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.

Highlights

  • Remote and continuous/intermittent monitoring (RCIM) has proven to be a promising route to deliver preventative care by reducing both the death rate and burdens placed on the healthcare system [1,2,3]

  • This study showed that the Apple Watch 4 had the highest accuracy with an mean absolute error (MAE) of 2.7 beats per minute (BPM) at rest and 4.6

  • pulse transit time (PTT), Teng and Zhang discovered that, with a contact force of 0.1–0.8 N applied at the finger, the PTT measured from the R peak of an ECG and the peak of the second derivative of the PPG increased significantly (p = 0.014) until the estimated transmural pressure approached

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Summary

Introduction

Remote and continuous/intermittent monitoring (RCIM) has proven to be a promising route to deliver preventative care by reducing both the death rate and burdens placed on the healthcare system [1,2,3]. The systolic peak can be used for heart rate, the dicrotic notch and the area of the curve before and after the notch are used for stroke volume, slope transit time can be used for hypertension, the first derivative parameters are largely used to assess blood velocity, and the five points in the second derivative are used ratiometrically to assess vascular health and risk for cardiovascular disease [22,28,29,30]. True health monitoring should consider the obvious noise sources for commercial fitness devices such as motion artifacts and ambient light, but some of the sources of variability found in diverse patient populations that are prone to cardiovascular disease These often-overlooked disparities with diversity (e.g., skin tone and obesity) are becoming more documented in the literature [34,35,36]. This work could assist in defining the parameters that would be needed for human trials to validate the efficacy of constructed devices across variable populations

Individual Variations in the Human Population
BPM across all skin
Obesity
Gender
Physiology
Respiration
Venous Pulsations
Body Site of Measurement
Local Body Temperature
External Factors
Motion Artifacts
Ambient Light
Applied Pressure to Measurement Site
Findings
Summary
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