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

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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.

Original language	English
Title of host publication	2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	856-860
Number of pages	5
ISBN (Print)	9781538646588
DOIs	https://doi.org/10.1109/ICASSP.2018.8461489
Publication status	Published - 10 Sept 2018
Externally published	Yes
Event	2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Calgary, Canada Duration: 15 Apr 2018 → 20 Apr 2018

Publication series

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

Conference

Conference	2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018
Country/Territory	Canada
City	Calgary
Period	15/04/18 → 20/04/18

Keywords

Intracranial EEG
Seizure detection
Supervised learning

Access to Document

10.1109/ICASSP.2018.8461489

Cite this

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. In 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings (pp. 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. pp. 856-860 (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings).

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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,

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doi = "10.1109/ICASSP.2018.8461489",

language = "English",

isbn = "9781538646588",

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

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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. in 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., pp. 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).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Gramfort, Alexandre

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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.

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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. In 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

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Keywords

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