Rules-Based and SVM-Q Methods With Multitapers and Convolution for Sleep EEG Stages Classification
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 com...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9815054/ |
| _version_ | 1811225380968726528 |
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| author | Ignacio A. Zapata Yan Li Peng Wen |
| author_facet | Ignacio A. Zapata Yan Li Peng Wen |
| author_sort | Ignacio A. Zapata |
| collection | DOAJ |
| description | 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 methods is implemented to extract the features from a single sleep EEG data channel. Then two separated 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. |
| first_indexed | 2024-04-12T09:06:20Z |
| format | Article |
| id | doaj.art-ac923e4121ad4403838849ac2839b2bd |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-12T09:06:20Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-ac923e4121ad4403838849ac2839b2bd2022-12-22T03:39:06ZengIEEEIEEE Access2169-35362022-01-0110712997131010.1109/ACCESS.2022.31882869815054Rules-Based and SVM-Q Methods With Multitapers and Convolution for Sleep EEG Stages ClassificationIgnacio A. Zapata0https://orcid.org/0000-0002-1237-9113Yan Li1https://orcid.org/0000-0002-4694-4926Peng Wen2School of Sciences, University of Southern Queensland, Toowoomba, QLD, AustraliaSchool of Sciences, University of Southern Queensland, Toowoomba, QLD, AustraliaSchool of Sciences, University of Southern Queensland, Toowoomba, QLD, AustraliaSleep 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 methods is implemented to extract the features from a single sleep EEG data channel. Then two separated 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.https://ieeexplore.ieee.org/document/9815054/Multitaperssupport vector machineSVM-Qspectral estimationsleep EEGsleep stages |
| spellingShingle | Ignacio A. Zapata Yan Li Peng Wen Rules-Based and SVM-Q Methods With Multitapers and Convolution for Sleep EEG Stages Classification IEEE Access Multitapers support vector machine SVM-Q spectral estimation sleep EEG sleep stages |
| title | Rules-Based and SVM-Q Methods With Multitapers and Convolution for Sleep EEG Stages Classification |
| title_full | Rules-Based and SVM-Q Methods With Multitapers and Convolution for Sleep EEG Stages Classification |
| title_fullStr | Rules-Based and SVM-Q Methods With Multitapers and Convolution for Sleep EEG Stages Classification |
| title_full_unstemmed | Rules-Based and SVM-Q Methods With Multitapers and Convolution for Sleep EEG Stages Classification |
| title_short | Rules-Based and SVM-Q Methods With Multitapers and Convolution for Sleep EEG Stages Classification |
| title_sort | rules based and svm q methods with multitapers and convolution for sleep eeg stages classification |
| topic | Multitapers support vector machine SVM-Q spectral estimation sleep EEG sleep stages |
| url | https://ieeexplore.ieee.org/document/9815054/ |
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