Information-based detection of nonlinear Granger causality in multivariate processes via a nonuniform embedding technique

Faes, Luca; Nollo, Giandomenico; Porta, Alberto

doi:10.1103/physreve.83.051112

Cited by 222 publications

(256 citation statements)

References 53 publications

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“…In the most general case, and when nonlinear effects are relevant, non-parametric approaches are recommended to yield model-free estimates of entropy and MI [29,[32][33][34]. However, the necessity to estimate entropies of variables of very high dimension may impair the reliability of model-free estimators, especially when short realizations of the processes are available [35].…”

Section: Computation Of Information Dynamicsmentioning

confidence: 99%

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

Faes

Porta

Nollo

2015

Entropy

Self Cite

115

155

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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 OPEN ACCESSEntropy 2015, 17 278 dynamics and to interpret the alteration of these effects with changing experimental conditions.

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Section: Computation Of Information Dynamicsmentioning

confidence: 99%

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

Faes

Porta

Nollo

2015

Entropy

Self Cite

115

155

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“…STE is a robust and computationally fast method to quantify the dominating direction of information flow between time series from structurally identical and non-identical coupled systems. Recently, Faes et al [52,53] introduced an enhanced version based on the corrected CE and a sequential procedure for non-uniform embedding to assess nonlinear GC in multivariate time series. It is actually a modified estimation of TE.…”

Section: (C) Entropymentioning

confidence: 99%

Cardiovascular and cardiorespiratory coupling analyses: a review

Schulz

Adochiei

Edu

et al. 2013

Phil. Trans. R. Soc. A.

145

127

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Recently, methods have been developed to analyse couplings in dynamic systems. In the field of medical analysis of complex cardiovascular and cardiorespiratory systems, there is growing interest in how insights may be gained into the interaction between regulatory mechanisms in healthy and diseased persons. The couplings within and between these systems can be linear or nonlinear. However, the complex mechanisms involved in cardiovascular and cardiorespiratory regulation very likely interact with each other in a nonlinear way. Recent advances in nonlinear dynamics and information theory have allowed the multivariate study of information transfer between time series. They therefore might be able to provide additional diagnostic and prognostic information in medicine and might, in particular, be able to complement traditional linear coupling analysis techniques. In this review, we describe the approaches (Granger causality, nonlinear prediction, entropy, symbolization, phase synchronization) most commonly applied to detect direct and indirect couplings between time series, especially focusing on nonlinear approaches. We will discuss their capacity to quantify direct and indirect couplings and the direction (driver–response relationship) of the considered interaction between different biological time series. We also give their basic theoretical background, their basic requirements for application, their main features and demonstrate their usefulness in different applications in the field of cardiovascular and cardiorespiratory coupling analyses.

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“…To investigate the functional relevance of coupling between the brain and the heart, CCE based on embedding the multivariate time series was used to detect nonlinear causal interactions between the brain and the heart. This method has been successfully used for assessment of cardiovascular regulatory mechanisms, such as analysis of the variability in heart period, arterial pressure, and respiratory rate; and investigation of the information flow across brain areas [28]. ECG data cannot be used in isolation to investigate the information flow between the brain and the heart, because the autonomic nervous system regulates cardiac performance by changing the RR interval.…”

Section: Discussionmentioning

confidence: 99%

Causal interactions between the cerebral cortex and the autonomic nervous system

Yu¹,

Zhang²,

Zhang

2014

Sci. China Life Sci.

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Mental states such as stress and anxiety can cause heart disease. On the other hand, meditation can improve cardiac performance. In this study, the heart rate variability, directed transfer function and corrected conditional entropy were used to investigate the effects of mental tasks on cardiac performance, and the functional coupling between the cerebral cortex and the heart. When subjects tried to decrease their heart rate by volition, the sympathetic nervous system was inhibited and the heart rate decreased. When subjects tried to increase their heart rate by volition, the parasympathetic nervous system was inhibited and the sympathetic nervous system was stimulated, and the heart rate increased. When autonomic nervous system activity was regulated by mental tasks, the information flow from the post-central areas to the pre-central areas of the cerebral cortex increased, and there was greater coupling between the brain and the heart. Use of directed transfer function and corrected conditional entropy techniques enabled analysis of electroencephalographic recordings, and of the information flow causing functional coupling between the brain and the heart. mental task, autonomic nervous system, cerebral cortex, heart rate variability, information flow Citation:Yu XL, Zhang C, Zhang JB. Causal interactions between the cerebral cortex and the autonomic nervous system.

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Information-based detection of nonlinear Granger causality in multivariate processes via a nonuniform embedding technique

Cited by 222 publications

References 53 publications

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

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

Cardiovascular and cardiorespiratory coupling analyses: a review

Causal interactions between the cerebral cortex and the autonomic nervous system

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