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Phys Rev E Stat Nonlin Soft Matter Phys. 2011 May;83(5 Pt 1):051112. Epub 2011 May 11.

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

Author information

1
Department of Physics and BIOtech, University of Trento, Trento, Italy. luca.faes@unitn.it

Abstract

We present an approach, framed in information theory, to assess nonlinear causality between the subsystems of a whole stochastic or deterministic dynamical system. The approach follows a sequential procedure for nonuniform embedding of multivariate time series, whereby embedding vectors are built progressively on the basis of a minimization criterion applied to the entropy of the present state of the system conditioned to its past states. A corrected conditional entropy estimator compensating for the biasing effect of single points in the quantized hyperspace is used to guarantee the existence of a minimum entropy rate at which to terminate the procedure. The causal coupling is detected according to the Granger notion of predictability improvement, and is quantified in terms of information transfer. We apply the approach to simulations of deterministic and stochastic systems, showing its superiority over standard uniform embedding. Effects of quantization, data length, and noise contamination are investigated. As practical applications, we consider the assessment of cardiovascular regulatory mechanisms from the analysis of heart period, arterial pressure, and respiration time series, and the investigation of the information flow across brain areas from multichannel scalp electroencephalographic recordings.

PMID:
21728495
DOI:
10.1103/PhysRevE.83.051112
[Indexed for MEDLINE]

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