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Article

A Computational Modeling and Simulation Workflow to Investigate the Impact of Patient-Specific and Device Factors on Hemodynamic Measurements from Non-Invasive Photoplethysmography

by
Jesse Fine
1,
Michael J. McShane
1,2,3,
Gerard L. Coté
1,2,* and
Christopher G. Scully
4
1
Department of Biomedical Engineering, Texas A&M University, College Station, TX 77843, USA
2
Center for Remote Health Technologies and Systems, Texas A&M Engineering Experiment Station, Texas A&M University, College Station, TX 77843, USA
3
Department of Materials Science and Engineering, Texas A&M University, College Station, TX 77843, USA
4
Office of Science and Engineering Laboratories, Division of Biomedical Physics, Center for Devices and Radiological Health, Food and Drug Administration, Silver Spring, MD 20993, USA
*
Author to whom correspondence should be addressed.
Biosensors 2022, 12(8), 598; https://doi.org/10.3390/bios12080598
Submission received: 6 June 2022 / Revised: 16 July 2022 / Accepted: 27 July 2022 / Published: 4 August 2022

Abstract

Cardiovascular disease is the leading cause of death globally. To provide continuous monitoring of blood pressure (BP), a parameter which has shown to improve health outcomes when monitored closely, many groups are trying to measure blood pressure via noninvasive photoplethysmography (PPG). However, the PPG waveform is subject to variation as a function of patient-specific and device factors and thus a platform to enable the evaluation of these factors on the PPG waveform and subsequent hemodynamic parameter prediction would enable device development. Here, we present a computational workflow that combines Monte Carlo modeling (MC), gaussian combination, and additive noise to create synthetic dataset of volar fingertip PPG waveforms representative of a diverse cohort. First, MC is used to determine PPG amplitude across age, skin tone, and device wavelength. Then, gaussian combination generates accurate PPG waveforms, and signal processing enables data filtration and feature extraction. We improve the limitations of current synthetic PPG frameworks by enabling inclusion of physiological and anatomical effects from body site, skin tone, and age. We then show how the datasets can be used to examine effects of device characteristics such as wavelength, analog to digital converter specifications, filtering method, and feature extraction. Lastly, we demonstrate the use of this framework to show the insensitivity of a support vector machine predictive algorithm compared to a neural network and bagged trees algorithm.
Keywords: photoplethysmography; remote monitoring; computational modeling and simulation; medical device design photoplethysmography; remote monitoring; computational modeling and simulation; medical device design

Share and Cite

MDPI and ACS Style

Fine, J.; McShane, M.J.; Coté, G.L.; Scully, C.G. A Computational Modeling and Simulation Workflow to Investigate the Impact of Patient-Specific and Device Factors on Hemodynamic Measurements from Non-Invasive Photoplethysmography. Biosensors 2022, 12, 598. https://doi.org/10.3390/bios12080598

AMA Style

Fine J, McShane MJ, Coté GL, Scully CG. A Computational Modeling and Simulation Workflow to Investigate the Impact of Patient-Specific and Device Factors on Hemodynamic Measurements from Non-Invasive Photoplethysmography. Biosensors. 2022; 12(8):598. https://doi.org/10.3390/bios12080598

Chicago/Turabian Style

Fine, Jesse, Michael J. McShane, Gerard L. Coté, and Christopher G. Scully. 2022. "A Computational Modeling and Simulation Workflow to Investigate the Impact of Patient-Specific and Device Factors on Hemodynamic Measurements from Non-Invasive Photoplethysmography" Biosensors 12, no. 8: 598. https://doi.org/10.3390/bios12080598

APA Style

Fine, J., McShane, M. J., Coté, G. L., & Scully, C. G. (2022). A Computational Modeling and Simulation Workflow to Investigate the Impact of Patient-Specific and Device Factors on Hemodynamic Measurements from Non-Invasive Photoplethysmography. Biosensors, 12(8), 598. https://doi.org/10.3390/bios12080598

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