A Meta-heuristic Approach to Identification of Renal Blood Flow

Hypertension (high blood pressure) is the most prominent cardiovascular disease affecting the majority of the population. One of the primary mediators of controlling blood pressure is the kidney, which is innervated by sympathetic nerves, through control of blood volume. It is therefore important to develop a dynamic model which explains the interaction between the sympathetic nerve activity (SNA) and renal blood flow (RBF). The present study proposes a simple approach based on binary particle swarms (BPSO) to model the complex interaction between sympathetic nerve activity (SNA) and renal blood flow (RBF) using polynomial nonlinear autoregressive with exogenous input (NARX) model. The effectiveness of BPSO is demonstrated by fitting models to different sets of RBF data which are collected from several rabbits under two conditions (vasoconstriction and vasodilation). Frequency domain analysis of the fitted model is carried out to investigate if there exist any similarities in the renal dynamics under the infusion of vasoconstrictors or vasodilators.