Identifying seizure onset zone from electrocorticographic recordings: A machine learning approach based on phase locking value

•PLVs of electrodes with seizure activity are different than others prior to a seizure.•A machine learning approach can build a model for automated detection of SOZs.•Saving of time and effort to localize SOZs for clinical purposes.•Capability to detect true SOZs in cases where expert judgment faile...

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Published in:Seizure (London, England) England), 2017-10, Vol.51, p.35-42
Main Authors: Elahian, Bahareh, Yeasin, Mohammed, Mudigoudar, Basanagoud, Wheless, James W., Babajani-Feremi, Abbas
Format: Article
Language:English
Subjects:
Adolescent
Adult
Algorithms
Child, Preschool
Electrocorticographic (ECoG) recording
Electrocorticography - methods
Epilepsy surgery
Female
Humans
Intracranial EEG
Intraoperative Neurophysiological Monitoring - methods
Machine Learning
Machine learning approach
Male
Phase locking value (PLV)
Retrospective Studies
Seizure onset zone (SOZ)
Seizure outcome
Seizures - physiopathology
Seizures - surgery
Young Adult
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Summary:•PLVs of electrodes with seizure activity are different than others prior to a seizure.•A machine learning approach can build a model for automated detection of SOZs.•Saving of time and effort to localize SOZs for clinical purposes.•Capability to detect true SOZs in cases where expert judgment failed. 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). 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. 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. 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.
ISSN:1059-1311
1532-2688
DOI:10.1016/j.seizure.2017.07.010