Performance comparison of seizure detection methods using EEG of newborns for implementation of a DSP subsystem

This paper deals with the problem of seizure detection in newborns using the EEG signal. The performance of three techniques for seizure detection is investigated. The technique of Roessgen is model based and uses parameter estimation for detection. The two other methods are non-parametric. The technique of Gotman uses frequency analysis to determine the changes in the dominant peak of the frequency spectrum of short epochs of EEG data. The technique of Liu performs analysis in the time domain and is based on the auto-correlation function of short epochs of EEG data. This paper discusses the underlying methodology of the different techniques and presents a comparison of their performance on simulated EEG signal with the aim of considering the best for a possible implementation within a DSP system for automatic seizure detection. The obtained results show that Gotman's method outperforms the two other methods in terms of good detection, the missing detection, and the false alarm rates.