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@article{1569797,
author = {Cimbálník, Jan and Klimes, Petr and Sladky, Vladimir and Nejedly, Petr and Jurak, Pavel and Pail, Martin and Roman, Robert and Daniel, Pavel and Guragain, Hari and Brinkmann, Benjamin and Brázdil, Milan and Worrell, Greg},
article_location = {Clare},
article_number = {10},
doi = {http://dx.doi.org/10.1016/j.clinph.2019.07.024},
keywords = {Drug resistant epilepsy; Epileptogenic zone localization; Multi-feature approach; High frequency oscillations; Connectivity; Machine learning},
language = {eng},
issn = {1388-2457},
journal = {Clinical Neurophysiology},
title = {Multi-feature localization of epileptic foci from interictal, intracranial EEG},
url = {http://dx.doi.org/10.1016/j.clinph.2019.07.024},
volume = {130},
year = {2019}
}
TY - JOUR
ID - 1569797
AU - Cimbálník, Jan - Klimes, Petr - Sladky, Vladimir - Nejedly, Petr - Jurak, Pavel - Pail, Martin - Roman, Robert - Daniel, Pavel - Guragain, Hari - Brinkmann, Benjamin - Brázdil, Milan - Worrell, Greg
PY - 2019
TI - Multi-feature localization of epileptic foci from interictal, intracranial EEG
JF - Clinical Neurophysiology
VL - 130
IS - 10
SP - 1945-1953
EP - 1945-1953
PB - Elsevier Ireland
SN - 13882457
KW - Drug resistant epilepsy
KW - Epileptogenic zone localization
KW - Multi-feature approach
KW - High frequency oscillations
KW - Connectivity
KW - Machine learning
UR - http://dx.doi.org/10.1016/j.clinph.2019.07.024
L2 - http://dx.doi.org/10.1016/j.clinph.2019.07.024
N2 - Objective: When considering all patients with focal drug-resistant epilepsy, as high as 40-50% of patients suffer seizure recurrence after surgery. To achieve seizure freedom without side effects, accurate localization of the epileptogenic tissue is crucial before its resection. We investigate an automated, fast, objective mapping process that uses only interictal data. Methods: We propose a novel approach based on multiple iEEG features, which are used to train a support vector machine (SVM) model for classification of iEEG electrodes as normal or pathologic using 30 min of inter-ictal recording. Results: The tissue under the iEEG electrodes, classified as epileptogenic, was removed in 17/18 excellent outcome patients and was not entirely resected in 8/10 poor outcome patients. The overall best result was achieved in a subset of 9 excellent outcome patients with the area under the receiver operating curve = 0.95. Conclusion: SVM models combining multiple iEEG features show better performance than algorithms using a single iEEG marker. Multiple iEEG and connectivity features in presurgical evaluation could improve epileptogenic tissue localization, which may improve surgical outcome and minimize risk of side effects. Significance: In this study, promising results were achieved in localization of epileptogenic regions by SVM models that combine multiple features from 30 min of inter-ictal iEEG recordings. (C) 2019 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.
ER -
CIMBÁLNÍK, Jan, Petr KLIMES, Vladimir SLADKY, Petr NEJEDLY, Pavel JURAK, Martin PAIL, Robert ROMAN, Pavel DANIEL, Hari GURAGAIN, Benjamin BRINKMANN, Milan BRÁZDIL and Greg WORRELL. Multi-feature localization of epileptic foci from interictal, intracranial EEG. \textit{Clinical Neurophysiology}. Clare: Elsevier Ireland, 2019, vol.~130, No~10, p.~1945-1953. ISSN~1388-2457. Available from: https://dx.doi.org/10.1016/j.clinph.2019.07.024.
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