Hybrid Modeling of Photoplethysmography for Non-invasive Monitoring of Cardiovascular Parameters
This work addresses the problem of non-invasive cardiovascular monitoring for precision health, offering an incremental improvement over existing methods.
The paper tackled the challenge of non-invasively predicting cardiovascular biomarkers like stroke volume and cardiac output from photoplethysmography (PPG) signals, which is difficult due to scarce annotated data, and demonstrated that their hybrid model outperforms a supervised baseline in monitoring temporal changes in these biomarkers.
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.