دورية أكاديمية

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

التفاصيل البيبلوغرافية
العنوان: A Computational Modeling and Simulation Workflow to Investigate the Impact of Patient-Specific and Device Factors on Hemodynamic Measurements from Non-Invasive Photoplethysmography
المؤلفون: Jesse Fine, Michael J. McShane, Gerard L. Coté, Christopher G. Scully
المصدر: Biosensors, Vol 12, Iss 8, p 598 (2022)
بيانات النشر: MDPI AG, 2022.
سنة النشر: 2022
المجموعة: LCC:Biotechnology
مصطلحات موضوعية: photoplethysmography, remote monitoring, computational modeling and simulation, medical device design, Biotechnology, TP248.13-248.65
الوصف: 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.
نوع الوثيقة: article
وصف الملف: electronic resource
اللغة: English
تدمد: 2079-6374
Relation: https://www.mdpi.com/2079-6374/12/8/598; https://doaj.org/toc/2079-6374
DOI: 10.3390/bios12080598
URL الوصول: https://doaj.org/article/35fd458f0575430f81079f6941c50460
رقم الأكسشن: edsdoj.35fd458f0575430f81079f6941c50460
قاعدة البيانات: Directory of Open Access Journals
الوصف
تدمد:20796374
DOI:10.3390/bios12080598