TY - JOUR
T1 - Multi-feature localization of epileptic foci from interictal, intracranial EEG
AU - Cimbalnik, Jan
AU - Klimes, Petr
AU - Sladky, Vladimir
AU - Nejedly, Petr
AU - Jurak, Pavel
AU - Pail, Martin
AU - Roman, Robert
AU - Daniel, Pavel
AU - Guragain, Hari
AU - Brinkmann, Benjamin
AU - Brazdil, Milan
AU - Worrell, Greg
N1 - Publisher Copyright:
© 2019 International Federation of Clinical Neurophysiology
PY - 2019/10
Y1 - 2019/10
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.
AB - 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.
KW - Connectivity
KW - Drug resistant epilepsy
KW - Epileptogenic zone localization
KW - High frequency oscillations
KW - Machine learning
KW - Multi-feature approach
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U2 - 10.1016/j.clinph.2019.07.024
DO - 10.1016/j.clinph.2019.07.024
M3 - Article
C2 - 31465970
AN - SCOPUS:85071109284
SN - 1388-2457
VL - 130
SP - 1945
EP - 1953
JO - Clinical Neurophysiology
JF - Clinical Neurophysiology
IS - 10
ER -