An Ensemble Learning Approach to Detect Epileptic Seizures from Long Intracranial EEG Recordings
Jean-Baptiste Schiratti, Jean-Eudes Le Douget, Michel Le Van Quyen, Slim Essid, Alexandre Gramfort
Abstract
This paper proposes a patient-specific supervised classification algorithm to detect seizures in long offline intracranial electroencephalographic (iEEG) recordings. The main idea of the proposed algorithm is to combine a set of probabilistic classifiers, trained on a dataset of 1 s epochs, into a weighted ensemble classifier which can be used to analyze longer 5 s data segments. The method is trained and evaluated on 24 patients, all suffering from focal medically intractable epilepsy, from the Epilepsiae database. The evaluation of the method, conducted using an average of 113 hours (min: 32 h, max: 229 h) of iEEG data per patient, shows that the proposed algorithm improves upon existing methods for seizure detection with iEEG.
BibTeX
@inproceedings{icassp2018_anensemblelearni,
title = {An Ensemble Learning Approach to Detect Epileptic Seizures from Long Intracranial EEG Recordings},
author = {Jean-Baptiste Schiratti and Jean-Eudes Le Douget and Michel Le Van Quyen and Slim Essid and Alexandre Gramfort},
booktitle = {ICASSP 2018},
year = {2018}
}