Developing a tunable Q-factor wavelet transform based algorithm for epileptic EEG feature extraction
Paper
Paper/Presentation Title | Developing a tunable Q-factor wavelet transform based algorithm for epileptic EEG feature extraction |
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Presentation Type | Paper |
Authors | Al Ghayab, Hadi Ratham (Author), Li, Yan (Author), Siuly, Siuly (Author), Abdulla, Shahab (Author) and Wen, Paul (Author) |
Editors | Siuly, Siuly, Huang, Zhisheng, Aickelin, Uwe, Zhou, Rui, Wang, Hua, Zhang, Yanchun and Klimenko, Stanislav |
Journal or Proceedings Title | Proceedings of the 6th International Conference on Health Information Science (HIS 2017) |
Journal Citation | 10594, pp. 45-55 |
Number of Pages | 11 |
Year | 2017 |
Place of Publication | Germany |
ISBN | 9783319691817 |
9783319691824 | |
Digital Object Identifier (DOI) | https://doi.org/10.1007/978-3-319-69182-4_6 |
Web Address (URL) of Paper | https://link.springer.com/chapter/10.1007/978-3-319-69182-4_6 |
Web Address (URL) of Conference Proceedings | https://link.springer.com/book/10.1007/978-3-319-69182-4 |
Conference/Event | 6th International Conference on Health Information Science (HIS 2017) |
Event Details | 6th International Conference on Health Information Science (HIS 2017) Parent International Conference on Health Information Science (HIS) Delivery In person Event Date 07 to end of 09 Oct 2017 Event Location 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. |
Keywords | electroencephalography (EEG), tunable Q-factor wavelet transform, statistical method, k nearest neighbour |
ANZSRC Field of Research 2020 | 400399. Biomedical engineering not elsewhere classified |
461399. Theory of computation not elsewhere classified | |
460905. Information systems development methodologies and practice | |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Series | Lecture Notes in Computer Science |
Byline Affiliations | Faculty of Health, Engineering and Sciences |
Department of Mathematics and Computing | |
Victoria University | |
Open Access College | |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q473y/developing-a-tunable-q-factor-wavelet-transform-based-algorithm-for-epileptic-eeg-feature-extraction
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