Propagation of uncertainty and analysis of signal-to-noise in nonlinear compliance estimations of an arterial system model

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.