Detection of newborn EEG seizure using optimal features based on discrete wavelet transform

A new automated method is proposed to detect seizure events in newborns from electroencephalogram (EEG) data. The detection scheme is based on observing the changing behavior of the wavelet coefficients (WCs) of the EEG signal at different scales. An optimal feature subset is obtained using the mutual information evaluation function (MIEF). The MIEF algorithm evaluates a set of candidate features extracted from WCs to select an informative feature subset. The subset is then fed to an artificial neural network (ANN) classifier that organizes the EEG signal into seizure or non-seizure activity. The performance of the proposed features is compared with that of the features obtained using a mutual information feature selection (MIFS) algorithm. The training and test sets are obtained from EEG data acquired from 5 neonates with ages ranging from 2 days to 2 weeks.