An ensemble learning approach to detect epileptic seizures from long intracranial EEG recordings

Jean-Baptiste Schiratti (1) , Jean-Eudes Le Douget (2) , Michel Le van Quyen (2) , Slim Essid (3) , Alexandre Gramfort (4, 1, 5)

1 LTCI - Laboratoire Traitement et Communication de l'Information
2 ICM - Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute
3 LTCI - Laboratoire Traitement et Communication de l'Information
4 Inria Saclay - Ile de France
5 PARIETAL - Modelling brain structure, function and variability based on high-field MRI data

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.

Mots clés: Intracranial EEG; Machine learning; Epilepsy and seizures; Intracranial EEG

Domaines: Modélisation et simulation; Sciences du Vivant [q-bio]; Applications [stat.AP]

Jean-Baptiste Schiratti, Jean-Eudes Le Douget, Michel Le van Quyen, Slim Essid, Alexandre Gramfort. An ensemble learning approach to detect epileptic seizures from long intracranial EEG recordings. ICASSP 2018 - 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP, Apr 2018, Calgary, Canada. ⟨hal-01724272⟩