A machine-learning algorithm for detecting seizure termination in scalp EEG

Abstract

Efforts to develop algorithms that can robustly detect the cessation of seizure activity within scalp EEGs are now underway. Such algorithms can facilitate novel clinical applications such as the estimation of a seizure's duration; the delivery of therapies designed to mitigate postictal period symptoms; or detection of the presence of status epilepticus. In this article, we present and evaluate a novel, machine learning-based method for detecting the termination of electrographic seizure activity. When tested on 133 seizures from a public database, our method successfully detected the end of 132 seizures within 10.3 ± 5.5 seconds of the time determined by an electroencephalographer to represent the electrographic end of seizure. Furthermore, by pairing our seizure end detector with a previously published seizure onset detector, we could automatically estimate the duration of 85% of test electrographic seizures within a 15-second error margin compared with electroencephalographer determinations. This article is part of a Supplemental Special Issue entitled The Future of Automated Seizure Detection and Prediction.