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International Journal of

Recent Trends in Engineering

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International Journal of Recent Trends in Engineering (IJRTE)

ISSN 1797-9617

Volume 1, Number 1, May 2009

Issue on Computer Science

Page(s): 307-312

Automated Detection Of Epileptic EEG Using Approximate Entropy In Elman Networks

Srinath Vukkadala, Vijayalakshmi.S, and Vijayapriya.S

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Abstract

Epilepsy detection involves analyzing Electroencephalogram(EEG) signals from the subject. Ambulatory recordings of EEG signals produce lengthy data and the epileptic activity detection requires that the entire length of the EEG data be time consumingly analyzed by an expert. However the traditional methods of analysis are tedious, and so many automated diagnostic systems for epilepsy have emerged in recent years. In this paper an Elman neural network based automated epileptic EEG detection system that uses approximate entropy (ApEn) as the input feature is proposed. ApEn is a statistical parameter that measures the predictability of the current amplitude values of a physiological signal based on its previous amplitude values. It is known that during an epileptic seizure the value of the ApEn drops sharply and this fact is used in the proposed system.

Index Terms

Approximate Entropy, Elman Network, Resilient Backpropagation, Epilepsy, Epileptic EEG Detection, Siezure Detection.

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