Developing a tunable Q-factor wavelet transform based algorithm for epileptic EEG feature extraction

Al Ghayab, Hadi Ratham and Li, Yan and Siuly, Siuly and Abdulla, Shahab and Wen, Paul (2017) Developing a tunable Q-factor wavelet transform based algorithm for epileptic EEG feature extraction. In: 6th International Conference on Health Information Science: Health Information Science (HIS 2017), 7-9 Oct 2017, Moscow, Russian Federation.

Abstract

Brain signals refer to electroencephalogram (EEG) data that contain the most important information in the human brain, which are non-stationary and nonlinear in nature. EEG signals are a mixture of sustained oscillation and non-oscillatory transients that are difficult to deal with by linear methods. This paper proposes a new technique based on a tunable Q-factor wavelet transform (TQWT) and statis-tical method (SM), denoted as TQWT-SM, to analyze epileptic EEG recordings. Firstly, EEG signals are decomposed into different sub-bands by the TWQT method, which is parameterized by its Q-factor and redundancy. This approach depends on the resonance of signals, instead of frequency or scales as the Fourier and wavelet transforms do. Secondly, each type of the sub-band vector is divided into n windows, and 10 statistical features from each window are extracted. Finally all the obtained statistical features are forwarded to a k nearest neighbor (k-NN) classifier to evaluate the performance of the proposed TQWT-SM method. The TQWT-SM features extraction method achieves good experimental results for the seven different epileptic EEG binary-categories by the k-NN classifier, in terms of accuracy (Acc), Matthew’s correlation coefficient (MCC), and F score (F1). The outcomes of the proposed technique can assist the experts to detect epi-leptic seizures.


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Item Type: Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)
Refereed: Yes
Item Status: Live Archive
Additional Information: Permanent restricted access to the Published version, in accordance with the copyright policy of the publisher.
Faculty / Department / School: Current - Faculty of Health, Engineering and Sciences - School of Agricultural, Computational and Environmental Sciences
Date Deposited: 13 Feb 2018 06:03
Last Modified: 19 Sep 2018 03:25
Uncontrolled Keywords: electroencephalography (EEG), tunable Q-factor wavelet transform, statistical method, k nearest neighbour
Fields of Research : 09 Engineering > 0903 Biomedical Engineering > 090399 Biomedical Engineering not elsewhere classified
08 Information and Computing Sciences > 0802 Computation Theory and Mathematics > 080201 Analysis of Algorithms and Complexity
08 Information and Computing Sciences > 0806 Information Systems > 080608 Information Systems Development Methodologies
Socio-Economic Objective: E Expanding Knowledge > 97 Expanding Knowledge > 970108 Expanding Knowledge in the Information and Computing Sciences
Identification Number or DOI: 10.1007/978-3-319-69182-4_6
URI: http://eprints.usq.edu.au/id/eprint/33246

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