Detection of seizures in intracranial EEG: UPenn and Mayo Clinic's Seizure Detection Challenge.

Abstract

A system for detection of seizures in intracranial EEG is presented that is based on a combination of generative, discriminative and hybrid approaches. We present a methodology to effectively benefit from the advantages each classifier offers. In particular, Gaussian mixture models, Support Vector Machines, hybrid likelihood ratio and Gaussian supervector approaches are developed and combined for the task. This system participated in the UPenn and Mayo Clinic's Seizure Detection Challenge, ranking in the top 5 of over 200 participants. The drawbacks of the proposed method with respect to the winning solutions are critically assessed.