  Recent developments in cardiovascular modelling allow us to simulate blood
flow in an entire human body. Such model can also be used to create databases
of virtual subjects, with sizes limited only by computational resources. In
this work, we study if it is possible to estimate cardiovascular health indices
using machine learning approaches. In particular, we carry out theoretical
assessment of estimating aortic pulse wave velocity, diastolic and systolic
blood pressure and stroke volume using pulse transit/arrival timings derived
from photopletyshmography signals. For predictions, we train Gaussian process
regression using a database of virtual subjects generated with a cardiovascular
simulator. Simulated results provides theoretical assessment of accuracy for
predictions of the health indices. For instance, aortic pulse wave velocity can
be estimated with a high accuracy (r>0.9) when photopletyshmography is measured
from left carotid artery using a combination of foot-to-foot pulse transmit
time and peak location derived for the predictions. Similar accuracy can be
reached for diastolic blood pressure, but predictions of systolic blood
pressure are less accurate (r>0.75) and the stroke volume predictions are
mostly contributed by heart rate.
