Rules-Based and SVM-Q Methods With Multitapers and Convolution for Sleep EEG Stages Classification
Article
Zapata, Ignacio A., Li, Yan and Wen, Peng. 2022. "Rules-Based and SVM-Q Methods With
Multitapers and Convolution for Sleep EEG Stages Classification." IEEE Access. 10, pp. 71299-71310. https://doi.org/10.1109/ACCESS.2022.3188286
| Article Title | Rules-Based and SVM-Q Methods With |
|---|---|
| ERA Journal ID | 210567 |
| Article Category | Article |
| Authors | Zapata, Ignacio A., Li, Yan (Author) and Wen, Peng (Author) |
| Journal Title | IEEE Access |
| Journal Citation | 10, pp. 71299-71310 |
| Number of Pages | 12 |
| Year | 2022 |
| Publisher | IEEE (Institute of Electrical and Electronics Engineers) |
| Place of Publication | United States |
| ISSN | 2169-3536 |
| Digital Object Identifier (DOI) | https://doi.org/10.1109/ACCESS.2022.3188286 |
| Web Address (URL) | https://ieeexplore.ieee.org/document/9815054 |
| Abstract | Sleep EEG signals analysis is an approach that helps researchers identify and understand the different phenomena concealed within sleep EEG data. This research introduces a time-frequency analysis approach to untangle the parameters of the sleep stages classification from EEG data. This approach computes the spectral estimation of a signal based on a set of controlled wavelets using a multitaper with convolution (MT&C) method. In this study, the MT&C method is implemented to extract the features from a single sleep EEG data channel. Then two separate approaches are applied for sleep stage classification. The first one is based on the EEG waves characteristic definitions of sleep stages (named as Rules-based method) to directly classify each 30-second EEG segment after the feature extraction. The second approach uses a support vector machine with quadratic equation (SVM-Q) classifier to classify the sleep stages based on experts’ scoring. The experimental results are evaluated, and the outcomes show an overall accuracy of 90% with an average sensitivity of 96.2% and an average specificity of 93.2% using an SVM-Q classifier and an 87.6% accuracy for the Rules-based method on healthy subjects. On the other hand, the accuracy on subjects with abnormal sleep EEG data is of 78.1% with the SVM-Q classifier and 73.4% with the Rules- based method. |
| Keywords | Sleep; Electroencephalography; Databases; Feature extraction; Electrooculography; Estimation; Time-frequency analysis; Multitapers; support vector machine; SVM-Q; spectral estimation; sleep EEG; sleep stages; sleep rules; spectra density estimation (SDE) |
| Related Output | |
| Is part of | Multi-Method Approaches for Sleep EEG Analysis And Sleep Stage Classification |
| ANZSRC Field of Research 2020 | 490599. Statistics not elsewhere classified |
| 429999. Other health sciences not elsewhere classified | |
| 400607. Signal processing | |
| 461199. Machine learning not elsewhere classified | |
| Public Notes | This article is part of a UniSQ Thesis by publication. See Related Output. |
| Byline Affiliations | School of Sciences |
| Institution of Origin | University of Southern Queensland |
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Diykh, Mohammed and Li, Yan. 2016. "Fuzzy and non-fuzzy approaches for digital image classification." Journal of Theoretical and Applied Information Technology. 95 (4), pp. 858-870.Discriminating the brain activities for brain–computer interface applications through the optimal allocation-based approach
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Sahi, Aqeel, Lai, David and Li, Yan. 2015. "Parallel encryption mode for probabilistic scheme to secure data in the cloud." 10th International Conference on Information Technology and Applications (ICITA 2015). Sydney, Australia 01 - 04 Jul 2015 Australia.Analysis of alcoholic EEG signals based on horizontal visibility graph entropy
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Wang, Li-li, Zhang, Tao, Yan, Hong-quiang, Peng, Mao, Fang, Zheng-ping, Li, Yan and Wang, Hao. 2014. "Flame-retardant coating by alternate assembly of poly(vinylphosphonic acid) and polyethylenimine for ramie fabrics." Chinese Journal of Polymer Science. 32 (3), pp. 305-314. https://doi.org/10.1007/s10118-014-1408-yStudy on orthogonal basis NN-based storage modelling for Lake Hume of Upper Murray River, Australia
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Siuly, Siuly, Li, Yan and Wen, Peng (Paul). 2014. "Modified CC-LR algorithm with three diverse feature sets for motor imagery tasks classification in EEG based brain–computer interface ." Computer Methods and Programs in Biomedicine. 113 (3), pp. 767-780. https://doi.org/10.1016/j.cmpb.2013.12.020A novel statistical algorithm for multiclass EEG signal classification
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Qu, Jiansheng, Zeng, Jingjing, Li, Yan, Wang, Qin, Maraseni, Tek, Zhang, Lihua, Zhang, Zhiqiang and Clarke-Sather, Abigail. 2013. "Household carbon dioxide emissions from peasants and herdsmen in northwestern arid-alpine regions, China." Energy Policy. 57, pp. 133-140. https://doi.org/10.1016/j.enpol.2012.12.065Model based predictive control of depth of anaesthesia
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