Identifying seizure onset zone from electrocorticographic recordings: A machine learning approach based on phase locking value
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 (80–150 Hz) and the phase of lower frequency rhythms (4–30 Hz) 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.
Publication Title
Seizure
Recommended Citation
Elahian, B., Yeasin, M., Mudigoudar, B., Wheless, J., & Babajani-Feremi, A. (2017). Identifying seizure onset zone from electrocorticographic recordings: A machine learning approach based on phase locking value. Seizure, 35-42. https://doi.org/10.1016/j.seizure.2017.07.010
