Computerized detection of cyclic alternating patterns of sleep: A new paradigm, future scope and challenges
Article
Article Title | Computerized detection of cyclic alternating patterns of sleep: A new paradigm, future scope and challenges |
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ERA Journal ID | 5039 |
Article Category | Article |
Authors | Sharma, Manish, Lodhi, Harsh, Yadav, Rishita, Elphick, Heather and Acharya, U. Rajendra |
Journal Title | Computer Methods and Programs in Biomedicine |
Journal Citation | 235 |
Article Number | 107471 |
Number of Pages | 16 |
Year | 2023 |
Publisher | Elsevier |
Place of Publication | Ireland |
ISSN | 0169-2607 |
1872-7565 | |
Digital Object Identifier (DOI) | https://doi.org/10.1016/j.cmpb.2023.107471 |
Web Address (URL) | https://www.sciencedirect.com/science/article/pii/S0169260723001372 |
Abstract | Background and objectives: Sleep quality is associated with wellness, and its assessment can help diagnose several disorders and diseases. Sleep analysis is commonly performed based on self-rating indices, sleep duration, environmental factors, physiologically and polysomnographic-derived parameters, and the occurrence of disorders. However, the correlation that has been observed between the subjective assessment and objective measurements of sleep quality is small. Recently, a few automated systems have been suugested to measure sleep quality to address this challenge. Sleep quality can be assessed by evaluating macrostructure-based sleep analysis via the examination of sleep cycles, namely Rapid Eye Movement (REM) and Non Rapid Eye Movement (NREM) with N1, N2, and N3 stages. However, macrostructure sleep analysis does not consider transitory phenomena like Methods: This literature survey examined the automated assessment of CAP and related parameters. We have reviewed 34 research articles, including fourteen ML, nine DL, and ten based on some other techniques. Results: The review includes various algorithms, databases, features, classifiers, and classification performances and their comparisons, advantages, and limitations of automated systems for CAP assessment. Conclusion: A detailed description of state-of-the-art research findings on automated CAP assessment and associated challenges has been presented. Also, the research gaps have been identified based on our review. Further, future research directions are suggested for sleep quality assessment using CAP. |
Keywords | A and B phase; A phase; A subphases; Automatic detection; Cyclic alternating patterns (CAP); Machine learning; Deep learning |
ANZSRC Field of Research 2020 | 400306. Computational physiology |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | Institute of Infrastructure, Technology, Research and Management (IITRAM), India |
Sheffield Children’s NHS Foundation Trust, United Kingdom | |
Asia University, Taiwan | |
School of Mathematics, Physics and Computing | |
National University of Singapore |
https://research.usq.edu.au/item/z1w13/computerized-detection-of-cyclic-alternating-patterns-of-sleep-a-new-paradigm-future-scope-and-challenges
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