Investigating cardiac and respiratory determinants of heart rate variability in an information-theoretic framework

Abstract

This study was aimed at comparing two alternative information-theoretic approaches for the combined analysis of heart rate variability (HRV) and respiration variability (RV). The approaches decompose the predictive information about HRV in two terms, quantifying respectively the information stored into HRV and that transferred to HRV from RV. Storage and transfer were assessed by the popular self entropy (SE) and transfer entropy (TE) measures, as well as by the alternative conditional SE (cSE) and cross entropy (CE) measures. The comparison was performed at a theoretical level, computing the exact values of the four measures for simulated cardiorespiratory dynamics, and on real data, estimating the measures from RV and HRV time series taken from healthy subjects during head-up tilt and paced breathing protocols. Both analyses suggested that, for the study of cardiorespiratory interactions which are mostly unidirectional from RV to HRV, the decomposition evidencing cSE and CE is more suitable to describe respiratory sinus arrhythmia and its modifications related to changes in cardiorespiratory interactions.