ArXiv Preprint
Epilepsy affects millions of people, reducing quality of life and increasing
risk of premature death. One-third of epilepsy cases are drug-resistant and
require surgery for treatment, which necessitates localizing the seizure onset
zone (SOZ) in the brain. Attempts have been made to use cortico-cortical evoked
potentials (CCEPs) to improve SOZ localization but none have been successful
enough for clinical adoption. Here, we compare the performance of ten machine
learning classifiers in localizing SOZ from CCEP data. This preliminary study
validates a novel application of machine learning, and the results establish
our approach as a promising line of research that warrants further
investigation. This work also serves to facilitate discussion and collaboration
with fellow machine learning and/or epilepsy researchers.
Ian G. Malone, Kaleb E. Smith, Morgan E. Urdaneta, Tyler S. Davis, Daria Nesterovich Anderson, Brian J. Phillip, John D. Rolston, Christopher R. Butson
2022-11-15
