Developing a tunable Q-factor wavelet transform based algorithm for epileptic EEG feature extraction
Paper
Al Ghayab, Hadi Ratham, Li, Yan, Siuly, Siuly, Abdulla, Shahab and Wen, Paul. 2017. "Developing a tunable Q-factor wavelet transform based algorithm for epileptic EEG feature extraction." Siuly, Siuly, Huang, Zhisheng, Aickelin, Uwe, Zhou, Rui, Wang, Hua, Zhang, Yanchun and Klimenko, Stanislav (ed.) 6th International Conference on Health Information Science: Health Information Science (HIS 2017). Moscow, Russian Federation 07 - 09 Oct 2017 Germany. https://doi.org/10.1007/978-3-319-69182-4_6
| Paper/Presentation Title | Developing a tunable Q-factor wavelet transform based algorithm for epileptic EEG feature extraction |
|---|---|
| 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: Health Information Science (HIS 2017) |
| ERA Conference ID | 73271 |
| 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%2F978-3-319-69182-4_6#citeas |
| Conference/Event | 6th International Conference on Health Information Science: Health Information Science (HIS 2017) |
| International Conference on Health Information Science (HIS) | |
| Event Details | 6th International Conference on Health Information Science: Health Information Science (HIS 2017) Event Date 07 to end of 09 Oct 2017 Event Location Moscow, Russian Federation |
| Event Details | International Conference on Health Information Science (HIS) HIS |
| 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. |
| 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 |
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Al‐Hadeethi, Ikhlas, Li, Yan, Odhafa, Abdul Kareem H., Seneweera, Saman and Lam, Shu K.. 2019. "Assessment of grain quality in terms of functional group response to elevated [CO2], water, and nitrogen using a meta‐analysis: grain protein, zinc, and iron under future climate." Ecology and Evolution. 9 (13), pp. 7425-7437. https://doi.org/10.1002/ece3.5210K-complexes Detection in EEG Signals using Fractal and Frequency Features Coupled with an Ensemble Classification Model
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Zhang, Ji, Lafta, Raid, Tao, Xiaohui, Li, Yan, Chen, Fulong, Luo, Yonglong and Zhu, Xiaodong. 2017. "Coupling a fast fourier transformation with a machine learning ensemble model to support recommendations for heart disease patients in a telehealth environment." IEEE Access. 5 (1), pp. 10674-10685. https://doi.org/10.1109/ACCESS.2017.2706318Input selection and performance optimization of ANN-based streamflow forecasts in the drought-prone Murray Darling Basin region using IIS and MODWT algorithm
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Palendeng, Mario Elvis, Wen, Peng and Li, Yan. 2014. "Real-time depth of anaesthesia assessment using strong analytical signal transform technique." Australasian Physical and Engineering Sciences in Medicine. 37 (4), pp. 723-730. https://doi.org/10.1007/s13246-014-0309-2Designing a robust feature extraction method based on optimum allocation and principal component analysis for epileptic EEG signal classification
Siuly, Siuly, Li, Yan, Siuly, Siuly and Li, Yan. 2015. "Designing a robust feature extraction method based on optimum allocation and principal component analysis for epileptic EEG signal classification." Computer Methods and Programs in Biomedicine. 119 (1), pp. 29-42. https://doi.org/10.1016/j.cmpb.2015.01.002Anaesthetic EEG signal denoise using improved nonlocal mean methods
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Wandel, Andrew P., Robinson, Clare, Abdulla, Shahab, Dalby, Tim, Frederiks, Anita and Galligan, Linda. 2015. "Students' mathematical preparation: differences in staff and student perceptions." International Journal of Innovation in Science and Mathematics Education. 23 (1), pp. 82-93.Analyzing EEG signals using graph entropy based principle component analysis and J48 decision tree
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Li, Yan and Rad, A. B.. 1997. "A cascading structure and training method for multilayer neural network." International Journal of Neural Systems. 8 (5/6), pp. 509-515. https://doi.org/10.1142/S0129065797000495The value and cost of complexity in predictive modelling: role of tissue anisotropic conductivity and fibre tracts in neuromodulation
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Nguyen-Ky, Tai, Wen, Peng, Li, Yan and Malan, Mel. 2012. "Measuring the hypnotic depth of anaesthesia based on the EEG signal using combined wavelet transform, eigenvector and normalisation techniques." Computers in Biology and Medicine. 42 (6), pp. 680-691. https://doi.org/10.1016/j.compbiomed.2012.03.004Model based predictive control of depth of anaesthesia
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Bashar, Md. Rezaul, Li, Yan and Wen, Peng. 2008. "A study of white matter and skull inhomogeneous anisotropic tissue conductivities on EEG forward head modeling." Karim, Mohammad (ed.) DMAI 2008: 1st IEEE International Workshop on Data Mining and Artificial Intelligence. Khulna, Bangladesh 24 - 27 Dec 2008 Bangladesh. https://doi.org/10.1109/ICCITECHN.2008.4803103Tension control of a winding machine for rectangular coils
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Ma, Guangzhi, Lu, Yansheng, Wen, Peng and Song, Engmin. 2010. "A novel attribute reduction algorithm based on peer-to-peer technique and rough set theory." Li, Yan, Yang, Jiajia, Wen, Peng and Wu, Jinglong (ed.) 2010 IEEE/ICME International Conference on Complex Medical Engineering (ICME 2010). Gold Coast, Australia 13 - 15 Jul 2010 Brisbane, Australia. https://doi.org/10.1109/ICCME.2010.5558832Fruit drying process: analysis, modeling and simulation
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