Automated Detection of Sleep Stages Using Deep Learning Techniques: A Systematic Review of the Last Decade (2010–2020)

LOH, Hui Wen, OOI, Chui Ping, VICNESH, Jahmunah, OH, Shu Lih, FAUST, Oliver, GERTYCH, Arkadiusz and ACHARYA, U. Rajendra (2020). Automated Detection of Sleep Stages Using Deep Learning Techniques: A Systematic Review of the Last Decade (2010–2020). Applied Sciences, 10 (24), e8963.

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Open Access URL: https://www.mdpi.com/2076-3417/10/24/8963 (Published version)
Link to published version:: https://doi.org/10.3390/app10248963
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    Abstract

    Sleep is vital for one’s general well-being, but it is often neglected, which has led to an increase in sleep disorders worldwide. Indicators of sleep disorders, such as sleep interruptions, extreme daytime drowsiness, or snoring, can be detected with sleep analysis. However, sleep analysis relies on visuals conducted by experts, and is susceptible to inter- and intra-observer variabilities. One way to overcome these limitations is to support experts with a programmed diagnostic tool (PDT) based on artificial intelligence for timely detection of sleep disturbances. Artificial intelligence technology, such as deep learning (DL), ensures that data are fully utilized with low to no information loss during training. This paper provides a comprehensive review of 36 studies, published between March 2013 and August 2020, which employed DL models to analyze overnight polysomnogram (PSG) recordings for the classification of sleep stages. Our analysis shows that more than half of the studies employed convolutional neural networks (CNNs) on electroencephalography (EEG) recordings for sleep stage classification and achieved high performance. Our study also underscores that CNN models, particularly one-dimensional CNN models, are advantageous in yielding higher accuracies for classification. More importantly, we noticed that EEG alone is not sufficient to achieve robust classification results. Future automated detection systems should consider other PSG recordings, such as electroencephalogram (EEG), electrooculogram (EOG), and electromyogram (EMG) signals, along with input from human experts, to achieve the required sleep stage classification robustness. Hence, for DL methods to be fully realized as a practical PDT for sleep stage scoring in clinical applications, inclusion of other PSG recordings, besides EEG recordings, is necessary. In this respect, our report includes methods published in the last decade, underscoring the use of DL models with other PSG recordings, for scoring of sleep stages.

    Item Type: Article
    Additional Information: ** From MDPI via Jisc Publications Router ** Licence for this article: https://creativecommons.org/licenses/by/4.0/ **Journal IDs: eissn 2076-3417 **History: published 15-12-2020; accepted 08-12-2020
    Uncontrolled Keywords: sleep disorder, obstructive sleep disorder, overnight polysomnogram, EEG, EMG, ECG, HRV signals, deep learning
    Identification Number: https://doi.org/10.3390/app10248963
    Page Range: e8963
    SWORD Depositor: Colin Knott
    Depositing User: Colin Knott
    Date Deposited: 21 Dec 2020 16:44
    Last Modified: 22 Dec 2020 12:15
    URI: http://shura.shu.ac.uk/id/eprint/27831

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