Multivariate multiscale entropy: a tool for complexity analysis of multichannel data.

By Mosabber Uddin Ahmed, Danilo P. Mandic. Physical review. E, Statistical, nonlinear, and soft matter physics, 2011

327 citations recorded by the source corpus.

This work generalizes the recently introduced univariate multiscale entropy (MSE) analysis to the multivariate case. This is achieved by introducing multivariate sample entropy (MSampEn) in a rigorous way, in order to account for both within- and cross-channel dependencies in multiple data channels, and by evaluating it over multiple temporal scales. The multivariate MSE (MMSE) method is shown to provide an assessment of the underlying dynamical richness of multichannel observations, and more degrees of freedom in the analysis than standard MSE. The benefits of the proposed approach are illustrated by simulations on complexity analysis of multivariate stochastic processes and on real-world multichannel physiological and environmental data.

Subfields: Artificial Intelligence, Economics and Econometrics, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/38958923db7a09cb33d498633a0d0c71b709da98

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Citations in the corpus

The Multiscale Entropy Algorithm and Its Variants: A Review (2015, methodology): “The detailed multivariate sample entropy algorithm can be found in [33,34].”
Time-dependent series variance learning with recurrent mixture density networks (2013, background): “This is a micro-level view of the underlying volatility process, which is different from the macro-level view of complexity science [12,35].”
Causation entropy identifies indirect influences, dominance of neighbors and anticipatory couplings (2014, background): “Given the recent advancements in estimating transfer entropy in rather general settings includingmultivariate time series and infinite time delay [14,35], it is our hope to build on the idea of causation entropy to explore information flow and coupling inference in larger-scale systems, which are im”