Multivariate entropy analysis with data-driven scales

Author

Ahmed, Mobyen Uddin ; Rehman, N. ; Looney, David ; Rutkowski, Tomasz M. ; Kidmose, Preben ; Mandic, Danilo P.

Author_Institution

Dept. of Electr. & Electron. Eng., Imperial Coll. London, London, UK

fYear

2012

fDate

25-30 March 2012

Firstpage

3901

Lastpage

3904

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.

Keywords

entropy; signal sampling; biological system; data-adaptive scale algorithm; data-driven multiscale technique; data-driven scale; empirical mode decomposition; information theoretic measurement; multivariate extension; multivariate sample entropy analysis; multivariate signal; physiological data support; structural regularity; Complexity theory; Entropy; Indexes; Time series analysis; Vectors; White noise; Multivariate sample entropy; complexity of physiological data; dynamical complexity; multivariate empirical mode decomposition; multivariate multiscale entropy;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech and Signal Processing (ICASSP), 2012 IEEE International Conference on

Conference_Location

Kyoto

ISSN

1520-6149

Print_ISBN

978-1-4673-0045-2

Electronic_ISBN

1520-6149

Type

conf

DOI

10.1109/ICASSP.2012.6288770

Filename

6288770