Using high-frequency oscillations from brief intraoperative neural recordings to predict the seizure onset zone

Abstract

BackgroundWhile high-frequency oscillations (HFOs) and their stereotyped clusters (sHFOs) have emerged as potential neuro-biomarkers for the rapid localization of the seizure onset zone (SOZ) in epilepsy, their clinical application is hindered by the challenge of automated elimination of pseudo-HFOs originating from artifacts in heavily corrupted intraoperative neural recordings. This limitation has led to a reliance on semi-automated detectors, coupled with manual visual artifact rejection, impeding the translation of findings into clinical practice.MethodsIn response, we have developed a computational framework that integrates sparse signal processing and ensemble learning to automatically detect genuine HFOs of intracranial EEG data. This framework is utilized during intraoperative monitoring (IOM) while implanting electrodes and postoperatively in the epilepsy monitoring unit (EMU) before the respective surgery.ResultsOur framework demonstrates a remarkable ability to eliminate pseudo-HFOs in heavily corrupted neural data, achieving accuracy levels comparable to those obtained through expert visual inspection. It not only enhances SOZ localization accuracy of IOM to a level comparable to EMU but also successfully captures sHFO clusters within IOM recordings, exhibiting high specificity to the primary SOZ.ConclusionsThese findings suggest that intraoperative HFOs, when processed with computational intelligence, can be used as early feedback for SOZ tailoring surgery to guide electrode repositioning, enhancing the efficacy of the overall invasive therapy. Medication-resistant epilepsy is a form of epilepsy that cannot be controlled with drugs. In such cases, surgery is often required to remove the brain regions where seizures start. To identify these areas, electrodes are typically implanted in the brain, and the patient's brain activity is monitored for several days or weeks in the hospital, a process that can be lengthy and risky. We investigated whether seizure-causing brain regions could be identified earlier by applying a computational intelligence method to brain signals recorded during electrode implantation surgery. Our algorithm automatically detected abnormal high-frequency oscillations (HFOs) associated with epileptic brain tissue, improving the accuracy of identifying the areas that need to be removed. This approach could help clinicians make quicker, more precise decisions, reducing the need for prolonged monitoring and minimizing risks. Fazli Besheli et al. identify intraoperative high frequency oscillations (HFOs) using noise-resilient computational intelligence to effectively localize seizure-generating brain regions. Epileptogenic zones are identified from brief intraoperative neural recordings.