Epileptic seizure identification from electroencephalography signal using DE-RBFNs ensemble

Satchidanada Dehuri; Alok Kumar Jagadev; Sung Bae Cho

doi:10.1016/j.procs.2013.10.012

Epileptic seizure identification from electroencephalography signal using DE-RBFNs ensemble

Satchidanada Dehuri, Alok Kumar Jagadev, Sung Bae Cho

College of Computing

Research output: Contribution to journal › Conference article › peer-review

19 Citations (Scopus)

Abstract

In this paper, an ensemble of radial basis function neural networks (RBFNs) optimized by differential evolution (DE) (DE-RBFNs) 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.

Original language	English
Pages (from-to)	84-95
Number of pages	12
Journal	Procedia Computer Science
Volume	23
DOIs	https://doi.org/10.1016/j.procs.2013.10.012
Publication status	Published - 2013
Event	4th International Conference on Computational Systems-Biology and Bioinformatics, CSBio 2013 - Seoul, Korea, Republic of Duration: 2013 Nov 7 → 2013 Nov 9

Bibliographical note

Funding Information:
Authors are gratefully acknowledge the support of the Original Technology Research Program for Brain Science through the National Research Foundation (NRF) of Korea (NRF: 2010-0018948) funded by the Ministry of Education, Science, and Technology.

All Science Journal Classification (ASJC) codes

Computer Science(all)

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10.1016/j.procs.2013.10.012

Cite this

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title = "Epileptic seizure identification from electroencephalography signal using DE-RBFNs ensemble",

abstract = "In this paper, an ensemble of radial basis function neural networks (RBFNs) optimized by differential evolution (DE) (DE-RBFNs) 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.",

author = "Satchidanada Dehuri and Jagadev, {Alok Kumar} and Cho, {Sung Bae}",

note = "Funding Information: Authors are gratefully acknowledge the support of the Original Technology Research Program for Brain Science through the National Research Foundation (NRF) of Korea (NRF: 2010-0018948) funded by the Ministry of Education, Science, and Technology.; 4th International Conference on Computational Systems-Biology and Bioinformatics, CSBio 2013 ; Conference date: 07-11-2013 Through 09-11-2013",

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T1 - Epileptic seizure identification from electroencephalography signal using DE-RBFNs ensemble

AU - Dehuri, Satchidanada

AU - Jagadev, Alok Kumar

AU - Cho, Sung Bae

N1 - Funding Information: Authors are gratefully acknowledge the support of the Original Technology Research Program for Brain Science through the National Research Foundation (NRF) of Korea (NRF: 2010-0018948) funded by the Ministry of Education, Science, and Technology.

PY - 2013

Y1 - 2013

N2 - In this paper, an ensemble of radial basis function neural networks (RBFNs) optimized by differential evolution (DE) (DE-RBFNs) 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.

AB - In this paper, an ensemble of radial basis function neural networks (RBFNs) optimized by differential evolution (DE) (DE-RBFNs) 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.

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DO - 10.1016/j.procs.2013.10.012

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AN - SCOPUS:84896964008

SN - 1877-0509

VL - 23

SP - 84

EP - 95

JO - Procedia Computer Science

JF - Procedia Computer Science

T2 - 4th International Conference on Computational Systems-Biology and Bioinformatics, CSBio 2013

Y2 - 7 November 2013 through 9 November 2013

ER -

Epileptic seizure identification from electroencephalography signal using DE-RBFNs ensemble

Abstract

Bibliographical note

All Science Journal Classification (ASJC) codes

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