Multi-feature localization of epileptic foci from interictal,
intracranial EEG

J 2019

Multi-feature localization of epileptic foci from interictal, intracranial EEG

CIMBÁLNÍK, Jan, Petr KLIMES, Vladimir SLADKY, Petr NEJEDLY, Pavel JURAK et. al.

Základní údaje

Originální název

Multi-feature localization of epileptic foci from interictal, intracranial EEG

Autoři

CIMBÁLNÍK, Jan (203 Česká republika, garant), Petr KLIMES (203 Česká republika), Vladimir SLADKY (203 Česká republika), Petr NEJEDLY (203 Česká republika), Pavel JURAK (203 Česká republika), Martin PAIL (203 Česká republika, domácí), Robert ROMAN (203 Česká republika, domácí), Pavel DANIEL (203 Česká republika, domácí), Hari GURAGAIN (840 Spojené státy), Benjamin BRINKMANN (840 Spojené státy), Milan BRÁZDIL (203 Česká republika, domácí) a Greg WORRELL (840 Spojené státy)

Vydání

Clinical Neurophysiology, Clare, Elsevier Ireland, 2019, 1388-2457

Další údaje

Jazyk

angličtina

Typ výsledku

Článek v odborném periodiku

Obor

30103 Neurosciences

Stát vydavatele

Irsko

Utajení

není předmětem státního či obchodního tajemství

Odkazy

URL

Impakt faktor

Impact factor: 3.214

Kód RIV

RIV/00216224:14110/19:00110969

Organizační jednotka

Lékařská fakulta

DOI

http://dx.doi.org/10.1016/j.clinph.2019.07.024

UT WoS

000485832400022

Klíčová slova anglicky

Drug resistant epilepsy; Epileptogenic zone localization; Multi-feature approach; High frequency oscillations; Connectivity; Machine learning

Štítky

14110127, CF MAFIL, podil, rivok

Příznaky

Mezinárodní význam, Recenzováno

Změněno: 7. 10. 2024 10:41, Ing. Jana Kuchtová

Anotace

V originále

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.

Návaznosti

LM2015062, projekt VaV

Název: Národní infrastruktura pro biologické a medicínské zobrazování

Investor: Ministerstvo školství, mládeže a tělovýchovy ČR, National research infrastructure for biological and medical imaging

90062, velká výzkumná infrastruktura

Název: Czech-BioImaging

Citovat

CIMBÁLNÍK, Jan, Petr KLIMES, Vladimir SLADKY, Petr NEJEDLY, Pavel JURAK, Martin PAIL, Robert ROMAN, Pavel DANIEL, Hari GURAGAIN, Benjamin BRINKMANN, Milan BRÁZDIL a Greg WORRELL. Multi-feature localization of epileptic foci from interictal, intracranial EEG. Clinical Neurophysiology. Clare: Elsevier Ireland, 2019, roč. 130, č. 10, s. 1945-1953. ISSN 1388-2457. Dostupné z: https://dx.doi.org/10.1016/j.clinph.2019.07.024.

@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 a Greg WORRELL. Multi-feature localization of epileptic foci from interictal, intracranial EEG. \textit{Clinical Neurophysiology}. Clare: Elsevier Ireland, 2019, roč.~130, č.~10, s.~1945-1953. ISSN~1388-2457. Dostupné z: https://dx.doi.org/10.1016/j.clinph.2019.07.024.

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