Identifying a low-order beat-to-beat model of arterial baroreflex action

Abstract

The arterial baroreflex is a fast-acting control mechanism that the body relies on to regulate blood pressure. Previous efforts to quantitatively model the baroreflex have relied primarily on non-parametric characterization of the transfer function from blood pressure to heart rate (Berger et al.,1989, Akselrod et al., 1981,1985). Of the parametric models proposed, most focus on matching empirical transfer functions with continuous-time models (Berger et al., 1991). Use of these models is often restricted to simulation, and consequently not focused on prediction. We develop a beat-to-beat, one-pole model for the baroreflex that can parsimoniously capture both the empirical frequency-domain and time-domain characteristics of the baroreflex. Further, we develop a robust identification method for on-line estimation of our model parameters from clinical data. We conclude by presenting preliminary results of our model and estimation method applied to patients undergoing drug-induced autonomic blockade.