Multi-feature localization of epileptic foci from interictal, intracranial EEG. Issue 10 (October 2019)

• Record Type: Journal Article
• Authors: Cimbalnik, Jan; Klimes, Petr; Sladky, Vladimir; Nejedly, Petr; Jurak, Pavel; Pail, Martin; Roman, Robert; Daniel, Pavel; Guragain, Hari; Brinkmann, Benjamin; Brazdil, Milan; Worrell, Greg
• Is Part Of: Clinical neurophysiology. Volume 130:Issue 10(2019:Oct.)
• Page Start: 1945
• Page End: 1953
• Publication Date: 2019-10
• Subjects: Drug resistant epilepsy -- Epileptogenic zone localization -- Multi-feature approach -- High frequency oscillations -- Connectivity -- Machine learning; Neurophysiology -- Periodicals; Electroencephalography -- Periodicals; Electromyography -- Periodicals; Neurology -- Periodicals; 612.8
• DOI: 10.1016/j.clinph.2019.07.024
• Languages: English
• ISSNs: 1388-2457

Abstract

Highlights: Multi-feature approach in localization of epileptogenic tissue is superior to using single feature. Multi-feature approach can improve epileptogenic brain localization. The presented algorithm performed well on datasets from different institutions. Abstract: 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.