Causal transfer function analysis to describe closed loop interactions between cardiovascular and cardiorespiratory variability signals.
Although the concept of transfer function is intrinsically related to an input-output relationship, the traditional and widely used estimation method merges both feedback and feedforward interactions between the two analyzed signals. This limitation may endanger the reliability of transfer function analysis in biological systems characterized by closed loop interactions. In this study, a method for estimating the transfer function between closed loop interacting signals was proposed and validated in the field of cardiovascular and cardiorespiratory variability. The two analyzed signals x and y were described by a bivariate autoregressive model, and the causal transfer function from x to y was estimated after imposing causality by setting to zero the model coefficients representative of the reverse effects from y to x. The method was tested in simulations reproducing linear open and closed loop interactions, showing a better adherence of the causal transfer function to the theoretical curves with respect to the traditional approach in presence of non-negligible reverse effects. It was then applied in ten healthy young subjects to characterize the transfer functions from respiration to heart period (RR interval) and to systolic arterial pressure (SAP), and from SAP to RR interval. In the first two cases, the causal and non-causal transfer function estimates were comparable, indicating that respiration, acting as exogenous signal, sets an open loop relationship upon SAP and RR interval. On the contrary, causal and traditional transfer functions from SAP to RR were significantly different, suggesting the presence of a considerable influence on the opposite causal direction. Thus, the proposed causal approach seems to be appropriate for the estimation of parameters, like the gain and the phase lag from SAP to RR interval, which have a large clinical and physiological relevance.
Lab. Biosegnali, Dipartimento di Fisica, Universitá di Trento, via Sommarive 14, 38050 Trento, Povo, Italy. faes@science.unitn.it
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D000328:Adult; D000465:Algorithms; D001794:Blood Pressure / Q000502:physiology; D002320:Cardiovascular Physiological Phenomena; D003198:Computer Simulation; D005246:Feedback / Q000502:physiology; D005260:Female; D006321:Heart / Q000502:physiology*; D006801:Humans; D016014:Linear Models; D008168:Lung / Q000502:physiology*; D008297:Male; D008954:Models, Biological; D012044:Regression Analysis; D015203:Reproducibility of Results; D012143:Respiratory Physiological Phenomena