Automated Detection of Postictal Generalized EEG Suppression

Détails

ID Serval
serval:BIB_138F8F2DDAD9
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Institution
Titre
Automated Detection of Postictal Generalized EEG Suppression
Périodique
IEEE Trans Biomed Eng
Auteur⸱e⸱s
Theeranaew W., McDonald J., Zonjy B., Kaffashi F., Moseley B. D., Friedman D., So E., Tao J., Nei M., Ryvlin P., Surges R., Thijs R., Schuele S., Lhatoo S., Loparo K. A.
ISSN
1558-2531 (Electronic)
ISSN-L
0018-9294
Statut éditorial
Publié
Date de publication
02/2018
Volume
65
Numéro
2
Pages
371-377
Langue
anglais
Notes
Theeranaew, Wanchat
McDonald, James
Zonjy, Bilal
Kaffashi, Farhad
Moseley, Brian D
Friedman, Daniel
So, Elson
Tao, James
Nei, Maromi
Ryvlin, Philippe
Surges, Rainer
Thijs, Roland
Schuele, Stephan
Lhatoo, Samden
Loparo, Kenneth A
eng
U01 NS090405/NS/NINDS NIH HHS/
U01 NS090407/NS/NINDS NIH HHS/
U01 NS090408/NS/NINDS NIH HHS/
Research Support, N.I.H., Extramural
IEEE Trans Biomed Eng. 2018 Feb;65(2):371-377. doi: 10.1109/TBME.2017.2771468.
Résumé
Although there is no strict consensus, some studies have reported that Postictal generalized EEG suppression (PGES) is a potential electroencephalographic (EEG) biomarker for risk of sudden unexpected death in epilepsy (SUDEP). PGES is an epoch of EEG inactivity after a seizure, and the detection of PGES in clinical data is extremely difficult due to artifacts from breathing, movement and muscle activity that can adversely affect the quality of the recorded EEG data. Even clinical experts visually interpreting the EEG will have diverse opinions on the start and end of PGES for a given patient. The development of an automated EEG suppression detection tool can assist clinical personnel in the review and annotation of seizure files, and can also provide a standard for quantifying PGES in large patient cohorts, possibly leading to further clarification of the role of PGES as a biomarker of SUDEP risk. In this paper, we develop an automated system that can detect the start and end of PGES using frequency domain features in combination with boosting classification algorithms. The average power for different frequency ranges of EEG signals are extracted from the prefiltered recorded signal using the fast fourier transform and are used as the feature set for the classification algorithm. The underlying classifiers for the boosting algorithm are linear classifiers using a logistic regression model. The tool is developed using 12 seizures annotated by an expert then tested and evaluated on another 20 seizures that were annotated by 11 experts.
Pubmed
Création de la notice
29/11/2018 13:37
Dernière modification de la notice
20/08/2019 13:42
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