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

J. Cimbalnik, P. Klimes, V. Sladky, P. Nejedly, P. Jurak, M. Pail, R. Roman, P. Daniel, H. Guragain, B. Brinkmann, M. Brazdil, G. Worrell,

. 2019 ; 130 (10) : 1945-1953. [pub] 20190805

Language English Country Netherlands

Document type Journal Article, Research Support, N.I.H., Extramural, Research Support, Non-U.S. Gov't

Persistent link   https://www.medvik.cz/link/bmc20023611

Grant support
R01 NS063039 NINDS NIH HHS - United States
R01 NS078136 NINDS NIH HHS - United States

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.

References provided by Crossref.org

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$a Cimbalnik, Jan $u International Clinical Research Center, St. Anne's University Hospital, Brno, Czech Republic; Mayo Systems Electrophysiology Laboratory, Department of Neurology, Mayo Clinic, 200 First St SW, Rochester, MN 55905, USA. Electronic address: jan.cimbalnik@fnusa.cz.
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$a Multi-feature localization of epileptic foci from interictal, intracranial EEG / $c J. Cimbalnik, P. Klimes, V. Sladky, P. Nejedly, P. Jurak, M. Pail, R. Roman, P. Daniel, H. Guragain, B. Brinkmann, M. Brazdil, G. Worrell,
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$a 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.
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$a Klimes, Petr $u International Clinical Research Center, St. Anne's University Hospital, Brno, Czech Republic; Mayo Systems Electrophysiology Laboratory, Department of Neurology, Mayo Clinic, 200 First St SW, Rochester, MN 55905, USA; Institute of Scientific Instruments, The Czech Academy of Sciences, Brno, Czech Republic.
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$a Sladky, Vladimir $u International Clinical Research Center, St. Anne's University Hospital, Brno, Czech Republic; Mayo Systems Electrophysiology Laboratory, Department of Neurology, Mayo Clinic, 200 First St SW, Rochester, MN 55905, USA.
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$a Nejedly, Petr $u International Clinical Research Center, St. Anne's University Hospital, Brno, Czech Republic; Mayo Systems Electrophysiology Laboratory, Department of Neurology, Mayo Clinic, 200 First St SW, Rochester, MN 55905, USA; Institute of Scientific Instruments, The Czech Academy of Sciences, Brno, Czech Republic.
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$a Jurak, Pavel $u Institute of Scientific Instruments, The Czech Academy of Sciences, Brno, Czech Republic.
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$a Roman, Robert $u Behavioral and Social Neuroscience Research Group, CEITEC - Central European Institute of Technology, Masaryk University, Brno, Czech Republic.
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$a Guragain, Hari $u Mayo Systems Electrophysiology Laboratory, Department of Neurology, Mayo Clinic, 200 First St SW, Rochester, MN 55905, USA.
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$a Brazdil, Milan $u Brno Epilepsy Center, Department of Neurology, St. Anne's University Hospital and Faculty of Medicine, Masaryk University, Brno, Czech Republic; Behavioral and Social Neuroscience Research Group, CEITEC - Central European Institute of Technology, Masaryk University, Brno, Czech Republic.
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$a Worrell, Greg $u Mayo Systems Electrophysiology Laboratory, Department of Neurology, Mayo Clinic, 200 First St SW, Rochester, MN 55905, USA; Department of Physiology and Biomedical Engineering, Mayo Clinic, 200 First St SW, Rochester, MN 55905, USA.
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