Classification of epileptic EEG signals based on simple random sampling and sequential feature selection

Al Ghayab, Hadi and Li, Yan and Abdulla, Shahab and Diykh, Mohammed and Wan, Xiangkui (2016) Classification of epileptic EEG signals based on simple random sampling and sequential feature selection. Brain Informatics, 3 (2). pp. 85-91. ISSN 2198-4018

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Abstract

Electroencephalogram (EEG) signals are used broadly in the medical fields. The main applications of EEG signals are the diagnosis and treatment of diseases such as epilepsy, Alzheimer, sleep problems and so on. This paper presents a new method which extracts and selects features from multi-channel EEG signals. This research focuses on three main points. Firstly, simple random sampling (SRS) technique is used to extract features from the time domain of EEG signals. Secondly, the sequential feature selection (SFS) algorithm is applied to select the key features and to reduce the dimensionality of the data. Finally, the selected features are forwarded to a least square support vector machine (LS_SVM) classifier to classify the EEG signals. The LS_SVM classifier classified the features which are extracted and selected from the SRS and the SFS. The experimental results show that the method achieves 99.90, 99.80 and 100 % for classification accuracy, sensitivity and specificity, respectively.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: Published version made available in accordance with Creative Commons Attribution License 4.0. The proposed method is very efficient for analysing and classifying epileptic EEG signals. It will be also useful for the classification of other biomedical data. Published version made available under open access.
Faculty / Department / School: Current - Faculty of Health, Engineering and Sciences - School of Agricultural, Computational and Environmental Sciences
Date Deposited: 06 Jul 2016 01:42
Last Modified: 06 Jul 2016 04:47
Uncontrolled Keywords: electroencephalogram; epileptic seizures; simple random sampling; sequential feature selection; least square support vector machine
Fields of Research : 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080109 Pattern Recognition and Data Mining
08 Information and Computing Sciences > 0802 Computation Theory and Mathematics > 080201 Analysis of Algorithms and Complexity
Socio-Economic Objective: B Economic Development > 89 Information and Communication Services > 8902 Computer Software and Services > 890205 Information Processing Services (incl. Data Entry and Capture)
C Society > 92 Health > 9205 Specific Population Health (excl. Indigenous Health) > 920599 Specific Population Health (excl. Indigenous Health) not elsewhere classified
Identification Number or DOI: 10.1007/s40708-016-0039-1
URI: http://eprints.usq.edu.au/id/eprint/29014

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