Classification of Scalp EEG States Prior to Clinical Seizure Onset

To investigate the feasibility of improving the performance of an EEG-based multistate classifier (MSC) previously proposed by our group.

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%.

The MSC could be a useful approach for seizure-monitoring both in the clinic and at home.

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.