A comparison of two techniques for detecting seizure in newborn EEG data

This paper considers the problem of automatic classification of newborn electroencephalogram (EEG) data in order to diagnose for seizure. It is shown that good detection performance of seizure EEG is possible using a methodology based on a model for the generation of the EEG. This model is derived from the histology and biophysics of a localised portion of the brain and is thus physically motivated. The model based detection scheme is first presented and used to detect seizure in real newborn EEG data. These results are then compared with an alternative classification approach known as the quadratic detection filter (QDF). It is shown that the model based scheme is far superior to the QDF since it is not adversely affected by the variability (or non-stationarity) of EEG data, which hinders the performance of most traditional EEG classifiers (such as the QDF).