  The arterial system dynamically loads the heart through changes in arterial
compliance. The pressure-volume relation of arteries is known to be nonlinear,
but arterial compliance is often modeled as a constant value, due to ease of
estimation and interpretation. Incorporating nonlinear arterial compliance
affords insight into the continuous variations of arterial compliance in a
cardiac cycle and its effects on the heart, as the arterial system is coupled
with the left ventricle. We recently proposed a method for estimating nonlinear
compliance parameters that yielded good results under various vasoactive
states. This study examines the performance of the proposed method by
quantifying the uncertainty of the method in the presence of noise and
propagating the uncertainty through the system model to analyze its effects on
model predictions. Kernel density estimation used within a bootstrap Monte
Carlo simulation showed the method to be stable for various vasoactive states.
