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.

Behavioral and Social Neuroscience Research Group CEITEC Central European Institute of Technology Masaryk University Brno Czech Republic

Brno Epilepsy Center Department of Neurology St Anne's University Hospital and Faculty of Medicine Masaryk University Brno Czech Republic

Brno Epilepsy Center Department of Neurology St Anne's University Hospital and Faculty of Medicine Masaryk University Brno Czech Republic; Behavioral and Social Neuroscience Research Group CEITEC Central European Institute of Technology Masaryk University Brno Czech Republic

Institute of Scientific Instruments The Czech Academy of Sciences Brno Czech Republic

International Clinical Research Center St Anne's University Hospital Brno Czech Republic; Mayo Systems Electrophysiology Laboratory Department of Neurology Mayo Clinic 200 1st St SW Rochester MN 55905 USA

International Clinical Research Center St Anne's University Hospital Brno Czech Republic; Mayo Systems Electrophysiology Laboratory Department of Neurology Mayo Clinic 200 1st St SW Rochester MN 55905 USA; Institute of Scientific Instruments The Czech Academy of Sciences Brno Czech Republic

Mayo Systems Electrophysiology Laboratory Department of Neurology Mayo Clinic 200 1st St SW Rochester MN 55905 USA

Mayo Systems Electrophysiology Laboratory Department of Neurology Mayo Clinic 200 1st St SW Rochester MN 55905 USA; Department of Physiology and Biomedical Engineering Mayo Clinic 200 1st St SW Rochester MN 55905 USA

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