Robust model-based estimators for cardiac nerve activity

This paper outlines our design of a continuous estimator for the sympathetic innervation of the heart. The estimator is computed by linear methods, yet it is tested on a nonlinear, detailed model of cardiovascular and respiratory dynamics. Inverting a model output (blood pressure) to recover internal activity by means of the H_∞ and structured singular value (μ) design methods, allows direct treatment of signal and model uncertainty (noise and nonlinearity, respectively), in a computationally convenient, linear way. Numerical simulations suggest that the variability of the blood pressure signal can be decoded to yield good estimates of nerve activity despite parametric uncertainty, in a range of physiological conditions