Title: An approach to automated classification of epileptic seizures using Artificial Neural Network

Authors: D. Najumnissa, S. Shenbaga Devi

Addresses: Department of Instrumentation and Control Engineering, BSA Crescent Engineering College, Chennai, India. ' Center for Medical Electronics, Department of Electronics and Communication Engineering, College of Engineering, Guindy, Anna University, Chennai, India

Abstract: Epileptic seizures are important public health issues as they affect 0.8% of humans. Electroencephalograph (EEG) records provide important understanding of epileptic disorders. The conventional method is to interpret by visual inspection of common patterns. This work deals with novel method of data generation using feature extraction and classification using Back Propagation Algorithm. Twelve patients| EEG is used for training and six patients| EEG for testing. Thus, the designed network classifies normal and types of abnormal EEG like focal, absence and tonic-clonic seizures. The network correctly classified normal and abnormal conditions. The performance of neural model has an accuracy of 96.3%.

Keywords: ANNs; artificial neural networks; BPN; backpropagation algorithm; epileptic seizures; epilepsy; EEG; electroencephalograph; feature extraction; hidden nodes; network architecture; network training; performance analysis; seizure detection; classification.

DOI: 10.1504/IJBET.2009.027801

International Journal of Biomedical Engineering and Technology, 2009 Vol.2 No.4, pp.382 - 399

Published online: 11 Aug 2009 *

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