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

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.

New Methodology of Human Health Express Diagnostics Based on Pulse Wave Measurements and Occlusion Test

“…It helps to obtain more accurate values of the position on the time axis for maxima and minima. These data are necessary to solve several problems in diagnosing cardiac activity [ 14 , 15 , 16 , 17 , 18 , 19 ]. However, there is no information about the state of health in the parameters of these steps.…”

“…The pulse wave signal must be divided into six intervals (two correspond to the rising front, two to the falling front, and two to the area of the maxima). It is a classical division of the pulse wave period, which is used regardless of the method of its registration [ 4 , 9 , 16 , 17 , 18 , 19 , 20 , 21 ]. One dependence is used to describe the rise fronts of a pulse wave (in amplitude from A 0 to A max1 and from A n-k to A max2 ), and another dependence is used for the decaying front (from A max1 to A n-k and from A max1 to A n ).…”

“…Therefore, for personal use for these purposes, transmission pulse oximetry is used [ 12 , 13 ], which allows you to register a pulse wave and measure the pulse and oxygen saturation of hemoglobin in the blood. Modeling and analysis of the pulse waveform [ 14 , 15 ], assessment of its propagation velocity [ 16 , 17 ], and measurement of cardiac output based on it [ 18 , 19 ] help physicians assess the functional state of the cardiovascular and other human systems.…”

Wearable Multi-Channel Pulse Signal Acquisition System Based on Flexible MEMS Sensor Arrays with TSV Structure

“…Given its non-invasive, convenient, and rapid diagnosis, pulse diagnosis has significant potential for application and development. As sensor and signal processing technologies continue to advance [ 9 , 10 , 11 , 12 ], the objective and digitalization of pulse diagnosis have become research hotspots.…”