Integrating artificial intelligence with real-time intracranial EEG monitoring to automate interictal identification of seizure onset zones in focal epilepsy.

Journal: Journal of neural engineering
Published Date:

Abstract

OBJECTIVE: 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.

Authors

  • Yogatheesan Varatharajah
    * Electrical and Computer Engineering, University of Illinois at Urbana-Champaign Urbana, IL 61801, USA.
  • Brent Berry
  • Jan Cimbalnik
  • Vaclav Kremen
    Mayo Systems Electrophysiology Laboratory, Department of Neurology, Mayo Clinic, Rochester, MN, USA.
  • Jamie Van Gompel
  • Matt Stead
    Mayo Systems Electrophysiology Laboratory, Mayo Clinic, Rochester, MN, United States of America.
  • Benjamin Brinkmann
  • Ravishankar Iyer
  • Gregory Worrell