Classification of Scalp EEG States Prior to Clinical Seizure Onset

Abstract

Objective: To investigate the feasibility of improving the performance of an EEG-based multistate classifier (MSC) previously proposed by our group. Results: Using the random forest (RF) classifiers on the previously reported dataset of patients, but with three improvements to classification logic, the specificity of our alarm algorithm improves from 82.4% to 92.0%, and sensitivity from 87.9% to 95.2%. Discussion: The MSC could be a useful approach for seizure-monitoring both in the clinic and at home. Methods: Three improvements to the MSC are described. Firstly, an additional check using RF outputs is made prior to alarm to confirm increasing probability of a seizure onset state. Secondly, a post-alarm detection horizon that accounts for the seizure state duration is implemented. Thirdly, the alarm decision window is kept constant.