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

Jan Cimbalnik, Petr Klimes, Vladimir Sladky, Petr Nejedly, Pavel Jurak, Martin Pail, Robert Roman, Pavel Daniel, Hari Guragain, Benjamin Brinkmann, Milan Brazdil, Greg Worrell

Research output: Contribution to journalArticle

2 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)1945-1953
Number of pages9
JournalClinical Neurophysiology
Volume130
Issue number10
DOIs
StatePublished - Oct 2019

Keywords

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

ASJC Scopus subject areas

  • Sensory Systems
  • Neurology
  • Clinical Neurology
  • Physiology (medical)

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  • Cite this

    Cimbalnik, J., Klimes, P., Sladky, V., Nejedly, P., Jurak, P., Pail, M., Roman, R., Daniel, P., Guragain, H., Brinkmann, B., Brazdil, M., & Worrell, G. (2019). Multi-feature localization of epileptic foci from interictal, intracranial EEG. Clinical Neurophysiology, 130(10), 1945-1953. https://doi.org/10.1016/j.clinph.2019.07.024