This investigation is part of a major project of the Signal Processing Research Centre (SPRC) to develop a technique to automatically detect epileptic seizure in newborns using EEG. Currently there are three published techniques under examination by the SPRC that aim to achieve this. The technique of Roessgen et al. (1998) is model based and uses parameter estimation for detection. The two other methods are non-parametric. The technique of Gotman et al. (1997) uses frequency analysis to detect changes in the dominant peak of the frequency spectrum of short epochs of EEG data. The technique of Liu et al. (1992) performs analysis in the time domain and is based on the autocorrelation function of short epochs of EEG data. Despite varying approaches, the techniques investigated here all attempt to detect periodicity in the EEG. This periodicity is the main characteristic of EEG seizure waveforms. The underlying methodologies of the three published techniques are discussed. Implementation of the three techniques is also discussed. Further work will involve the comparison of the three implementations on a common set of neonatal EEG recordings. It is anticipated that time-frequency analysis of neonatal EEG will be pursued as the basis for future detection techniques.