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Computational and Mathematical Methods in Medicine
Volume 2017, Article ID 6849360, 10 pages
https://doi.org/10.1155/2017/6849360
Research Article

Automatic Detection of Epilepsy and Seizure Using Multiclass Sparse Extreme Learning Machine Classification

1School of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an 710049, China
2Guangdong Xi’an Jiaotong University Academy, Shunde 528300, China

Correspondence should be addressed to Zunchao Li; nc.ude.utjx@ilcz

Received 21 March 2017; Accepted 21 May 2017; Published 19 June 2017

Academic Editor: Valeri Makarov

Copyright © 2017 Yuanfa Wang 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

An automatic detection system for distinguishing normal, ictal, and interictal electroencephalogram (EEG) signals is of great help in clinical practice. This paper presents a three-class classification system based on discrete wavelet transform (DWT) and the nonlinear sparse extreme learning machine (SELM) for epilepsy and epileptic seizure detection. Three-level lifting DWT using Daubechies order 4 wavelet is introduced to decompose EEG signals into delta, theta, alpha, and beta subbands. Considering classification accuracy and computational complexity, the maximum and standard deviation values of each subband are computed to create an eight-dimensional feature vector. After comparing five multiclass SELM strategies, the one-against-one strategy with the highest accuracy is chosen for the three-class classification system. The performance of the designed three-class classification system is tested with publicly available epilepsy dataset. The results show that the system achieves high enough classification accuracy by combining the SELM and DWT and reduces training and testing time by decreasing computational complexity and feature dimension. With excellent classification performance and low computation complexity, this three-class classification system can be utilized for practical epileptic EEG detection, and it offers great potentials for portable automatic epilepsy and seizure detection system in the future hardware implementation.