Sources of Inaccuracy in Photoplethysmography for Continuous Cardiovascular Monitoring

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.

Tourist Experiences Recommender System Based on Emotion Recognition with Wearable Data

“…Wearables have entered the market in great numbers in recent years [ 9 , 26 ], as have increasingly incorporate sensors that measure physiological signals. The most common sensor present in this type of device is the Photoplethysmogram (PPG) [ 12 , 27 , 28 , 29 ], which registers HR signals. The configuration of these devices has aroused scientific interest in detecting the emotional state of a person from these signals [ 12 , 13 , 30 ].…”

“…The configuration of these devices has aroused scientific interest in detecting the emotional state of a person from these signals [ 12 , 13 , 30 ]. However, not all wearables have the same level of accuracy in getting the physiological signals [ 29 ]. There are devices ranges [ 10 , 26 ]: Expensive devices with high accuracy sensors, principally used for healthcare purposes, for instance, include the Emphatica E4 [ 11 , 13 , 28 , 31 ].…”

Monitoring Non-dipping Heart Rate by Consumer-Grade Wrist-Worn Devices: An Avenue for Cardiovascular Risk Assessment in Hypertension

“…Nonetheless, PPG comes with various sources of noise that may interfere with HR measurement if left unmitigated by post-sensing processing via algorithms and data analytics. Such sources of noise can be primarily divided into three categories: (i) individual variation, e.g., skin tone, obesity, age, and gender; (ii) physiology, e.g., respiratory rate, venous pulsation, and local body temperature; and (iii) external factors, e.g., motion artifacts, ambient light, and pressure applied to the skin (34). Of note, in validation studies, wrist-worn devices using PPG for HR measurement achieved high agreement with standard ECG, or chest-strap telemetry, indicative of minimal interference by noise or other artifacts (22).…”

An Adaptive Heart Rate Monitoring Algorithm for Wearable Healthcare Devices

“…The peak tracking stage contained the peak detection and tracking operations. Sources of inaccuracy in wearable optical heart rate sensors were further investigated in [21,22].…”