a brief survey of computational models of normal and epileptic eeg signals: a guideline to model based seizure prediction

in recent decades, seizure prediction caused a lot of research in both signal processing and neuroscience field. the researches tried to enhance the conventional seizure prediction algorithms such that rate of the false alarms be appropriately small so that seizures can be predicted according to clinical standards. up to now none of the proposed algorithms have been sufficiently adequate. in this paper we show that considering mechanism of generation of seizure, the prediction results may be improved. for this purpose, an algorithm based on identification of parameters of a physiological model of seizure is introduced. some models of eeg signals that potentially can also be considered as models of seizure and some developed seizure models are reviewed. as an example a model of depth-eeg signals proposed by wendling is studied and is shown to be a suitable model.