Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/42935
Title: Application of Photoplethysmography signals for Healthcare systems: An in-depth review
Contributor(s): Loh, Hui Wen (author); Xu, Shuting (author); Faust, Oliver (author); Ooi, Chui Ping (author); Barua, Prabal Datta (author); Chakraborty, Subrata  (author)orcid ; Tan, Ru-San (author); Molinari, Filippo (author); Acharya, U Rajendra (author)
Publication Date: 2022-04
Early Online Version: 2022-02-01
DOI: 10.1016/j.cmpb.2022.106677
Handle Link: https://hdl.handle.net/1959.11/42935
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.

Publication Type: Journal Article
Source of Publication: Computer Methods and Programs in Biomedicine, v.216, p. 1-14
Publisher: Elsevier Ireland Ltd
Place of Publication: Ireland
ISSN: 1872-7565
0169-2607
Fields of Research (FoR) 2020: 460102 Applications in health
461103 Deep learning
460308 Pattern recognition
Socio-Economic Objective (SEO) 2020: 209999 Other health not elsewhere classified
280115 Expanding knowledge in the information and computing sciences
Peer Reviewed: Yes
HERDC Category Description: C1 Refereed Article in a Scholarly Journal
Appears in Collections:Journal Article
School of Science and Technology

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