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Implementation of Epileptic EEG using Recurrent Neural Network


M. Gayatri, Arun Kumar, Manish Janghu, Mandeep Kaur, T.V. Prasad


Vol. 10  No. 3  pp. 290-296


The ambulant EEG (electroencephalogram) signal plays an important role in the diagnosis of epilepsy but data recordings generate very lengthy data in the detection of epilepsy which is very time consuming. The traditional method of analysis being tedious, many automated diagnostic systems for epilepsy has emerged in recent years. This paper proposes reason for epilepsy, different type of seizures, stage of epilepsy in patient and how it can be implemented using Artificial Neural Network naming Elman Neural Networks. We know that the value of the ApEn drops sharply during an epileptic seizure so we used it as an input feature. ApEn is a statistical parameter that measures the predictability of the current amplitude values of a physiological signal based on its previous amplitude values. ApEn is used for the first time in the proposed system for the implementation of epilepsy using neural networks.


Approximate entropy, Artificial Neural Network, Epilepsy Electroencephalogram (EEG), Elman Neural Network, Seizure