An Ensemble Learning Approach to Detect Epileptic Seizures from Long Intracranial EEG Recordings

J. B. Schiratti; Jean Eudes Le Douget; Michel Le Van Quyen; Slim Essid; Alexandre Gramfort

doi:10.1109/ICASSP.2018.8461489

An Ensemble Learning Approach to Detect Epileptic Seizures from Long Intracranial EEG Recordings

J. B. Schiratti
, Jean Eudes Le Douget
, Michel Le Van Quyen
, Slim Essid
, Alexandre Gramfort

Université Paris-Saclay
Institut du Cerveau et de la Moelle épinière (ICM)

Résultats de recherche: Le chapitre dans un livre, un rapport, une anthologie ou une collection › Contribution à une conférence › Revue par des pairs

Résumé

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.

langue originale	Anglais
titre	2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings
Editeur	Institute of Electrical and Electronics Engineers Inc.
Pages	856-860
Nombre de pages	5
ISBN (imprimé)	9781538646588
Les DOIs	https://doi.org/10.1109/ICASSP.2018.8461489
état	Publié - 10 sept. 2018
Modification externe	Oui
Evénement	2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Calgary, Canada Durée: 15 avr. 2018 → 20 avr. 2018

Série de publications

Nom	ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume	2018-April
ISSN (imprimé)	1520-6149

Une conférence

Une conférence	2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018
Pays/Territoire	Canada
La ville	Calgary
période	15/04/18 → 20/04/18

Accès au document

10.1109/ICASSP.2018.8461489

Contient cette citation

Schiratti, J. B., Le Douget, J. E., Le Van Quyen, M., Essid, S., & Gramfort, A. (2018). An Ensemble Learning Approach to Detect Epileptic Seizures from Long Intracranial EEG Recordings. Dans 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings (p. 856-860). Article 8461489 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol 2018-April). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICASSP.2018.8461489

Schiratti, J. B. ; Le Douget, Jean Eudes ; Le Van Quyen, Michel et al. / An Ensemble Learning Approach to Detect Epileptic Seizures from Long Intracranial EEG Recordings. 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2018. p. 856-860 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings).

@inproceedings{f93defc461a941a3ad7a2ee22ac39fe1,

title = "An Ensemble Learning Approach to Detect Epileptic Seizures from Long Intracranial EEG Recordings",

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.",

keywords = "Intracranial EEG, Seizure detection, Supervised learning",

author = "Schiratti, \{J. B.\} and \{Le Douget\}, \{Jean Eudes\} and \{Le Van Quyen\}, Michel and Slim Essid and Alexandre Gramfort",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 ; Conference date: 15-04-2018 Through 20-04-2018",

year = "2018",

month = sep,

day = "10",

doi = "10.1109/ICASSP.2018.8461489",

language = "English",

isbn = "9781538646588",

series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "856--860",

booktitle = "2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings",

}

Schiratti, JB, Le Douget, JE, Le Van Quyen, M, Essid, S & Gramfort, A 2018, An Ensemble Learning Approach to Detect Epileptic Seizures from Long Intracranial EEG Recordings. Dans 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings., 8461489, ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, VOL. 2018-April, Institute of Electrical and Electronics Engineers Inc., p. 856-860, 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018, Calgary, Canada, 15/04/18. https://doi.org/10.1109/ICASSP.2018.8461489

An Ensemble Learning Approach to Detect Epileptic Seizures from Long Intracranial EEG Recordings. / Schiratti, J. B.; Le Douget, Jean Eudes; Le Van Quyen, Michel et al.
2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2018. p. 856-860 8461489 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings; Vol 2018-April).

Résultats de recherche: Le chapitre dans un livre, un rapport, une anthologie ou une collection › Contribution à une conférence › Revue par des pairs

TY - GEN

T1 - An Ensemble Learning Approach to Detect Epileptic Seizures from Long Intracranial EEG Recordings

AU - Schiratti, J. B.

AU - Le Douget, Jean Eudes

AU - Le Van Quyen, Michel

AU - Essid, Slim

AU - Gramfort, Alexandre

PY - 2018/9/10

Y1 - 2018/9/10

N2 - 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.

AB - 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.

KW - Intracranial EEG

KW - Seizure detection

KW - Supervised learning

U2 - 10.1109/ICASSP.2018.8461489

DO - 10.1109/ICASSP.2018.8461489

M3 - Conference contribution

AN - SCOPUS:85054221384

SN - 9781538646588

T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

SP - 856

EP - 860

BT - 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018

Y2 - 15 April 2018 through 20 April 2018

ER -

Schiratti JB, Le Douget JE, Le Van Quyen M, Essid S, Gramfort A. An Ensemble Learning Approach to Detect Epileptic Seizures from Long Intracranial EEG Recordings. Dans 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2018. p. 856-860. 8461489. (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings). doi: 10.1109/ICASSP.2018.8461489

An Ensemble Learning Approach to Detect Epileptic Seizures from Long Intracranial EEG Recordings

Résumé

Série de publications

Une conférence

Accès au document

Empreinte digitale

Contient cette citation