Next Article in Journal
Intra-Segment Coordination Variability in Road Cyclists during Pedaling at Different Intensities
Next Article in Special Issue
Comorbidity Pattern Analysis for Predicting Amyotrophic Lateral Sclerosis
Previous Article in Journal
Hydrogen Storage in Propane-Hydrate: Theoretical and Experimental Study
Previous Article in Special Issue
Intracranial Hemorrhage Detection in Head CT Using Double-Branch Convolutional Neural Network, Support Vector Machine, and Random Forest
Review

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

1
School of Science and Technology, Singapore University of Social Sciences, Singapore 599494, Singapore
2
School of Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore
3
Department of Engineering and Mathematics, Sheffield Hallam University, Sheffield S1 1WB, UK
4
Department of Surgery, Department of Pathology and Laboratory Medicine, Cedars-Sinai Medical Center, Los Angeles, CA 90040, USA
5
Faculty of Biomedical Engineering, Silesian University of Technology, Roosevelta 40, 41-800 Zabrze, Poland
6
Department of Bioinformatics and Medical Engineering, Asia University, Taichung 41354, Taiwan
7
International Research Organization for Advanced Science and Technology (IROAST), Kumamoto University, Kumamoto 860-8555, Japan
8
School of Management and Enterprise, University of Southern Queensland, Darling Heights, QLD 4350, Australia
*
Authors to whom correspondence should be addressed.
Appl. Sci. 2020, 10(24), 8963; https://doi.org/10.3390/app10248963
Received: 3 November 2020 / Revised: 8 December 2020 / Accepted: 8 December 2020 / Published: 15 December 2020
(This article belongs to the Special Issue Machine Learning for Biomedical Application)
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. View Full-Text
Keywords: sleep disorder; obstructive sleep disorder; overnight polysomnogram; EEG; EMG; ECG; HRV signals; deep learning sleep disorder; obstructive sleep disorder; overnight polysomnogram; EEG; EMG; ECG; HRV signals; deep learning
Show Figures

Figure 1

MDPI and ACS Style

Loh, H.W.; Ooi, C.P.; Vicnesh, J.; Oh, S.L.; Faust, O.; Gertych, A.; Acharya, U.R. Automated Detection of Sleep Stages Using Deep Learning Techniques: A Systematic Review of the Last Decade (2010–2020). Appl. Sci. 2020, 10, 8963. https://doi.org/10.3390/app10248963

AMA Style

Loh HW, Ooi CP, Vicnesh J, Oh SL, Faust O, Gertych A, Acharya UR. Automated Detection of Sleep Stages Using Deep Learning Techniques: A Systematic Review of the Last Decade (2010–2020). Applied Sciences. 2020; 10(24):8963. https://doi.org/10.3390/app10248963

Chicago/Turabian Style

Loh, Hui W., Chui P. Ooi, Jahmunah Vicnesh, Shu L. Oh, Oliver Faust, Arkadiusz Gertych, and U. R. Acharya 2020. "Automated Detection of Sleep Stages Using Deep Learning Techniques: A Systematic Review of the Last Decade (2010–2020)" Applied Sciences 10, no. 24: 8963. https://doi.org/10.3390/app10248963

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop