Prediction of seizure onset zone in epilepsy patients via a network coupling measure

Abstract

Epilepsy, a chronic neurological disorder characterized by recurrent seizures, affects millions globally. For patients with drug-resistant epilepsy, surgical intervention becomes a viable option. However, precise localization of the seizure onset zone (SOZ) is crucial for successful surgery. This thesis investigates the potential of the L measure, a non-linear method analyzing directional couplings between brain regions, for SOZ detection in pharmacoresistant epilepsy patients using electroencephalography (EEG) data recorded in a natural environment. We analyzed seizure dynamics in 10 patients using EEG data from the Melbourne NeuroVista Seizure Prediction Trial database. Applying the L measure, we explored connectivity patterns within and across brain regions during pre-ictal, seizure onset, and ictal stages. Network analysis using graph theory metrics assessed these variations across EEG channels and patients to identify potential SOZ locations. Furthermore, we developed a novel method, to track channel connectivity dynamics during seizures, potentially detecting the SOZ with higher temporal resolution. These findings are expected to contribute to a more comprehensive understanding of seizure dynamics and the potential of the L measure for SOZ detection in pharmacoresistant epilepsy patients. This research may pave the way for improved surgical planning and treatment outcomes for this challenging patient population.