Feature selection using angle modulated simulated Kalman filter for peak classification of EEG signals

Adam, A. and Ibrahim, Z. and Mokhtar, N. and Shapiai, M.I. and Mubin, M. and Saad, I. (2016) Feature selection using angle modulated simulated Kalman filter for peak classification of EEG signals. SpringerPlus, 5 (1). p. 1580. ISSN 2193-1801, DOI https://doi.org/10.1186/s40064-016-3277-z.

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Official URL: http://dx.doi.org/10.1186/s40064-016-3277-z


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.

Item Type: Article
Funders: High Impact Research Fund (UM.C/HIR/MOHE/ENG/16 Account code: D000016-16001), Matching Grant (Q.K130000.3043.00M79), Internal UMP Grant (GRS1503120)
Uncontrolled Keywords: Neural network with random weights (NNRW ); Kalman filtering; Simulated Kalman filter (SKF); Electroencephalogram (EEG); Peak detection algorithm; Pattern recognition
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Faculty of Engineering
Depositing User: Ms. Juhaida Abd Rahim
Date Deposited: 23 Oct 2017 04:12
Last Modified: 23 Oct 2017 04:13
URI: http://eprints.um.edu.my/id/eprint/18053

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