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Year : 2011  |  Volume : 1  |  Issue : 1  |  Page : 62-72

A brief survey of computational models of normal and epileptic EEG signals: A guideline to model-based seizure prediction

1 Medical Image and Signal Processing Research Center, Isfahan University of Medical Sciences; Departments of Electrical and Computer Engineering, Digital Signal Processing Laboratory, Isfahan University of Technology, Isfahan, Iran
2 Department of Electrical, Engineering Faculty, Shahid Chamran University of Ahvaz, Ahvaz, Iran

Correspondence Address:
Farzaneh Shayegh
Medical Image and Signal Processing Research Center, Isfahan University of Medical Sciences, Isfahan
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/2228-7477.83521

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

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