Please use this identifier to cite or link to this item: https://doi.org/10.1109/IEMBS.2006.259990
Title: Elman neural networks for dynamic modeling of epileptic EEG
Authors: Kannathal, N.
Puthusserypady, S.K. 
Min, L.C.
Keywords: Autoregressive modeling
EEG
Epilepsy
Neural networks
Issue Date: 2006
Source: Kannathal, N.,Puthusserypady, S.K.,Min, L.C. (2006). Elman neural networks for dynamic modeling of epileptic EEG. Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings : 6145-6148. ScholarBank@NUS Repository. https://doi.org/10.1109/IEMBS.2006.259990
Abstract: In this paper, autoregressive modeling technique and neural network based modeling techniques are used to model and simulate electroencephalogram (EEG) signals. EEG signal modeling is used as a tool to identify pathophysiological EEG changes potentially useful in clinical diagnosis. The normal, background and epileptic EEG signals are modeled and the dynamical properties of the actual and modeled signals are compared. Chaotic invariants like correlation dimension (D2), largest Lyapunov exponent (λ1) , Hurst exponent (H) and Kolmogorov entropy (K) are used to characterize the dynamical properties of the actual and modeled signals. Our study showed that the dynamical properties of the EEG signal modeled using neural network (NN) techniques are very similar to that of the signal. © 2006 IEEE.
Source Title: Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
URI: http://scholarbank.nus.edu.sg/handle/10635/83691
ISBN: 1424400325
ISSN: 05891019
DOI: 10.1109/IEMBS.2006.259990
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