Rules-Based and SVM-Q Methods With Multitapers and Convolution for Sleep EEG Stages Classification
Article
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 |
https://research.usq.edu.au/item/q7wz0/rules-based-and-svm-q-methods-with-multitapers-and-convolution-for-sleep-eeg-stages-classification
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License: CC BY 4.0 | ||
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