Designing a robust feature extraction method based on optimum allocation and principal component analysis for epileptic EEG signal classification

Siuly, Siuly and Li, Yan (2015) Designing a robust feature extraction method based on optimum allocation and principal component analysis for epileptic EEG signal classification. Computer Methods and Programs in Biomedicine, 119 (1). pp. 29-42. ISSN 0169-2607

Abstract

The aim of this study is to design a robust feature extraction method for the classification of multiclass EEG signals to determine valuable features from original epileptic EEG data and to discover an efficient classifier for the features. An optimum allocation based principal component analysis method named as OA_PCA is developed for the feature extraction from epileptic EEG data. As EEG data from different channels are correlated and huge in number, the optimum allocation (OA) scheme is used to discover the most favorable representatives with minimal variability from a large number of EEG data. The principal component analysis (PCA) is applied to construct uncorrelated components and also to reduce the dimensionality of the OA samples for an enhanced recognition. In order to choose a suitable classifier for the OA_PCA feature set, four popular classifiers: least square support vector machine (LS-SVM), naive bayes classifier (NB), k-nearest neighbor algorithm (KNN), and linear discriminant analysis (LDA) are applied and tested. Furthermore, our approaches are also compared with some recent research work. The experimental results show that the LS-SVM_1v1 approach yields 100% of the overall classification accuracy (OCA), improving up to 7.10% over the existing algorithms for the epileptic EEG data. The major finding of this research is that the LS-SVM with the 1v1 system is the best technique for the OA_PCA features in the epileptic EEG signal classification that outperforms all the recent reported existing methods in the literature.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: © 2015 Elsevier Ireland Ltd. Permanent restricted access to published version due to publisher copyright policy.
Faculty / Department / School: Current - Faculty of Health, Engineering and Sciences - School of Agricultural, Computational and Environmental Sciences
Date Deposited: 24 Mar 2015 06:49
Last Modified: 29 Jun 2016 03:25
Uncontrolled Keywords: electroencephalogram (EEG); K-nearest neighbor algorithm (KNN); least square support vector machine (LS-SVM); naive Bayes classifier (NB); optimum allocation; principal component analysis
Fields of Research : 11 Medical and Health Sciences > 1109 Neurosciences > 110904 Neurology and Neuromuscular Diseases
09 Engineering > 0906 Electrical and Electronic Engineering > 090609 Signal Processing
01 Mathematical Sciences > 0102 Applied Mathematics > 010202 Biological Mathematics
Socio-Economic Objective: E Expanding Knowledge > 97 Expanding Knowledge > 970109 Expanding Knowledge in Engineering
Identification Number or DOI: 10.1016/j.cmpb.2015.01.002
URI: http://eprints.usq.edu.au/id/eprint/26952

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