Feature Selection using Angle Modulated Simulated Kalman Filter for Peak Classification of EEG Signals

In the existing electroencephalogram (EEG) signals peak classification research, the existing models, such as Dumpala, Acir, Liu, and Dingle peak models, employ different set of features. However, all these models may not be able to offer good performance for various applications and it is found to...

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Main Authors:	Asrul, Adam, Zuwairie, Ibrahim, Norrima, Mokhtar, Mohd Ibrahim, Shapiai, Marizan, Mubin, Ismail, Saad
Format:	Article
Language:	English
Published:	Springer 2016
Subjects:	TK Electrical engineering. Electronics Nuclear engineering
Online Access:	http://umpir.ump.edu.my/id/eprint/14661/ http://umpir.ump.edu.my/id/eprint/14661/ http://umpir.ump.edu.my/id/eprint/14661/ http://umpir.ump.edu.my/id/eprint/14661/1/Feature%20selection%20using%20angle%20modulated%20simulated%20Kalman%20filter%20for%20peak%20classification%20of%20EEG%20signals.pdf

id	ump-14661
recordtype	eprints
spelling	ump-146612018-02-08T00:28:46Z http://umpir.ump.edu.my/id/eprint/14661/ Feature Selection using Angle Modulated Simulated Kalman Filter for Peak Classification of EEG Signals Asrul, Adam Zuwairie, Ibrahim Norrima, Mokhtar Mohd Ibrahim, Shapiai Marizan, Mubin Ismail, Saad TK Electrical engineering. Electronics Nuclear engineering In the existing electroencephalogram (EEG) signals peak classification research, the existing models, such as Dumpala, Acir, Liu, and Dingle peak models, employ different set of features. However, all these models may not be able to offer good performance for various applications and it is found to be problem dependent. Therefore, the objective of this study is to combine all the associated features from the existing models before selecting the best combination of features. A new optimization algorithm, namely as angle modulated simulated Kalman filter (AMSKF) will be employed as feature selector. Also, the neural network random weight method is utilized in the proposed AMSKF technique as a classifier. In the conducted experiment, 11,781 samples of peak candidate are employed in this study for the validation purpose. The samples are collected from three different peak event-related EEG signals of 30 healthy subjects; (1) single eye blink, (2) double eye blink, and (3) eye movement signals. The experimental results have shown that the proposed AMSKF feature selector is able to find the best combination of features and performs at par with the existing related studies of epileptic EEG events classification. Springer 2016 Article PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/14661/1/Feature%20selection%20using%20angle%20modulated%20simulated%20Kalman%20filter%20for%20peak%20classification%20of%20EEG%20signals.pdf Asrul, Adam and Zuwairie, Ibrahim and Norrima, Mokhtar and Mohd Ibrahim, Shapiai and Marizan, Mubin and Ismail, Saad (2016) Feature Selection using Angle Modulated Simulated Kalman Filter for Peak Classification of EEG Signals. SpringerPlus. pp. 1-24. ISSN 2193-1801 http://springerplus.springeropen.com/articles DOI: 10.1186/s40064-016-3277-z
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	TK Electrical engineering. Electronics Nuclear engineering
spellingShingle	TK Electrical engineering. Electronics Nuclear engineering Asrul, Adam Zuwairie, Ibrahim Norrima, Mokhtar Mohd Ibrahim, Shapiai Marizan, Mubin Ismail, Saad Feature Selection using Angle Modulated Simulated Kalman Filter for Peak Classification of EEG Signals
description	In the existing electroencephalogram (EEG) signals peak classification research, the existing models, such as Dumpala, Acir, Liu, and Dingle peak models, employ different set of features. However, all these models may not be able to offer good performance for various applications and it is found to be problem dependent. Therefore, the objective of this study is to combine all the associated features from the existing models before selecting the best combination of features. A new optimization algorithm, namely as angle modulated simulated Kalman filter (AMSKF) will be employed as feature selector. Also, the neural network random weight method is utilized in the proposed AMSKF technique as a classifier. In the conducted experiment, 11,781 samples of peak candidate are employed in this study for the validation purpose. The samples are collected from three different peak event-related EEG signals of 30 healthy subjects; (1) single eye blink, (2) double eye blink, and (3) eye movement signals. The experimental results have shown that the proposed AMSKF feature selector is able to find the best combination of features and performs at par with the existing related studies of epileptic EEG events classification.
format	Article
author	Asrul, Adam Zuwairie, Ibrahim Norrima, Mokhtar Mohd Ibrahim, Shapiai Marizan, Mubin Ismail, Saad
author_facet	Asrul, Adam Zuwairie, Ibrahim Norrima, Mokhtar Mohd Ibrahim, Shapiai Marizan, Mubin Ismail, Saad
author_sort	Asrul, Adam
title	Feature Selection using Angle Modulated Simulated Kalman Filter for Peak Classification of EEG Signals
title_short	Feature Selection using Angle Modulated Simulated Kalman Filter for Peak Classification of EEG Signals
title_full	Feature Selection using Angle Modulated Simulated Kalman Filter for Peak Classification of EEG Signals
title_fullStr	Feature Selection using Angle Modulated Simulated Kalman Filter for Peak Classification of EEG Signals
title_full_unstemmed	Feature Selection using Angle Modulated Simulated Kalman Filter for Peak Classification of EEG Signals
title_sort	feature selection using angle modulated simulated kalman filter for peak classification of eeg signals
publisher	Springer
publishDate	2016
url	http://umpir.ump.edu.my/id/eprint/14661/ http://umpir.ump.edu.my/id/eprint/14661/ http://umpir.ump.edu.my/id/eprint/14661/ http://umpir.ump.edu.my/id/eprint/14661/1/Feature%20selection%20using%20angle%20modulated%20simulated%20Kalman%20filter%20for%20peak%20classification%20of%20EEG%20signals.pdf
first_indexed	2023-09-18T22:18:39Z
last_indexed	2023-09-18T22:18:39Z
_version_	1777415507436109824

Feature Selection using Angle Modulated Simulated Kalman Filter for Peak Classification of EEG Signals

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