Towards Automated SOZ Localisation
Motivation
Surgical resection represents the most effective treatment for focal drug-resistant epilepsy (DRE) [1].
Favourable outcomes depend critically on accurate localisation of the seizure onset zone (SOZ) during intracranial EEG (iEEG) monitoring.
Current clinical localisation methods are time-intensive and highly subjective, necessitating an automated process to reduce clinical burden [1].
Objectives
Address the clinical and technical challenge of localising the SOZ using an objective algorithmic approach that will be implemented into a multimodal, pre-surgical planning pipeline.
Extract candidate electrophysiological biomarkers from iEEG signals, evaluating their utility at determining seizure onset against clinician-annotated events.
Use supervised classification to predict seizure-related channels, delineating the contacts at which onset is likely occurring.
Research Questions
Can a combination of stereo-EEG (sEEG) and electrocorticography (ECoG) data yield stronger predictions of seizure onset?
Which features are most discriminative at depicting onset times compared to expert clinical annotations?
Can we use supervised classification to determine the electrode contacts at which seizure activity is occurring?