Synthetic Photoplethysmography (PPG) of the radial artery through parallelized Monte Carlo and its correlation to Body Mass Index (BMI)

Boonya-Ananta, Tananant; Rodriguez, Antonio B.; Ajmal, Ajmal; Hansen, Anders Kragh; Hutcheson, Joshua D.; Ramella‐Roman, Jessica C.

doi:10.1101/2020.07.22.214015

Cited by 9 publications

(9 citation statements)

References 50 publications

(54 reference statements)

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“…True health monitoring should consider not only 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 now becoming more documented in the literature [ 34 , 35 , 36 ]. Furthermore, 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.…”

Section: Introductionmentioning

confidence: 99%

Sources of Inaccuracy in Photoplethysmography for Continuous Cardiovascular Monitoring

et al. 2021

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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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Section: Introductionmentioning

confidence: 99%

Sources of Inaccuracy in Photoplethysmography for Continuous Cardiovascular Monitoring

et al. 2021

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“…In population calibration, no calibration recordings are required and all calibration is handled using the individual's demographics and a similar training set. Certain dependencies on the morphology of the PPG waveform have been previously reported for age 13 , sex 14 and body mass index (BMI) 15 , however these are often not strong enough to allow for good accuracy when using population calibration strategies. As a result, the majority of studies proposing PPG-based BP estimation opt for a hybrid calibration strategy.…”

Section: Relationship Between Changes In Bp and Changes In The Ppgmentioning

confidence: 99%

Features from the photoplethysmogram and the electrocardiogram for estimating changes in blood pressure

Finnegan

Davidson

Harford

et al. 2022

Preprint

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There is increasing emphasis being placed on cuffless blood pressure (BP) monitoring using the electrocardiogram (ECG) and photoplethysmogram (PPG). These signals have previously been employed to compute estimates of the pulse arrival time (PAT, the time between characteristic fiducial points on the ECG and PPG) as a surrogate for BP. Current work in this field is focused on using either the ECG or the PPG alone as a single source BP estimator using characteristic morphological features and machine learning models. However, the appropriate features and models to use remain unclear. As a result, BP estimation using the ECG or the PPG signals alone has produced inconclusive results. In this work, we investigated the best features available from the PPG and the ECG for BP estimation using both linear and non-linear models. We conducted a clinical study involving 30 healthy volunteers (53.8% female, 28 (± 9) years old, with a body mass index of 22.5± (5.2 kg/m2). Each session lasted 28.0 (± 0.12) minutes and BP was varied by administering an infusion of phenylephrine, a medication that causes arterial and venous vasoconstriction. We extracted a large and diverse set of features from both the PPG and the ECG and assessed their individual importance for estimating changes in BP (∆BP) using a ranking coefficient. In addition to features commonly used in the literature, we propose new features extracted from both signals. We implemented linear (ordinary least squares, OLS) and non-linear (random forest, RF) machine learning models to estimate ∆BP. We adopted a hybrid calibration strategy by including patient demographics in the feature set. We trained, tuned, and evaluated these models in a nested leave-one-subject-out cross-validation framework and we reported the results as correlation coefficient (ρp), root mean squared error (RMSE), and mean absolute error (MAE). We compared our results to those of estimating ∆BP using PAT. The non-linear RF model significantly (p < 0.05) outperformed the linear OLS model using both the PPG and the ECG signals across all performance metrics. Estimating ∆SBP using the PPG alone (ρp = 0.86 (0.23), RMSE = 5.66 (4.76) mmHg, MAE =4.86 (4.29) mmHg) performed significantly better than using the ECG alone (ρp = 0.69 (0.45), RMSE = 6.79 (4.76) mmHg,MAE = 5.28 (4.57) mmHg), all p < 0.001. Estimating ∆BP using features from the PPG alone had a similar performance to that of using PAT (which requires a simultaneous ECG signal). Kurtosis of the PPG waveform showed consistently high feature ranking for both the OLS and RF models. Additionally, the highest ranking features from the PPG largely modelled increasing reflected wave interference driven by changes in arterial stiffness. This finding was supported by changes observed in the PPG waveform in response to the phenylephrine infusion. However, a large number of features were required for accurate BP estimation, highlighting the high complexity of the problem. We conclude that the PPG alone may be further explored as a potential single source, cuffless, blood pressure estimator. The use of the ECG alone is not justified. Non-linear models may perform better as they are able to incorporate interactions between feature values and demographics. However, demographics may not adequately account for the unique and individualised relationship between the extracted features and BP.

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“…Usually, patients with hypertension also have other individual differences, which results in interference of PPG waveforms and decreases the fitting accuracy of the model. These individual differences can be affected by several factors, such as diabetes, arrhythmia, pregnancy, lifestyle, age, gender, body mass index, daily variations, or environmental conditions such as temperature and experimental errors during the data measurement [34][35][36][37][38]. As cardiovascular features and signals vary among conditions and change with time, determining how much the BP estimation varies depending on a factor or in terms of time is important.…”

Section: Introductionmentioning

confidence: 99%

A Data-Driven Model with Feedback Calibration Embedded Blood Pressure Estimator Using Reflective Photoplethysmography

Chen

Huang

Fang

et al. 2022

Sensors

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Ambulatory blood pressure (BP) monitoring (ABPM) is vital for screening cardiovascular activity. The American College of Cardiology/American Heart Association guideline for the prevention, detection, evaluation, and management of BP in adults recommends measuring BP outside the office setting using daytime ABPM. The recommendation to use night–day BP measurements to confirm hypertension is consistent with the recommendation of several other guidelines. In recent studies, ABPM was used to measure BP at regular intervals, and it reduces the effect of the environment on BP. Out-of-office measurements are highly recommended by almost all hypertension organizations. However, traditional ABPM devices based on the oscillometric technique usually interrupt sleep. For all-day ABPM purposes, a photoplethysmography (PPG)-based wrist-type device has been developed as a convenient tool. This optical, noninvasive device estimates BP using morphological characteristics from PPG waveforms. As measurement can be affected by multiple variables, calibration is necessary to ensure that the calculated BP values are accurate. However, few studies focused on adaptive calibration. A novel adaptive calibration model, which is data-driven and embedded in a wearable device, was proposed. The features from a 15 s PPG waveform and personal information were input for estimation of BP values and our data-driven calibration model. The model had a feedback calibration process using the exponential Gaussian process regression method to calibrate BP values and avoid inter- and intra-subject variability, ensuring accuracy in long-term ABPM. The estimation error of BP (ΔBP = actual BP—estimated BP) of systolic BP was −0.1776 ± 4.7361 mmHg; ≤15 mmHg, 99.225%, and of diastolic BP was −0.3846 ± 6.3688 mmHg; ≤15 mmHg, 98.191%. The success rate was improved, and the results corresponded to the Association for the Advancement of Medical Instrumentation standard and British Hypertension Society Grading criteria for medical regulation. Using machine learning with a feedback calibration model could be used to assess ABPM for clinical purposes.

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Synthetic Photoplethysmography (PPG) of the radial artery through parallelized Monte Carlo and its correlation to Body Mass Index (BMI)

Cited by 9 publications

References 50 publications

Sources of Inaccuracy in Photoplethysmography for Continuous Cardiovascular Monitoring

Sources of Inaccuracy in Photoplethysmography for Continuous Cardiovascular Monitoring

Features from the photoplethysmogram and the electrocardiogram for estimating changes in blood pressure

A Data-Driven Model with Feedback Calibration Embedded Blood Pressure Estimator Using Reflective Photoplethysmography

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