Epileptic seizures detection in EEGs blending frequency domain with information gain technique
Article
Article Title | Epileptic seizures detection in EEGs blending frequency domain with information gain technique |
---|---|
ERA Journal ID | 36486 |
Article Category | Article |
Authors | Al Ghayab, Hadi Ratham (Author), Li, Yan (Author), Siuly, Siuly (Author) and Abdulla, Shahab (Author) |
Journal Title | Soft Computing |
Journal Citation | 23 (1), pp. 227-239 |
Number of Pages | 13 |
Year | 2019 |
Publisher | Springer |
Place of Publication | Germany |
ISSN | 1432-7643 |
1433-7479 | |
Digital Object Identifier (DOI) | https://doi.org/10.1007/s00500-018-3487-0 |
Web Address (URL) | https://link.springer.com/article/10.1007/s00500-018-3487-0 |
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. |
Keywords | electroencephalogram; epileptic seizures; frequency domain; information gain technique; least square support vector machine |
ANZSRC Field of Research 2020 | 469999. Other information and computing sciences not elsewhere classified |
400399. Biomedical engineering not elsewhere classified | |
460103. Applications in life sciences | |
461399. Theory of computation not elsewhere classified | |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | Faculty of Health, Engineering and Sciences |
Victoria University | |
Open Access College | |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q4x7x/epileptic-seizures-detection-in-eegs-blending-frequency-domain-with-information-gain-technique
256
total views9
total downloads2
views this month0
downloads this month