Cardiovascular variability signals: towards the identification of a closed-loop model of the neural control mechanisms

The authors consider parametric methods for processing cardiovascular signals and try to provide a global, although indirect evaluation of some neural regulatory activities. In particular, the variability signals of the heart rate (under the form of interval tachogram) and arterial blood pressure (systogram) together with respiratory movement signal (respirogram) are considered as inputs to a closed-loop model which describes a few aspects of the physiological interactions among the signals themselves. The identifiability of the transfer function of the model is demonstrated from the joint process black-box description of the signals. A direct identification procedure is proposed dividing the system into two dynamic adjustment models. A few suggestions are deduced on how and where the respirogram enters the model and on the genesis of the 10-s rhythm, parameters relevant to the Starling effect, Windkessel model, and the gain of baroreceptor mechanisms. The approach presented is intended also to provide a general frame for closed-loop identification in different pathophysiological conditions.