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

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.

[1]  Markus P. Schlaich,et al.  Change in Sympathetic Nerve Firing Pattern Associated with Dietary Weight Loss in the Metabolic Syndrome , 2011, Front. Physio..

[2]  J. Taylor,et al.  Short‐term cardiovascular oscillations in man: measuring and modelling the physiologies , 2002, The Journal of physiology.

[3]  A. Malliani,et al.  Model for the assessment of heart period and arterial pressure variability interactions and of respiration influences , 1994, Medical and Biological Engineering and Computing.

[4]  Schreiber,et al.  Measuring information transfer , 2000, Physical review letters.

[5]  Albert Y. Zomaya,et al.  The local information dynamics of distributed computation in complex systems , 2012 .

[6]  J. Cacioppo,et al.  Respiratory sinus arrhythmia: autonomic origins, physiological mechanisms, and psychophysiological implications. , 1993, Psychophysiology.

[7]  L. Faes,et al.  Information Domain Approach to the Investigation of Cardio-Vascular, Cardio-Pulmonary, and Vasculo-Pulmonary Causal Couplings , 2011, Front. Physio..

[8]  Andreas Voss,et al.  Cardiovascular and cardiorespiratory coupling analyses: a review , 2013, Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[9]  Elke Vlemincx,et al.  The effect of instructed ventilatory patterns on physiological and psychological dimensions of relaxation , 2010 .

[10]  A. Malliani,et al.  Information domain analysis of cardiovascular variability signals: Evaluation of regularity, synchronisation and co-ordination , 2000, Medical and Biological Engineering and Computing.

[11]  Luca Faes,et al.  Conditional Entropy-Based Evaluation of Information Dynamics in Physiological Systems , 2014 .

[12]  Luca Faes,et al.  Information decomposition of short-term cardiovascular and cardiorespiratory variability , 2013, Computing in Cardiology 2013.

[13]  Luca Faes,et al.  Measuring Connectivity in Linear Multivariate Processes: Definitions, Interpretation, and Practical Analysis , 2012, Comput. Math. Methods Medicine.