Application of photoplethysmography signals for healthcare systems: An in-depth review
Article
Article Title | Application of photoplethysmography signals for healthcare systems: An in-depth review |
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ERA Journal ID | 5039 |
Article Category | Article |
Authors | Loh, Hui Wen, Xu, Shuting, Faust, Oliver, Ooi, Chui Ping, Barua, Prabal Datta, Chakraborty, Subrata, Tan, Ru-San, Molinari, Filippo and Acharya, U. Rajendra |
Journal Title | Computer Methods and Programs in Biomedicine |
Journal Citation | 216, pp. 1-14 |
Article Number | 106677 |
Number of Pages | 14 |
Year | 2022 |
Publisher | Elsevier |
Place of Publication | Ireland |
ISSN | 0169-2607 |
1872-7565 | |
Digital Object Identifier (DOI) | https://doi.org/10.1016/j.cmpb.2022.106677 |
Web Address (URL) | https://www.sciencedirect.com/science/article/pii/S0169260722000621 |
Abstract | Background and objectives: Photoplethysmography (PPG) is a device that measures the amount of light absorbed by the blood vessel, blood, and tissues, which can, in turn, translate into various measurements such as the variation in blood flow volume, heart rate variability, blood pressure, etc. Hence, PPG signals can produce a wide variety of biological information that can be useful for the detection and diagnosis of various health problems. In this review, we are interested in the possible health disorders that can be detected using PPG signals. Methods: We applied PRISMA guidelines to systematically search various journal databases and identified 43 PPG studies that fit the criteria of this review. Results: Twenty-five health issues were identified from these studies that were classified into six categories: cardiac, blood pressure, sleep health, mental health, diabetes, and miscellaneous. Various routes were employed in these PPG studies to perform the diagnosis: machine learning, deep learning, and statistical routes. The studies were reviewed and summarized. Conclusions: We identified limitations such as poor standardization of sampling frequencies and lack of publicly available PPG databases. We urge that future work should consider creating more publicly available databases so that a wide spectrum of health problems can be covered. We also want to promote the use of PPG signals as a potential precision medicine tool in both ambulatory and hospital settings. |
Keywords | Blood pressure; Cardiac; Computer-aided diagnosis (CAD); Deep learning; Diabetes; Machine learning; Mental health; Photoplethysmography (PPG); PRISMA; Sleep |
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 | Singapore University of Social Sciences (SUSS), Singapore |
Cogninet Australia, Australia | |
University of Technology Sydney | |
Sheffield Hallam University, United Kingdom | |
School of Business | |
University of New England | |
National Heart Centre, Singapore | |
Duke-NUS Medical School, Singapore | |
Polytechnic University of Turin, Italy | |
Ngee Ann Polytechnic, Singapore | |
Asia University, Taiwan | |
Kumamoto University, Japan |
https://research.usq.edu.au/item/yyw6q/application-of-photoplethysmography-signals-for-healthcare-systems-an-in-depth-review
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