Cardiovascular regulation involves the interplay of many physiologic variables which interact through multiple feedback and control loops. This work examines the coupling between heart rate (HR), instantaneous lung volume (ILV) and arterial blood pressure (BP) through the use of closed loop system identification techniques in order to assess the underlying physiology without disrupting normal regulatory function.

We have developed an identifiable, closed loop model of cardiovascular regulation that contains four transfer relations representing both autonomic (ILV to HR, BP to HR) and mechanical (ILV to BP, HR to BP) coupling mechanisms. The model also contains two additive perturbations: one to HR and one to BP. When the experimental subject is physiologically stable and when the data fluctuate within a small neighborhood of their mean values, it is hypothesized that the interaction between HR, ILV and BP in the model should obey linear, time-invariant dynamics. These dynamics and the spectra of the noise sources can be uniquely estimated even in the presence of feedback if one assumes that each transfer relation is causal and if the data are sufficiently broad-band. One achieves the first condition by parameterizing the transfer functions with the appropriate constant- coefficient difference equations. The data set is rendered broad-band through the application of a wide-spectrum perturbation such as random-interval breathing.

This approach is applied here to assess the importance of respiration and HR in the genesis of BP fluctuations below 0.5 Hz. The results suggest that BP variability at low frequencies does not depend strongly on HR fluctuations, consistent with independent experimental evidence. The present application shows that the use of closed loop system identification offers a new, minimallyinvasive approach to estimating the dynamics of cardiovascular regulation. The techniques developed here are very general and can be applied to other biological and physiological systems.