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Evaluation of biases in remote photoplethysmography methods.

ABSTRACT: This work investigates the estimation biases of remote photoplethysmography (rPPG) methods for pulse rate measurement across diverse demographics. Advances in photoplethysmography (PPG) and rPPG methods have enabled the development of contact and noncontact approaches for continuous monitoring and collection of patient health data. The contagious nature of viruses such as COVID-19 warrants noncontact methods for physiological signal estimation. However, these approaches are subject to estimation biases due to variations in environmental conditions and subject demographics. The performance of contact-based wearable sensors has been evaluated, using off-the-shelf devices across demographics. However, the measurement uncertainty of rPPG methods that estimate pulse rate has not been sufficiently tested across diverse demographic populations or environments. Quantifying the efficacy of rPPG methods in real-world conditions is critical in determining their potential viability as health monitoring solutions. Currently, publicly available face datasets accompanied by physiological measurements are typically captured in controlled laboratory settings, lacking diversity in subject skin tones, age, and cultural artifacts (e.g, bindi worn by Indian women). In this study, we collect pulse rate and facial video data from human subjects in India and Sierra Leone, in order to quantify the uncertainty in noncontact pulse rate estimation methods. The video data are used to estimate pulse rate using state-of-the-art rPPG camera-based methods, and compared against ground truth measurements captured using an FDA-approved contact-based pulse rate measurement device. Our study reveals that rPPG methods exhibit similar biases when compared with a contact-based device across demographic groups and environmental conditions. The mean difference between pulse rates measured by rPPG methods and the ground truth is found to be ~2% (1 beats per minute (b.p.m.)), signifying agreement of rPPG methods with the ground truth. We also find that rPPG methods show pulse rate variability of ~15% (11 b.p.m.), as compared to the ground truth. We investigate factors impacting rPPG methods and discuss solutions aimed at mitigating variance.

SUBMITTER: Dasari A

PROVIDER: S-EPMC8175478 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundRemote photoplethysmography (rPPG) can record vital signs (VSs) by detecting subtle changes in the light reflected from the skin. Lifelight (Xim Ltd) is a novel software being developed as a medical device for the contactless measurement of VSs using rPPG via integral cameras on smart devices. Research to date has focused on extracting the pulsatile VS from the raw signal, which can be influenced by factors such as ambient light, skin thickness, facial movements, and skin tone.ObjectiveThis preliminary proof-of-concept study outlines a dynamic approach to rPPG signal processing wherein green channel signals from the most relevant areas of the face (the midface, comprising the cheeks, nose, and top of the lip) are optimized for each subject using tiling and aggregation (T&A) algorithms.MethodsHigh-resolution 60-second videos were recorded during the VISION-MD study. The midface was divided into 62 tiles of 20×20 pixels, and the signals from multiple tiles were evaluated using bespoke algorithms through weighting according to signal-to-noise ratio in the frequency domain (SNR-F) score or segmentation. Midface signals before and after T&A were categorized by a trained observer blinded to the data processing as 0 (high quality, suitable for algorithm training), 1 (suitable for algorithm testing), or 2 (inadequate quality). On secondary analysis, observer categories were compared for signals predicted to improve categories following T&A based on the SNR-F score. Observer ratings and SNR-F scores were also compared before and after T&A for Fitzpatrick skin tones 5 and 6, wherein rPPG is hampered by light absorption by melanin.ResultsThe analysis used 4310 videos recorded from 1315 participants. Category 2 and 1 signals had lower mean SNR-F scores than category 0 signals. T&A improved the mean SNR-F score using all algorithms. Depending on the algorithm, 18% (763/4212) to 31% (1306/4212) of signals improved by at least one category, with up to 10% (438/4212) improving into category 0, and 67% (2834/4212) to 79% (3337/4212) remaining in the same category. Importantly, 9% (396/4212) to 21% (875/4212) improved from category 2 (not usable) into category 1. All algorithms showed improvements. No more than 3% (137/4212) of signals were assigned to a lower-quality category following T&A. On secondary analysis, 62% of signals (32/52) were recategorized, as predicted from the SNR-F score. T&A improved SNR-F scores in darker skin tones; 41% of signals (151/369) improved from category 2 to 1 and 12% (44/369) from category 1 to 0.ConclusionsThe T&A approach to dynamic region of interest selection improved signal quality, including in dark skin tones. The method was verified by comparison with a trained observer's rating. T&A could overcome factors that compromise whole-face rPPG. This method's performance in estimating VS is currently being assessed.Trial registrationClinicalTrials.gov NCT04763746; https://clinicaltrials.gov/ct2/show/NCT04763746.

Project description:BackgroundAtrial fibrillation (AF) is the most common arrhythmia after cardiac surgery, yet the precise incidence and significance of arrhythmias after discharge home need to be better defined. Photoplethysmography (PPG)-based smartphone apps are promising tools to enable early detection and follow-up of arrhythmias.ObjectiveBy using a PPG-based smartphone app, we aimed to gain more insight into the prevalence of AF and other rhythm-related complications upon discharge home after cardiac surgery and evaluate the implementation of this app into routine clinical care.MethodsIn this prospective, single-center trial, patients recovering from cardiac surgery were asked to register their heart rhythm 3 times daily using a Food and Drug Administration-approved PPG-based app, for either 30 or 60 days after discharge home. Patients with permanent AF or a permanent pacemaker were excluded.ResultsWe included 24 patients (mean age 60.2 years, SD 12 years; 15/23, 65% male) who underwent coronary artery bypass grafting and/or valve surgery. During hospitalization, 39% (9/23) experienced postoperative AF. After discharge, the PPG app reported AF or atrial flutter in 5 patients. While the app notified flutter in 1 patient, this was a false positive, as electrocardiogram revealed a 2nd-degree, 2:1 atrioventricular block necessitating a permanent pacemaker. AF was confirmed in 4 patients (4/23, 17%) and interestingly, was associated with an underlying postoperative complication in 2 participants (pneumonia n=1, pericardial tamponade n=1). A significant increase in the proportion of measurements indicating sinus rhythm was observed when comparing the first to the second month of follow-up (P<.001). In the second month of follow-up, compliance was significantly lower with 2.2 (SD 0.7) measurements per day versus 3.0 (SD 0.8) measurements per day in the first month (P=.002). The majority of participants (17/23, 74%), as well as the surveyed primary care physicians, experienced positive value by using the app as they felt more involved in the postoperative rehabilitation.ConclusionsImplementation of smartphone-based PPG technology enables detection of AF and other rhythm-related complications after cardiac surgery. An association between AF detection and an underlying complication was found in 2 patients. Therefore, smartphone-based PPG technology may supplement rehabilitation after cardiac surgery by acting as a sentinel for underlying complications, rhythm-related or otherwise.

Project description:BackgroundContinuous monitoring of vital signs is critical for ensuring patient safety in intensive care units (ICUs) and is becoming increasingly relevant in general wards. The effectiveness of health information technologies such as patient-monitoring systems is highly determined by usability, the lack of which can ultimately compromise patient safety. Usability problems can be identified and prevented by involving users (ie, clinicians).ObjectiveIn this study, we aim to apply a human-centered design approach to evaluate the usability of a remote patient-monitoring system user interface (UI) in the ICU context and conceptualize and evaluate design changes.MethodsFollowing institutional review board approval (EA1/031/18), a formative evaluation of the monitoring UI was performed. Simulated use tests with think-aloud protocols were conducted with ICU staff (n=5), and the resulting qualitative data were analyzed using a deductive analytic approach. On the basis of the identified usability problems, we conceptualized informed design changes and applied them to develop an improved prototype of the monitoring UI. Comparing the UIs, we evaluated perceived usability using the System Usability Scale, performance efficiency with the normative path deviation, and effectiveness by measuring the task completion rate (n=5). Measures were tested for statistical significance using a 2-sample t test, Poisson regression with a generalized linear mixed-effects model, and the N-1 chi-square test. P<.05 were considered significant.ResultsWe found 37 individual usability problems specific to monitoring UI, which could be assigned to six subcodes: usefulness of the system, response time, responsiveness, meaning of labels, function of UI elements, and navigation. Among user ideas and requirements for the UI were high usability, customizability, and the provision of audible alarm notifications. Changes in graphics and design were proposed to allow for better navigation, information retrieval, and spatial orientation. The UI was revised by creating a prototype with a more responsive design and changes regarding labeling and UI elements. Statistical analysis showed that perceived usability improved significantly (System Usability Scale design A: mean 68.5, SD 11.26, n=5; design B: mean 89, SD 4.87, n=5; P=.003), as did performance efficiency (normative path deviation design A: mean 8.8, SD 5.26, n=5; design B: mean 3.2, SD 3.03, n=5; P=.001), and effectiveness (design A: 18 trials, failed 7, 39% times, passed 11, 61% times; design B: 20 trials, failed 0 times, passed 20 times; P=.002).ConclusionsUsability testing with think-aloud protocols led to a patient-monitoring UI with significantly improved usability, performance, and effectiveness. In the ICU work environment, difficult-to-use technology may result in detrimental outcomes for staff and patients. Technical devices should be designed to support efficient and effective work processes. Our results suggest that this can be achieved by applying basic human-centered design methods and principles.Trial registrationClinicalTrials.gov NCT03514173; https://clinicaltrials.gov/ct2/show/NCT03514173.

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Evaluation of biases in remote photoplethysmography methods.

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