  An ability to map seizure-generating brain tissue, i.e., the seizure onset
zone (SOZ), without recording actual seizures could reduce the duration of
invasive EEG monitoring for patients with drug-resistant epilepsy. A
widely-adopted practice in the literature is to compare the incidence
(events/time) of putative pathological electrophysiological biomarkers
associated with epileptic brain tissue with the SOZ determined from spontaneous
seizures recorded with intracranial EEG, primarily using a single biomarker.
Clinical translation of the previous efforts suffers from their inability to
generalize across multiple patients because of (a) the inter-patient
variability and (b) the temporal variability in the epileptogenic activity.
Here, we report an artificial intelligence-based approach for combining
multiple interictal electrophysiological biomarkers and their temporal
characteristics as a way of accounting for the above barriers and show that it
can reliably identify seizure onset zones in a study cohort of 82 patients who
underwent evaluation for drug-resistant epilepsy. Our investigation provides
evidence that utilizing the complementary information provided by multiple
electrophysiological biomarkers and their temporal characteristics can
significantly improve the localization potential compared to previously
published single-biomarker incidence-based approaches, resulting in an average
area under ROC curve (AUC) value of 0.73 in a cohort of 82 patients. Our
results also suggest that recording durations between ninety minutes and two
hours are sufficient to localize SOZs with accuracies that may prove clinically
relevant. The successful validation of our approach on a large cohort of 82
patients warrants future investigation on the feasibility of utilizing
intra-operative EEG monitoring and artificial intelligence to localize
epileptogenic brain tissue.
