Epileptic seizure identification from electroencephalography signal using DE-RBFNs ensemble

被引：16

作者：

Dehuri, Satchidanada ^{[1
]}

Jagadev, Alok Kumar ^{[2
]}

Cho, Sung-Bae ^{[3
]}

机构：

[1] Ajou Univ, Dept Syst Engn, San 5, Suwon 443749, South Korea

[2] SOA Univ, Dept Comp Sci & Engn, Bhubaneswar, Orissa, India

[3] Yonsei Univ, Soft Comp Lab, Dept Comp Sci, Seoul 120749, South Korea

来源：

4TH INTERNATIONAL CONFERENCE ON COMPUTATIONAL SYSTEMS-BIOLOGY AND BIOINFORMATICS (CSBIO2013) | 2013年 / 23卷

基金：

新加坡国家研究基金会;

关键词：

EEG; classification; radial basis function neural networks; differential evolution; bagging; ARTIFICIAL NEURAL-NETWORK; EEG; CLASSIFICATION; REGRESSION; MODEL;

D O I：

10.1016/j.procs.2013.10.012

中图分类号：

Q5 [生物化学]; Q7 [分子生物学];

学科分类号：

071010 ; 081704 ;

摘要：

In this paper, an ensemble of radial basis function neural networks (RBFNs) optimized by differential evolution (DE) (DERBFNs) is presented for identification of epileptic seizure by analyzing the electroencephalography (EEG) signal. The ensemble is based on the bagging approach and the base learner is DE-RBFNs. The EEGs are decomposed with wavelet transform into different sub-bands and some statistical information is extracted from the wavelet coefficients to supply as the input to ensemble of DE-RBFNs. A benchmark publicly available dataset is used to evaluate the proposed method. The classification results confirm that the proposed ensemble of DE-RBFNs has greater potentiality to identify the epileptic disorders. (C) 2013 The Authors. Published by Elsevier B.V.

引用

页码：84 / 95

页数：12

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