A model-based approach to stability analysis of autonomic-cardiac regulation.

This paper presents a model-based approach to analyze the stability of autonomic-cardiac regulation. In the proposed approach, a low-order lumped parameter model of autonomic-cardiac regulation is used to derive the system equilibria based on the measurements of heart rate and blood pressure, and then the stability margin associated with the equilibria is quantified via the Lyapunov's stability analysis method. A unique strength of the proposed approach is that it provides a quantitative measure of autonomic-cardiac stability via a computationally efficient analysis. Therefore, by integrating it with system identification techniques to derive autonomic-cardiac regulation model tuned to each individual, the proposed approach is able to assess subject-specific autonomic-cardiac stability. Indeed, our initial in-silico investigation showed that the proposed approach could estimate the system equilibria accurately, and the associated stability margin behaved consistently with widely accepted physiologic knowledge. The proposed approach may be useful in identifying physiological conditions that can lead to instability in autonomic-cardiac regulation, quantifying the margin of stability and distance to instability related to autonomic-cardiac regulation, and developing interventions to prevent autonomic-cardiac instability.