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

Abstract Objective In drug-resistant epilepsy, 40-50% of patients suffer from seizure recurrence. 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 minutes 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 = 0.952. Conclusion SVM models combining multiple iEEG features show better performance than algorithms using 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 minutes of inter-ictal iEEG recordings.