Detection of newborns´ EEG seizure using time-frequency divergence measures

A time-frequency approach for detecting seizure activities in newborns´ electroencephalogram (EEG) data is proposed. The discrimination between seizure and non-seizure states is based on the time-frequency distance between the consequent segments in the EEG signal. Three different time-frequency measures and three different reduced time-frequency distributions are used. The proposed method is tested on the EEG data acquired from three neonates with ages ranging from two days to two weeks. The experimental results validate the suitability of the proposed method in automated newborns´ seizure detection. The results show an average seizure detection rate of 96% and false alarm rate of 5%.