Elman neural networks for dynamic modeling of epileptic EEG.

Elman neural networks for dynamic modeling of epileptic EEG. Research Abstract Details 

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Elman neural networks for dynamic modeling of epileptic EEG. Abstract Text:

N Kannathal,S K Puthusserypady,Lim Choo Min,N Kannathal,Sadasivan K Puthusserypady,Lim Choo Min,

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 (lambda₁, 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.

Elman neural networks for dynamic modeling of epileptic EEG. Publishing Authors By Initials

N Kannathal,SK Puthusserypady,LC Min,N Kannathal,SK Puthusserypady,LC Min,

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Elman neural networks for dynamic modeling of epileptic EEG. Journal Published:

PUBLICATION TYPE: Journal Article

Journal: Conference proceedings : ... Annual International

VOLUME: 1

Page Numbers: 6145-8

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ISSN: 1557-170X

DAY: 23

MONTH: 10

YEAR: 2006

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LANGUAGE: eng

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AFFILIATION: Dept. of ECE, Nat. Univ. of Singapore.

Country: United States

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MEDLINETA: Conf Proc IEEE Eng Med Biol So

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