Classifying Burst and Suppression in the EEG of Post Asphyctic Newborns using a Support Vector Machine

Abstract

A support vector machine (SVM) was trained to distinguish bursts from suppression in burst-suppression EEG, using five features inherent in the electro-encephalogram (EEG) as input. The study was based on data from six full term infants who had suffered from perinatal asphyxia, and the machine was trained with reference classifications made by an experienced electroencephalographer. The results show that the method may be useful, but that differences between patients in the data set makes optimization of the system difficult