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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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 |
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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 |
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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 |
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Springer Verlag |
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2014 |
url |
http://eprints.utm.my/id/eprint/52122/ https://link.springer.com/chapter/10.1007%2F978-3-319-11897-0_41 |
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