Steady cardiovascular monitoring can play a key function in precision well being. Nevertheless, some basic cardiac
biomarkers of curiosity, together with stroke quantity and cardiac output, require invasive measurements, e.g., arterial stress
waveforms (APW). As a non-invasive various, photoplethysmography (PPG) measurements are routinely collected
in hospital settings. Sadly, the prediction of key cardiac biomarkers from PPG as an alternative of APW stays an open
problem, additional difficult by the shortage of annotated PPG measurements. As an answer, we suggest a hybrid ap-
proach that makes use of hemodynamic simulations and unlabeled scientific information to estimate cardiovascular biomarkers straight
from PPG indicators. Our hybrid mannequin combines a conditional variational autoencoder educated on paired PPG-APW information
with a conditional density estimator of cardiac biomarkers educated on labeled simulated APW segments. As a key outcome,
our experiments reveal that the proposed method can detect fluctuations of cardiac output and stroke quantity
and outperform a supervised baseline in monitoring temporal adjustments in these biomarkers.
- †ETH Zurich, Switzerland
- ** Work carried out whereas at Apple
