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      Sources of Inaccuracy in Photoplethysmography for Continuous Cardiovascular Monitoring

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          Abstract

          Photoplethysmography (PPG) is a low-cost, noninvasive optical technique that uses change in light transmission with changes in blood volume within tissue to provide information for cardiovascular health and fitness. As remote health and wearable medical devices become more prevalent, PPG devices are being developed as part of wearable systems to monitor parameters such as heart rate (HR) that do not require complex analysis of the PPG waveform. However, complex analyses of the PPG waveform yield valuable clinical information, such as: blood pressure, respiratory information, sympathetic nervous system activity, and heart rate variability. Systems aiming to derive such complex parameters do not always account for realistic sources of noise, as testing is performed within controlled parameter spaces. A wearable monitoring tool to be used beyond fitness and heart rate must account for noise sources originating from individual patient variations (e.g., skin tone, obesity, age, and gender), physiology (e.g., respiration, venous pulsation, body site of measurement, and body temperature), and external perturbations of the device itself (e.g., motion artifact, ambient light, and applied pressure to the skin). Here, we present a comprehensive review of the literature that aims to summarize these noise sources for future PPG device development for use in health monitoring.

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          Most cited references215

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          Separate multivariable risk algorithms are commonly used to assess risk of specific atherosclerotic cardiovascular disease (CVD) events, ie, coronary heart disease, cerebrovascular disease, peripheral vascular disease, and heart failure. The present report presents a single multivariable risk function that predicts risk of developing all CVD and of its constituents. We used Cox proportional-hazards regression to evaluate the risk of developing a first CVD event in 8491 Framingham study participants (mean age, 49 years; 4522 women) who attended a routine examination between 30 and 74 years of age and were free of CVD. Sex-specific multivariable risk functions ("general CVD" algorithms) were derived that incorporated age, total and high-density lipoprotein cholesterol, systolic blood pressure, treatment for hypertension, smoking, and diabetes status. We assessed the performance of the general CVD algorithms for predicting individual CVD events (coronary heart disease, stroke, peripheral artery disease, or heart failure). Over 12 years of follow-up, 1174 participants (456 women) developed a first CVD event. All traditional risk factors evaluated predicted CVD risk (multivariable-adjusted P<0.0001). The general CVD algorithm demonstrated good discrimination (C statistic, 0.763 [men] and 0.793 [women]) and calibration. Simple adjustments to the general CVD risk algorithms allowed estimation of the risks of each CVD component. Two simple risk scores are presented, 1 based on all traditional risk factors and the other based on non-laboratory-based predictors. A sex-specific multivariable risk factor algorithm can be conveniently used to assess general CVD risk and risk of individual CVD events (coronary, cerebrovascular, and peripheral arterial disease and heart failure). The estimated absolute CVD event rates can be used to quantify risk and to guide preventive care.
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            Optical properties of biological tissues: a review.

            A review of reported tissue optical properties summarizes the wavelength-dependent behavior of scattering and absorption. Formulae are presented for generating the optical properties of a generic tissue with variable amounts of absorbing chromophores (blood, water, melanin, fat, yellow pigments) and a variable balance between small-scale scatterers and large-scale scatterers in the ultrastructures of cells and tissues.
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              Body Mass Index

              The body mass index (BMI) is the metric currently in use for defining anthropometric height/weight characteristics in adults and for classifying (categorizing) them into groups. The common interpretation is that it represents an index of an individual’s fatness. It also is widely used as a risk factor for the development of or the prevalence of several health issues. In addition, it is widely used in determining public health policies.The BMI has been useful in population-based studies by virtue of its wide acceptance in defining specific categories of body mass as a health issue. However, it is increasingly clear that BMI is a rather poor indicator of percent of body fat. Importantly, the BMI also does not capture information on the mass of fat in different body sites. The latter is related not only to untoward health issues but to social issues as well. Lastly, current evidence indicates there is a wide range of BMIs over which mortality risk is modest, and this is age related. All of these issues are discussed in this brief review.
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                Author and article information

                Journal
                Biosensors (Basel)
                Biosensors (Basel)
                biosensors
                Biosensors
                MDPI
                2079-6374
                16 April 2021
                April 2021
                : 11
                : 4
                : 126
                Affiliations
                [1 ]Department of Biomedical Engineering, Texas A&M University, College Station, TX 77843, USA; jfine@ 123456tamu.edu (J.F.); klb4333@ 123456tamu.edu (K.L.B.)
                [2 ]Department of Biomedical Engineering, Florida International University, Miami, FL 33174, USA; arodr829@ 123456fiu.edu (A.J.R.); tboon007@ 123456fiu.edu (T.B.-a.); aajma003@ 123456fiu.edu (A.); jramella@ 123456fiu.edu (J.C.R.-R.)
                [3 ]Herbert Wertheim College of Medicine, Florida International University, Miami, FL 33199, USA
                [4 ]Department of Materials Science and Engineering, Texas A&M University, College Station, TX 77843, USA
                [5 ]Center for Remote Health Technologies and Systems, Texas A&M Engineering Experimentation Station, Texas A&M University, College Station, TX 77843, USA
                Author notes
                [* ]Correspondence: mcshane@ 123456tamu.edu (M.J.M.); gcote@ 123456tamu.edu (G.L.C.); Tel.: +1-979-845-7941 (M.J.M.); +1-979-458-6082 (G.L.C.)
                Author information
                https://orcid.org/0000-0002-3164-9625
                Article
                biosensors-11-00126
                10.3390/bios11040126
                8073123
                33923469
                0730be66-c862-471a-8287-a7b4c6aa70a5
                © 2021 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( https://creativecommons.org/licenses/by/4.0/).

                History
                : 08 March 2021
                : 09 April 2021
                Categories
                Review

                photoplethysmography,cardiovascular disease,remote health

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