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Heart rate variability results from the combined activity of severalphysiological systems, including the cardiac, vascular, and respiratory systemswhich have their own internal regulation, but also interact with each other topreserve the homeostatic function. These control mechanisms operate acrossmultiple temporal scales, resulting in the simultaneous presence of short-termdynamics and long-range correlations. The Network Physiology framework providesstatistical tools based on information theory able to quantify structuralaspects of multivariate and multiscale interconnected mechanisms driving thedynamics of complex physiological networks. In this work, the multiscalerepresentation of Transfer Entropy from Systolic Arterial Pressure (S) andRespiration (R) to Heart Period (H) and of its decomposition into unique,redundant and synergistic contributions is obtained using a VectorAutoRegressive Fractionally Integrated (VARFI) framework for Gaussianprocesses. This novel approach allows to quantify the directed information flowaccounting for the simultaneous presence of short-term dynamics and long-rangecorrelations among the analyzed processes. The approach is first illustrated insimulated VARFI processes and then applied to H, S and R time series measuredin healthy subjects monitored at rest and during mental and postural stress.Our results highlight the dependence of the information transfer on the balancebetween short-term and long-range correlations in coupled dynamical systems,which cannot be observed using standard methods that do not consider long-rangecorrelations. The proposed methodology shows that postural stress induceslarger redundant effects at short time scales and mental stress induces largercardiovascular information transfer at longer time scales.

physiological measurement

Multiscale Partial Information Decomposition of Dynamic Processes with  Short and Long-range correlations: Theory and Application to Cardiovascular  Control