDF Modeling and Ensemble Bagging Classifier

Antonio Quintero Rincon (1) , Carlos d'Giano (2) , Hadj Batatia (3)

1 ITBA - Instituto Tecnológico de Buenos Aires
2 Centro Integral de Epilepsia (Buenos Aires, Argentine)
3 IRIT-TCI - Traitement et Compréhension d’Images

This paper proposes a new algorithm for epileptic seizure onset detection in EEG signals. The algorithm relies on the measure of the entropy of observed data sequences. Precisely, the data is decomposed into different brain rhythms using wavelet multi-scale transformation. The resulting coefficients are represented using their generalized Gaussian distribution. The proposed algorithm estimates the parameters of the distribution and the associated entropy. Next, an ensemble bagging classifier is used to performs the seizure onset detection using the entropy of each brain rhythm, by discriminating between seizure and non-seizure. Preliminary experiments with 105 epileptic events suggest that the proposed methodology is a powerful tool for detecting seizures in epileptic signals in terms of classification accuracy, sensitivity and specificity.

Mots clés: Entropy; Generalized Gaussian distribution; Ensemble bagging classifier; Wavelet filter banks; EEG; Epilepsy

Domaines: Modélisation et simulation

Antonio Quintero Rincon, Carlos d'Giano, Hadj Batatia. Seizure Onset Detection in EEG Signals Based on Entropy from Generalized Gaussian PDF Modeling and Ensemble Bagging Classifier. International Conference on Digital Health Technologies (ICDHT 2018), Oct 2018, Sfax, Tunisia. pp.1-8. ⟨hal-03621636⟩