Epileptic seizures detection in EEGs blending frequency domain with information gain technique

Al Ghayab, Hadi Ratham and Li, Yan and Siuly, Siuly and Abdulla, Shahab (2019) Epileptic seizures detection in EEGs blending frequency domain with information gain technique. Soft Computing, 23 (1). pp. 227-239. ISSN 1432-7643

Abstract

This paper proposes a new algorithm which combines the information in frequency domain with the Information Gain (InfoGain) technique for the detection of epileptic seizures from electroencephalogram (EEG) data. The proposed method consists of four main steps. Firstly, in order to investigate which method is most suitable to decompose the EEG signals into frequency bands, we implement separately a fast Fourier transform (FFT) or discrete wavelet transform (DWT). Secondly, each band is partitioned into k windows and a set of statistical features are extracted from each window. Thirdly, the InfoGain is used to rank the extracted features and the most important ones are selected. Lastly, these features are forwarded to a least square support vector machine (LS-SVM) classifier to classify the EEG. This scheme is implemented and tested on a benchmark EEG database and also compared with other existing methods, based on some performance evaluation measures. The experimental results show that the proposed FFT combined with InfoGain method can generate better performance than the DWT method. This method achieves 100% accuracy for five different pairs: healthy people with eyes open (z) versus epileptic patients with activity seizures (s); healthy people with eyes closed (o) versus s; epileptic patients with free seizures (n) versus s; patients with free seizures epileptic (f) versus s; and z versus o. The accuracies obtained for two other pairs, (o vs. n) and (z vs. f), are 95.62 and 88.32%, respectively. These two pairs have more similarities with each other, leading to a lower level of accuracy. The proposed approach outperforms six other reported methods and achieves an 11.9% improvement. Finally, it can be concluded that the proposed FFT combined with InfoGain method has the capacity to detect epileptic seizures in EEG most effectively.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: Files associated with this item cannot be displayed due to copyright restrictions.
Faculty / Department / School: Current - Faculty of Health, Engineering and Sciences - School of Agricultural, Computational and Environmental Sciences
Date Deposited: 25 Jan 2019 02:47
Last Modified: 25 Jan 2019 06:08
Uncontrolled Keywords: electroencephalogram; epileptic seizures; frequency domain; information gain technique; 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 > 0801 Artificial Intelligence and Image Processing > 080199 Artificial Intelligence and Image Processing not elsewhere classified
09 Engineering > 0903 Biomedical Engineering > 090399 Biomedical Engineering not elsewhere classified
08 Information and Computing Sciences > 0803 Computer Software > 080301 Bioinformatics Software
08 Information and Computing Sciences > 0802 Computation Theory and Mathematics > 080201 Analysis of Algorithms and Complexity
Socio-Economic Objective: E Expanding Knowledge > 97 Expanding Knowledge > 970111 Expanding Knowledge in the Medical and Health Sciences
Identification Number or DOI: 10.1007/s00500-018-3487-0
URI: http://eprints.usq.edu.au/id/eprint/34758

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