Machine-Learning-Based-Approaches for Sleep Stage Classification Utilising a Combination of Physiological Signals: A Systematic Review
Increasingly prevalent sleep disorders worldwide significantly affect the well-being of individuals. Sleep disorder can be detected by dividing sleep into different stages. Hence, the accurate classification of sleep stages is crucial for detecting sleep disorders. The use of machine learning techni...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-12-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/24/13280 |
| _version_ | 1797382122719150080 |
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| author | Haifa Almutairi Ghulam Mubashar Hassan Amitava Datta |
| author_facet | Haifa Almutairi Ghulam Mubashar Hassan Amitava Datta |
| author_sort | Haifa Almutairi |
| collection | DOAJ |
| description | Increasingly prevalent sleep disorders worldwide significantly affect the well-being of individuals. Sleep disorder can be detected by dividing sleep into different stages. Hence, the accurate classification of sleep stages is crucial for detecting sleep disorders. The use of machine learning techniques on physiological signals has shown promising results in the automatic classification of sleep stages. The integration of information from multichannel physiological signals has shown to further enhance the accuracy of such classification. Existing literature reviews focus on studies utilising a single channel of EEG signals for sleep stage classification. However, other review studies focus on models developed for sleep stage classification, utilising either a single channel of physiological signals or a combination of various physiological signals. This review focuses on the classification of sleep stages through the integration of combined multichannel physiological signals and machine learning methods. We conducted a comprehensive review spanning from the year 2000 to 2023, aiming to provide a thorough and up-to-date resource for researchers in the field. We analysed approximately 38 papers investigating sleep stage classification employing various machine learning techniques integrated with combined signals. In this study, we describe the models proposed in the existing literature for sleep stage classification, discuss their limitations, and identify potential areas for future research. |
| first_indexed | 2024-03-08T21:01:56Z |
| format | Article |
| id | doaj.art-bd824110b219494a8e0243d9d49fb7bb |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-08T21:01:56Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-bd824110b219494a8e0243d9d49fb7bb2023-12-22T13:52:06ZengMDPI AGApplied Sciences2076-34172023-12-0113241328010.3390/app132413280Machine-Learning-Based-Approaches for Sleep Stage Classification Utilising a Combination of Physiological Signals: A Systematic ReviewHaifa Almutairi0Ghulam Mubashar Hassan1Amitava Datta2Department of Computer Science & Software Engineering, The University of Western Australia, Crawley, WA 6009, AustraliaDepartment of Computer Science & Software Engineering, The University of Western Australia, Crawley, WA 6009, AustraliaDepartment of Computer Science & Software Engineering, The University of Western Australia, Crawley, WA 6009, AustraliaIncreasingly prevalent sleep disorders worldwide significantly affect the well-being of individuals. Sleep disorder can be detected by dividing sleep into different stages. Hence, the accurate classification of sleep stages is crucial for detecting sleep disorders. The use of machine learning techniques on physiological signals has shown promising results in the automatic classification of sleep stages. The integration of information from multichannel physiological signals has shown to further enhance the accuracy of such classification. Existing literature reviews focus on studies utilising a single channel of EEG signals for sleep stage classification. However, other review studies focus on models developed for sleep stage classification, utilising either a single channel of physiological signals or a combination of various physiological signals. This review focuses on the classification of sleep stages through the integration of combined multichannel physiological signals and machine learning methods. We conducted a comprehensive review spanning from the year 2000 to 2023, aiming to provide a thorough and up-to-date resource for researchers in the field. We analysed approximately 38 papers investigating sleep stage classification employing various machine learning techniques integrated with combined signals. In this study, we describe the models proposed in the existing literature for sleep stage classification, discuss their limitations, and identify potential areas for future research.https://www.mdpi.com/2076-3417/13/24/13280classificationEEGEMGEOGdeep learningmachine learning |
| spellingShingle | Haifa Almutairi Ghulam Mubashar Hassan Amitava Datta Machine-Learning-Based-Approaches for Sleep Stage Classification Utilising a Combination of Physiological Signals: A Systematic Review Applied Sciences classification EEG EMG EOG deep learning machine learning |
| title | Machine-Learning-Based-Approaches for Sleep Stage Classification Utilising a Combination of Physiological Signals: A Systematic Review |
| title_full | Machine-Learning-Based-Approaches for Sleep Stage Classification Utilising a Combination of Physiological Signals: A Systematic Review |
| title_fullStr | Machine-Learning-Based-Approaches for Sleep Stage Classification Utilising a Combination of Physiological Signals: A Systematic Review |
| title_full_unstemmed | Machine-Learning-Based-Approaches for Sleep Stage Classification Utilising a Combination of Physiological Signals: A Systematic Review |
| title_short | Machine-Learning-Based-Approaches for Sleep Stage Classification Utilising a Combination of Physiological Signals: A Systematic Review |
| title_sort | machine learning based approaches for sleep stage classification utilising a combination of physiological signals a systematic review |
| topic | classification EEG EMG EOG deep learning machine learning |
| url | https://www.mdpi.com/2076-3417/13/24/13280 |
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