Multivariate entropy analysis with data-driven scales

M. U. Ahmed, N. Rehman, D. Looney, T. M. Rutkowski, P. Kidmose, D. P. Mandic

Research output: Contribution to book/anthology/report/proceedingArticle in proceedingsResearchpeer-review

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.
Original languageEnglish
Title of host publication2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Number of pages4
PublisherIEEE
Publication date1 Mar 2012
Pages3901-3904
ISBN (Print)9781467300469
DOIs
Publication statusPublished - 1 Mar 2012
EventIEEE International Conference on Acoustics, Speech and Signal Processing - Kyoto, Japan
Duration: 25 Mar 201230 Mar 2012

Conference

ConferenceIEEE International Conference on Acoustics, Speech and Signal Processing
Country/TerritoryJapan
CityKyoto
Period25/03/201230/03/2012

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