Bahareh Elahian1, Mohammed Yeasin2, Basanagoud Mudigoudar3, James W Wheless3, Abbas Babajani-Feremi4. 1. Department of Electrical and Computer Engineering, University of Memphis, Memphis, TN, USA. Electronic address: belahian@memphis.edu. 2. Department of Electrical and Computer Engineering, University of Memphis, Memphis, TN, USA. 3. Department of Pediatrics, Division of Pediatric Neurology, University of Tennessee Health Science Center, Le Bonheur Comprehensive Epilepsy Program, Le Bonheur Children's Hospital, Memphis, TN, USA; Neuroscience Institute, Le Bonheur Children's Hospital, Memphis, TN, USA. 4. Department of Pediatrics, Division of Pediatric Neurology, University of Tennessee Health Science Center, Le Bonheur Comprehensive Epilepsy Program, Le Bonheur Children's Hospital, Memphis, TN, USA; Neuroscience Institute, Le Bonheur Children's Hospital, Memphis, TN, USA; Department of Anatomy and Neurobiology, University of Tennessee Health Science Center, Memphis, TN, USA.
Abstract
PURPOSE: Using a novel technique based on phase locking value (PLV), we investigated the potential for features extracted from electrocorticographic (ECoG) recordings to serve as biomarkers to identify the seizure onset zone (SOZ). METHODS: We computed the PLV between the phase of the amplitude of high gamma activity (80-150Hz) and the phase of lower frequency rhythms (4-30Hz) from ECoG recordings obtained from 10 patients with epilepsy (21 seizures). We extracted five features from the PLV and used a machine learning approach based on logistic regression to build a model that classifies electrodes as SOZ or non-SOZ. RESULTS: More than 96% of electrodes identified as the SOZ by our algorithm were within the resected area in six seizure-free patients. In four non-seizure-free patients, more than 31% of the identified SOZ electrodes by our algorithm were outside the resected area. In addition, we observed that the seizure outcome in non-seizure-free patients correlated with the number of non-resected SOZ electrodes identified by our algorithm. CONCLUSION: This machine learning approach, based on features extracted from the PLV, effectively identified electrodes within the SOZ. The approach has the potential to assist clinicians in surgical decision-making when pre-surgical intracranial recordings are utilized.
PURPOSE: Using a novel technique based on phase locking value (PLV), we investigated the potential for features extracted from electrocorticographic (ECoG) recordings to serve as biomarkers to identify the seizure onset zone (SOZ). METHODS: We computed the PLV between the phase of the amplitude of high gamma activity (80-150Hz) and the phase of lower frequency rhythms (4-30Hz) from ECoG recordings obtained from 10 patients with epilepsy (21 seizures). We extracted five features from the PLV and used a machine learning approach based on logistic regression to build a model that classifies electrodes as SOZ or non-SOZ. RESULTS: More than 96% of electrodes identified as the SOZ by our algorithm were within the resected area in six seizure-free patients. In four non-seizure-free patients, more than 31% of the identified SOZ electrodes by our algorithm were outside the resected area. In addition, we observed that the seizure outcome in non-seizure-free patients correlated with the number of non-resected SOZ electrodes identified by our algorithm. CONCLUSION: This machine learning approach, based on features extracted from the PLV, effectively identified electrodes within the SOZ. The approach has the potential to assist clinicians in surgical decision-making when pre-surgical intracranial recordings are utilized.
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