Next Article in Journal
Black-Box Optimization Using Geodesics in Statistical Manifolds
Next Article in Special Issue
Analyses of Heart Rate, Respiration and Cardiorespiratory Coupling in Patients with Schizophrenia
Previous Article in Journal
The Entropy of an Armco Iron under Irreversible Deformation
Previous Article in Special Issue
Multiscale Entropy Analysis of Heart Rate Variability for Assessing the Severity of Sleep Disordered Breathing
Article Menu

Export Article

Open AccessArticle
Entropy 2015, 17(1), 277-303; doi:10.3390/e17010277

Information Decomposition in Bivariate Systems: Theory and Application to Cardiorespiratory Dynamics

1
BIOtech, Department of Industrial Engineering, University of Trento, and IRCS PAT-FBK Trento, Italy
2
Department of Biomedical Sciences for Health, University of Milan, Via R. Galeazzi 4, 20161 Milan, Italy
3
IRCCS Galeazzi Orthopedic Institute, Via R. Galeazzi 4, 20161 Milan, Italy
*
Author to whom correspondence should be addressed.
Received: 3 November 2014 / Accepted: 11 December 2014 / Published: 12 January 2015
(This article belongs to the Special Issue Entropy and Cardiac Physics)
View Full-Text   |   Download PDF [944 KB, uploaded 24 February 2015]   |  

Abstract

In the framework of information dynamics, the temporal evolution of coupled systems can be studied by decomposing the predictive information about an assigned target system into amounts quantifying the information stored inside the system and the information transferred to it. While information storage and transfer are computed through the known self-entropy (SE) and transfer entropy (TE), an alternative decomposition evidences the so-called cross entropy (CE) and conditional SE (cSE), quantifying the cross information and internal information of the target system, respectively. This study presents a thorough evaluation of SE, TE, CE and cSE as quantities related to the causal statistical structure of coupled dynamic processes. First, we investigate the theoretical properties of these measures, providing the conditions for their existence and assessing the meaning of the information theoretic quantity that each of them reflects. Then, we present an approach for the exact computation of information dynamics based on the linear Gaussian approximation, and exploit this approach to characterize the behavior of SE, TE, CE and cSE in benchmark systems with known dynamics. Finally, we exploit these measures to study cardiorespiratory dynamics measured from healthy subjects during head-up tilt and paced breathing protocols. Our main result is that the combined evaluation of the measures of information dynamics allows to infer the causal effects associated with the observed dynamics and to interpret the alteration of these effects with changing experimental conditions. View Full-Text
Keywords: cardiorespiratory interactions; causality; dynamical systems; information dynamics; heart rate variability; multivariate autoregressive processes; transfer entropy cardiorespiratory interactions; causality; dynamical systems; information dynamics; heart rate variability; multivariate autoregressive processes; transfer entropy
Figures

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Faes, L.; Porta, A.; Nollo, G. Information Decomposition in Bivariate Systems: Theory and Application to Cardiorespiratory Dynamics. Entropy 2015, 17, 277-303.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Entropy EISSN 1099-4300 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top