Table of Contents Author Guidelines Submit a Manuscript
Applied Bionics and Biomechanics
Volume 2017 (2017), Article ID 6848014, 12 pages
https://doi.org/10.1155/2017/6848014
Research Article

Prediction of Epileptic Seizure by Analysing Time Series EEG Signal Using -NN Classifier

1Department of EEE, Khulna University of Engineering & Technology (KUET), Khulna 9203, Bangladesh
2FSTK, University Sultan Zainal Abidin (UniSZA), 21300 Kuala Terengganu, Terengganu, Malaysia

Correspondence should be addressed to Md. Kamrul Hasan; moc.liamg@teukeeelurmak

Received 30 November 2016; Revised 29 March 2017; Accepted 11 April 2017; Published 13 August 2017

Academic Editor: Thibault Lemaire

Copyright © 2017 Md. Kamrul Hasan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Electroencephalographic signal is a representative signal that contains information about brain activity, which is used for the detection of epilepsy since epileptic seizures are caused by a disturbance in the electrophysiological activity of the brain. The prediction of epileptic seizure usually requires a detailed and experienced analysis of EEG. In this paper, we have introduced a statistical analysis of EEG signal that is capable of recognizing epileptic seizure with a high degree of accuracy and helps to provide automatic detection of epileptic seizure for different ages of epilepsy. To accomplish the target research, we extract various epileptic features namely approximate entropy (ApEn), standard deviation (SD), standard error (SE), modified mean absolute value (MMAV), roll-off (), and zero crossing (ZC) from the epileptic signal. The -nearest neighbours (-NN) algorithm is used for the classification of epilepsy then regression analysis is used for the prediction of the epilepsy level at different ages of the patients. Using the statistical parameters and regression analysis, a prototype mathematical model is proposed which helps to find the epileptic randomness with respect to the age of different subjects. The accuracy of this prototype equation depends on proper analysis of the dynamic information from the epileptic EEG.