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A hybrid unsupervised approach toward EEG epileptic spikes detection

Epileptic spikes are complementary sources of information in EEG to diagnose and localize the origin of epilepsy. However, not only is visual inspection of EEG labor intensive, time consuming, and prone to human error, but it also needs long-term training to acquire the level of skill required for i...

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Main Authors:	Khosropanah, Pegah, Ramli, Abdul Rahman, Abbasi, Mohammad Reza, Marhaban, Mohammad Hamiruce, Ahmedov, Anvarjon
Format:	Article
Language:	English English
Published:	Springer Verlag (Germany) 2018
Subjects:	QA75 Electronic computers. Computer science QM Human anatomy QP Physiology R Medicine (General) RZ Other systems of medicine TK Electrical engineering. Electronics Nuclear engineering
Online Access:	http://eprints.um.edu.my/19662/1/10.1007%40s00521-018-3797-2.pdf http://eprints.um.edu.my/19662/2/10.1007%40s00521-018-3797-2.pdf http://eprints.um.edu.my/19662/ http://dx.doi.org/10.1007/s00521-018-3797-2
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spelling	my.um.eprints.196622018-10-17T02:00:57Z http://eprints.um.edu.my/19662/ A hybrid unsupervised approach toward EEG epileptic spikes detection Khosropanah, Pegah Ramli, Abdul Rahman Abbasi, Mohammad Reza Marhaban, Mohammad Hamiruce Ahmedov, Anvarjon QA75 Electronic computers. Computer science QM Human anatomy QP Physiology R Medicine (General) RZ Other systems of medicine TK Electrical engineering. Electronics Nuclear engineering Epileptic spikes are complementary sources of information in EEG to diagnose and localize the origin of epilepsy. However, not only is visual inspection of EEG labor intensive, time consuming, and prone to human error, but it also needs long-term training to acquire the level of skill required for identifying epileptic discharges. Therefore, computer-aided approaches were employed for the purpose of saving time and increasing the detection and source localization accuracy. One of the most important artifacts that may be confused as an epileptic spike, due to morphological resemblance, is eye blink. Only a few studies consider removal of this artifact prior to detection, and most of them used either visual inspection or computer-aided approaches, which need expert supervision. Consequently, in this paper, an unsupervised and EEG-based system with embedded eye blink artifact remover is developed to detect epileptic spikes. The proposed system includes three stages: eye blink artifact removal, feature extraction, and classification. Wavelet transform was employed for both artifact removal and feature extraction steps, and adaptive neuro-fuzzy inference system for classification purpose. The proposed method is verified using a publicly available EEG dataset. The results show the efficiency of this algorithm in detecting epileptic spikes using low-resolution EEG with least computational complexity, highest sensitivity, and lesser human interaction compared to similar studies. Moreover, since epileptic spike detection is a vital component of epilepsy source localization, therefore this algorithm can be utilized for EEG-based pre-surgical evaluation of epilepsy. Springer Verlag (Germany) 2018 Article PeerReviewed application/pdf en http://eprints.um.edu.my/19662/1/10.1007%40s00521-018-3797-2.pdf application/pdf en http://eprints.um.edu.my/19662/2/10.1007%40s00521-018-3797-2.pdf Khosropanah, Pegah and Ramli, Abdul Rahman and Abbasi, Mohammad Reza and Marhaban, Mohammad Hamiruce and Ahmedov, Anvarjon (2018) A hybrid unsupervised approach toward EEG epileptic spikes detection. Neural Computing and Applications. pp. 1-22. ISSN 0941-0643 http://dx.doi.org/10.1007/s00521-018-3797-2 doi:10.1007/s00521-018-3797-2
institution	Universiti Malaya
building	UM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Malaya
content_source	UM Research Repository
url_provider	http://eprints.um.edu.my/
language	English English
topic	QA75 Electronic computers. Computer science QM Human anatomy QP Physiology R Medicine (General) RZ Other systems of medicine TK Electrical engineering. Electronics Nuclear engineering
spellingShingle	QA75 Electronic computers. Computer science QM Human anatomy QP Physiology R Medicine (General) RZ Other systems of medicine TK Electrical engineering. Electronics Nuclear engineering Khosropanah, Pegah Ramli, Abdul Rahman Abbasi, Mohammad Reza Marhaban, Mohammad Hamiruce Ahmedov, Anvarjon A hybrid unsupervised approach toward EEG epileptic spikes detection
description	Epileptic spikes are complementary sources of information in EEG to diagnose and localize the origin of epilepsy. However, not only is visual inspection of EEG labor intensive, time consuming, and prone to human error, but it also needs long-term training to acquire the level of skill required for identifying epileptic discharges. Therefore, computer-aided approaches were employed for the purpose of saving time and increasing the detection and source localization accuracy. One of the most important artifacts that may be confused as an epileptic spike, due to morphological resemblance, is eye blink. Only a few studies consider removal of this artifact prior to detection, and most of them used either visual inspection or computer-aided approaches, which need expert supervision. Consequently, in this paper, an unsupervised and EEG-based system with embedded eye blink artifact remover is developed to detect epileptic spikes. The proposed system includes three stages: eye blink artifact removal, feature extraction, and classification. Wavelet transform was employed for both artifact removal and feature extraction steps, and adaptive neuro-fuzzy inference system for classification purpose. The proposed method is verified using a publicly available EEG dataset. The results show the efficiency of this algorithm in detecting epileptic spikes using low-resolution EEG with least computational complexity, highest sensitivity, and lesser human interaction compared to similar studies. Moreover, since epileptic spike detection is a vital component of epilepsy source localization, therefore this algorithm can be utilized for EEG-based pre-surgical evaluation of epilepsy.
format	Article
author	Khosropanah, Pegah Ramli, Abdul Rahman Abbasi, Mohammad Reza Marhaban, Mohammad Hamiruce Ahmedov, Anvarjon
author_facet	Khosropanah, Pegah Ramli, Abdul Rahman Abbasi, Mohammad Reza Marhaban, Mohammad Hamiruce Ahmedov, Anvarjon
author_sort	Khosropanah, Pegah
title	A hybrid unsupervised approach toward EEG epileptic spikes detection
title_short	A hybrid unsupervised approach toward EEG epileptic spikes detection
title_full	A hybrid unsupervised approach toward EEG epileptic spikes detection
title_fullStr	A hybrid unsupervised approach toward EEG epileptic spikes detection
title_full_unstemmed	A hybrid unsupervised approach toward EEG epileptic spikes detection
title_sort	hybrid unsupervised approach toward eeg epileptic spikes detection
publisher	Springer Verlag (Germany)
publishDate	2018
url	http://eprints.um.edu.my/19662/1/10.1007%40s00521-018-3797-2.pdf http://eprints.um.edu.my/19662/2/10.1007%40s00521-018-3797-2.pdf http://eprints.um.edu.my/19662/ http://dx.doi.org/10.1007/s00521-018-3797-2
_version_	1643691053456293888
score	13.211869

A hybrid unsupervised approach toward EEG epileptic spikes detection

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