Hybrid Modeling of Photoplethysmography for Non-invasive Monitoring of Cardiovascular Parameters

TL;DR

This paper introduces a hybrid modeling approach combining simulations and unlabeled clinical data to non-invasively estimate cardiovascular biomarkers from PPG signals, addressing the challenge of predicting key cardiac parameters.

Contribution

The study presents a novel hybrid model that integrates hemodynamic simulations with clinical data to improve non-invasive cardiovascular monitoring from PPG signals.

Findings

Successfully detects fluctuations in cardiac output and stroke volume

Outperforms supervised baseline in monitoring temporal changes

Demonstrates feasibility of non-invasive biomarker estimation

Abstract

Continuous cardiovascular monitoring can play a key role in precision health. However, some fundamental cardiac biomarkers of interest, including stroke volume and cardiac output, require invasive measurements, e.g., arterial pressure waveforms (APW). As a non-invasive alternative, photoplethysmography (PPG) measurements are routinely collected in hospital settings. Unfortunately, the prediction of key cardiac biomarkers from PPG instead of APW remains an open challenge, further complicated by the scarcity of annotated PPG measurements. As a solution, we propose a hybrid approach that uses hemodynamic simulations and unlabeled clinical data to estimate cardiovascular biomarkers directly from PPG signals. Our hybrid model combines a conditional variational autoencoder trained on paired PPG-APW data with a conditional density estimator of cardiac biomarkers trained on labeled simulated APW segments. As a key result, our experiments demonstrate that the proposed approach can detect fluctuations of cardiac output and stroke volume and outperform a supervised baseline in monitoring temporal changes in these biomarkers.