Newborn EEG seizure detection using optimized time-frequency matched filter

In recent years, much effort has been made toward developing computerized methods to detect seizures. In adults, the clinical signs of seizures are well defined and easily recognizable. This is, however, not the case for newborns where the clinical signs are either subtle or completely absent. For this reason, the electroencephalogram (EEG) has been the most dependable tool used for detecting seizures in newborns. Considering the non-stationary and multicomponent nature of the EEG signals, time-frequency (TF) based methods were found to be very suitable for the analysis of such signals. Using TF representation of EEG signals allows extracting TF signatures that are characteristic of EEG seizures. In this paper we present a TF method for newborn EEG seizure detection using a TF matched filter. The TF signatures of EEG seizures are used to construct time-frequency templates that are used by the matched filter to detect EEG seizures. The results obtained so far are very promising.