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Classification of electroencephalogram signals using wavelet transform and particle swarm optimization

The electroencephalogram (EEG) is a signal measuring activities of the brain. Therefore, it contains useful information for diagnosis of epilepsy. However, it is a very time consuming and costly task to handle these subtle details by a human observer. In this paper, particle swarm optimization (PSO)...

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Main Authors: Ba-Karait, Nasser Omer, Shamsuddin, Siti Mariyam, Sudirman, Rubita
Format: Article
Published: Springer Verlag 2014
Subjects:
QA75 Electronic computers. Computer science
Online Access:http://eprints.utm.my/id/eprint/52122/
https://link.springer.com/chapter/10.1007%2F978-3-319-11897-0_41
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spelling my.utm.521222019-01-28T04:30:52Z http://eprints.utm.my/id/eprint/52122/ Classification of electroencephalogram signals using wavelet transform and particle swarm optimization Ba-Karait, Nasser Omer Shamsuddin, Siti Mariyam Sudirman, Rubita QA75 Electronic computers. Computer science The electroencephalogram (EEG) is a signal measuring activities of the brain. Therefore, it contains useful information for diagnosis of epilepsy. However, it is a very time consuming and costly task to handle these subtle details by a human observer. In this paper, particle swarm optimization (PSO) was proposed to automate the process of seizure detection in EEG signals. Initially, the EEG signals have been analysed using discrete wavelet transform (DWT) for features extraction. Then, the PSO algorithm has been trained to recognize the epileptic signals in EEG data. The results demonstrate the effectiveness of the proposed method in terms of classification accuracy and stability. A comparison with other methods in the literature confirms the superiority of the PSO. Springer Verlag 2014 Article PeerReviewed Ba-Karait, Nasser Omer and Shamsuddin, Siti Mariyam and Sudirman, Rubita (2014) Classification of electroencephalogram signals using wavelet transform and particle swarm optimization. Advances in Swarm Intelligence, ICSI 2014, PT II, 8795 . pp. 352-362. ISSN 0302-9743 https://link.springer.com/chapter/10.1007%2F978-3-319-11897-0_41
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Ba-Karait, Nasser Omer
Shamsuddin, Siti Mariyam
Sudirman, Rubita
Classification of electroencephalogram signals using wavelet transform and particle swarm optimization
description The electroencephalogram (EEG) is a signal measuring activities of the brain. Therefore, it contains useful information for diagnosis of epilepsy. However, it is a very time consuming and costly task to handle these subtle details by a human observer. In this paper, particle swarm optimization (PSO) was proposed to automate the process of seizure detection in EEG signals. Initially, the EEG signals have been analysed using discrete wavelet transform (DWT) for features extraction. Then, the PSO algorithm has been trained to recognize the epileptic signals in EEG data. The results demonstrate the effectiveness of the proposed method in terms of classification accuracy and stability. A comparison with other methods in the literature confirms the superiority of the PSO.
format Article
author Ba-Karait, Nasser Omer
Shamsuddin, Siti Mariyam
Sudirman, Rubita
author_facet Ba-Karait, Nasser Omer
Shamsuddin, Siti Mariyam
Sudirman, Rubita
author_sort Ba-Karait, Nasser Omer
title Classification of electroencephalogram signals using wavelet transform and particle swarm optimization
title_short Classification of electroencephalogram signals using wavelet transform and particle swarm optimization
title_full Classification of electroencephalogram signals using wavelet transform and particle swarm optimization
title_fullStr Classification of electroencephalogram signals using wavelet transform and particle swarm optimization
title_full_unstemmed Classification of electroencephalogram signals using wavelet transform and particle swarm optimization
title_sort classification of electroencephalogram signals using wavelet transform and particle swarm optimization
publisher Springer Verlag
publishDate 2014
url http://eprints.utm.my/id/eprint/52122/
https://link.springer.com/chapter/10.1007%2F978-3-319-11897-0_41
_version_ 1643653157764464640
score 13.211869

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