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

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.