A review of automated sleep disorder detection
Article
Article Title | A review of automated sleep disorder detection |
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ERA Journal ID | 5040 |
Article Category | Article |
Authors | Xu, Shuting, Faust, Oliver, Seoni, Silvia, Chakraborty, Subrata, Barua, Prabal Datta, Loh, Hui Wen, Elphick, Heather, Molinari, Filippo and Acharya, U. Rajendra |
Journal Title | Computers in Biology and Medicine |
Journal Citation | 150 |
Article Number | 106100 |
Number of Pages | 20 |
Year | 2022 |
Publisher | Elsevier |
Place of Publication | United Kingdom |
ISSN | 0010-4825 |
1879-0534 | |
Digital Object Identifier (DOI) | https://doi.org/10.1016/j.compbiomed.2022.106100 |
Web Address (URL) | https://www.sciencedirect.com/science/article/pii/S0010482522008083 |
Abstract | Automated sleep disorder detection is challenging because physiological symptoms can vary widely. These variations make it difficult to create effective sleep disorder detection models which support hu-man experts during diagnosis and treatment monitoring. From 2010 to 2021, authors of 95 scientific papers have taken up the challenge of automating sleep disorder detection. This paper provides an expert review of this work. We investigated whether digital technology and Artificial Intelligence (AI) can provide automated diagnosis support for sleep disorders. We followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines during the content discovery phase. We compared the performance of proposed sleep disorder detection methods, involving differ-ent datasets or signals. During the review, we found eight sleep disorders, of which sleep apnea and insomnia were the most studied. These disorders can be diagnosed using several kinds of biomedical signals, such as Electrocardiogram (ECG), Polysomnography (PSG), Electroencephalogram (EEG), Electromyogram (EMG), and snore sound. Subsequently, we established areas of commonality and distinctiveness. Common to all reviewed papers was that AI models were trained and tested with labelled physiological signals. Looking deeper, we discovered that 24 distinct algorithms were used for the detection task. The nature of these algorithms evolved, before 2017 only traditional Machine Learning (ML) was used. From 2018 onward, both ML and Deep Learning (DL) methods were used for sleep disorder detection. The strong emergence of DL algorithms has considerable implications for future detection systems because these algorithms demand significantly more data for training and testing when compared with ML. Based on our review results, we suggest that both type and amount of labelled data is crucial for the design of future sleep disorder detection systems because this will steer the choice of AI algorithm which establishes the desired decision support. As a guiding principle, more labelled data will help to represent the variations in symptoms. DL algorithms can extract information from these larger data quantities more effectively, therefore; we predict that the role of these algorithms will continue to expand. |
Keywords | Automated detection; Biomedical signals processing; Deep learning; Insomnia: artificial intelligence; Machine learning; Sleep apnea; Sleep disorder detection; Sleep disorders |
Contains Sensitive Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 400306. Computational physiology |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | Cogninet Australia, Australia |
Anglia Ruskin University, United Kingdom | |
Polytechnic University of Turin, Italy | |
University of New England | |
University of Technology Sydney | |
School of Business | |
Singapore University of Social Sciences (SUSS), Singapore | |
Sheffield Children’s NHS Foundation Trust, United Kingdom | |
Ngee Ann Polytechnic, Singapore | |
Asia University, Taiwan |
https://research.usq.edu.au/item/yyq24/a-review-of-automated-sleep-disorder-detection
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