Quality Assessment and Morphological Analysis of Photoplethysmography in Daily Life

Abstract

The photoplethysmographic (PPG) signal has been applied in various research fields, with promising results for its future clinical application. However, there are several sources of variability that, if not adequately controlled, can hamper its application in pervasive monitoring contexts. This study assessed and characterized the impact of several sources of variability, such as physical activity, age, sex, and health state on PPG signal quality and PPG waveform parameters (Rise Time, Pulse Amplitude, Pulse Time, Reflection Index, Delta T, and DiastolicAmplitude). We analyzed 31 24 h recordings by as many participants (19 healthy subjects and 12 oncological patients) with a wristband wearable device, selecting a set of PPG pulses labeled with three different quality levels. We implemented a Multinomial Logistic Regression (MLR) model to evaluate the impact of the aforementioned factors on PPG signal quality. We then extracted six parameters only on higher-quality PPG pulses and evaluated the influence of physical activity, age, sex, and health state on these parameters with Generalized Linear Mixed Effects Models (GLMM). We found that physical activity has a detrimental effect on PPG signal quality quality (94% of pulses with good quality when the subject is at rest vs. 9% during intense activity), and that health state affects the percentage of available PPG pulses of the best quality (at rest, 44% for healthy subjects vs. 13% for oncological patients). Most of the extracted parameters are influenced by physical activity and health state, while age significantly impacts two parameters related to arterial stiffness. These results can help expand the awareness that accurate, reliable information extracted from PPG signals can be reached by tackling and modeling different sources of inaccuracy.

Wearable Multisensor Ring-Shaped Probe for Assessing Stress and Blood Oxygenation: Design and Preliminary Measurements

“…As reported, the waveform clearly exhibits all the main fiducial points of a PPG signal, i.e., the systolic foot (representing the beginning of the systolic heart phase), the systolic peak (representing the maximum blood volume during the systolic phase), the dicrotic notch (corresponding to the closure of the aortic valve prevalently seen in the descending phase of young subjects with healthy compliant arteries [ 54 ]), and the diastolic peak (corresponding to both the diastolic heart phase and the peripheral wave reflection). Given that all the fiducial points are visible, the acquired signal can be considered as having a “diagnostic quality” according to Moscato et al [ 55 ], thus allowing a more in-depth morphological analysis if compared to “basic” quality signals.…”

“…The compact design of the device (in terms of its shape, size, and weight) minimizes the space occupied on the body, thus significantly reducing any discomfort experienced by the user. Moreover, the measurements carried out highlighted the high-quality of the acquired signals, with particular regard to the PPG waveform that exhibits all the main fiducial points used in literature, thus paving the way for the exploitation of our system for further morphological analyses [ 55 ].…”

Artificial Intelligence for Automatic Pain Assessment: Research Methods and Perspectives

“…When approaching the study of bio parameters, a number of factors must be taken into account. Cancer pain research and investigation on APA should prioritize examining the underlying pathophysiological mechanisms and evaluating the effectiveness of study models for different types of pain [ 97 ]. Cancer-induced neuropathic pain, for instance, has distinct pathophysiology, and this issue should be carefully addressed during the collection and analysis of physiological signals [ 98 ].…”

The 2023 wearable photoplethysmography roadmap

“…It saves computational resources by excluding low-quality pulses whilst retaining diagnostic quality pulses for analysis. During the daytime, only a small number of pulses can be expected to be of excellent quality and therefore suitable for PWA analysis (Moscato et al 2022). Many factors influence the morphology of pulse waves.…”

“…Indeed, as detailed in section 6 , studies have reported that between 44% and 86% of PPG signals collected are not of sufficient quality for pulse rate monitoring. PPG signal quality has been found to be substantially influenced by physical activity and health status (Moscato et al 2022 ). Therefore, optimal handling of imperfect PPG signals is of ultimate importance to realize the full potential of this ubiquitously available technology.…”