Jan Cimbalnik1, Petr Klimes2, Vladimir Sladky3, Petr Nejedly2, Pavel Jurak4, Martin Pail5, Robert Roman6, Pavel Daniel5, Hari Guragain7, Benjamin Brinkmann8, Milan Brazdil9, Greg Worrell8. 1. 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. 2. 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. 3. 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. 4. Institute of Scientific Instruments, The Czech Academy of Sciences, Brno, Czech Republic. 5. Brno Epilepsy Center, Department of Neurology, St. Anne's University Hospital and Faculty of Medicine, Masaryk University, Brno, Czech Republic. 6. Behavioral and Social Neuroscience Research Group, CEITEC - Central European Institute of Technology, Masaryk University, Brno, Czech Republic. 7. Mayo Systems Electrophysiology Laboratory, Department of Neurology, Mayo Clinic, 200 First St SW, Rochester, MN 55905, USA. 8. 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. 9. 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.
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.
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.
Keywords:
Connectivity; Drug resistant epilepsy; Epileptogenic zone localization; High frequency oscillations; Machine learning; Multi-feature approach
Authors: N Tzourio-Mazoyer; B Landeau; D Papathanassiou; F Crivello; O Etard; N Delcroix; B Mazoyer; M Joliot Journal: Neuroimage Date: 2002-01 Impact factor: 6.556
Authors: Richard J Staba; Charles L Wilson; Anatol Bragin; Donald Jhung; Itzhak Fried; Jerome Engel Journal: Ann Neurol Date: 2004-07 Impact factor: 10.422
Authors: Krishnakant V Saboo; Irena Balzekas; Vaclav Kremen; Yogatheesan Varatharajah; Michal Kucewicz; Ravishankar K Iyer; Gregory A Worrell Journal: Epilepsia Date: 2021-09-18 Impact factor: 5.864
Authors: Joshua J Bear; Jenifer L Sargent; Brent R O'Neill; Kevin E Chapman; Debashis Ghosh; Heidi E Kirsch; Jason R Tregellas Journal: J Clin Neurophysiol Date: 2021-11-23 Impact factor: 2.590
Authors: Casey Paquola; Jakob Seidlitz; Oualid Benkarim; Jessica Royer; Petr Klimes; Richard A I Bethlehem; Sara Larivière; Reinder Vos de Wael; Raul Rodríguez-Cruces; Jeffery A Hall; Birgit Frauscher; Jonathan Smallwood; Boris C Bernhardt Journal: PLoS Biol Date: 2020-11-30 Impact factor: 8.029
Authors: Jan Cimbalnik; Jaromir Dolezal; Çağdaş Topçu; Michal Lech; Victoria S Marks; Boney Joseph; Martin Dobias; Jamie Van Gompel; Gregory Worrell; Michal Kucewicz Journal: Sci Data Date: 2022-01-13 Impact factor: 6.444
Authors: Nykan Mirchi; Nebras M Warsi; Frederick Zhang; Simeon M Wong; Hrishikesh Suresh; Karim Mithani; Lauren Erdman; George M Ibrahim Journal: Front Hum Neurosci Date: 2022-06-27 Impact factor: 3.473
Authors: Jan Cimbalnik; Martin Pail; Petr Klimes; Vojtech Travnicek; Robert Roman; Adam Vajcner; Milan Brazdil Journal: Front Neurol Date: 2020-10-27 Impact factor: 4.003