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Multivariate entropy analysis with data-driven scales | IEEE Conference Publication | IEEE Xplore

Multivariate entropy analysis with data-driven scales


Abstract:

A data-adaptive algorithm for the entropy-based analysis of structural regularities (complexity) in multivariate signals is proposed. This is achieved by combining multiv...Show More

Abstract:

A data-adaptive algorithm for the entropy-based analysis of structural regularities (complexity) in multivariate signals is proposed. This is achieved by combining multivariate sample entropy with a multivariate extension of empirical mode decomposition, both data-driven multiscale techniques. The proposed analysis across data-adaptive scales makes the approach robust to nonstationarity, a critical issue with information theoretic measures. Simulations on synthetic and real-world physiological data support the approach and validate the hypothesis of increased complexity for unconstrained as compared to constrained (due to e.g. ageing or illness) biological systems.
Date of Conference: 25-30 March 2012
Date Added to IEEE Xplore: 30 August 2012
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Conference Location: Kyoto, Japan

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