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DOI 10.1016/j.cmpb.2022.106677

Медицинская библиотека · Computer methods and programs in biomedicine · 2022

Application of photoplethysmography signals for healthcare systems: An in-depth review.

Loh Hui Wen, Xu Shuting, Faust Oliver, Ooi Chui Ping, Barua Prabal Datta, Chakraborty Subrata, Tan Ru-San, Molinari Filippo, Acharya U Rajendra

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Аннотация

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.

Для цитирования: Loh Hui Wen, Xu Shuting, Faust Oliver, Ooi Chui Ping, Barua Prabal Datta, Chakraborty Subrata, Tan Ru-San, Molinari Filippo, Acharya U Rajendra. Application of photoplethysmography signals for healthcare systems: An in-depth review.. Computer methods and programs in biomedicine. 2022. DOI: 10.1016/j.cmpb.2022.106677.

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