Multivariate entropy analysis with data-driven scales

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

doi:10.1109/icassp.2012.6288770

Multivariate entropy analysis with data-driven scales

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

Department of Electrical and Computer Engineering - Signal Processing and Machine learning

Research output: Contribution to book/anthology/report/proceeding › Article in proceedings › Research › peer-review

12 Citations (Scopus)

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 language	English
Title of host publication	2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Number of pages	4
Publisher	IEEE
Publication date	1 Mar 2012
Pages	3901-3904
ISBN (Print)	9781467300469
DOIs	https://doi.org/10.1109/icassp.2012.6288770 https://doi.org/10.1109/ICASSP.2012.6288770
Publication status	Published - 1 Mar 2012
Event	IEEE International Conference on Acoustics, Speech and Signal Processing - Kyoto, Japan Duration: 25 Mar 2012 → 30 Mar 2012

Conference

Conference	IEEE International Conference on Acoustics, Speech and Signal Processing
Country/Territory	Japan
City	Kyoto
Period	25/03/2012 → 30/03/2012

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https://doi.org/10.1109%2Ficassp.2012.6288770

Cite this

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title = "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 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.",

author = "Ahmed, {M. U.} and N. Rehman and D. Looney and Rutkowski, {T. M.} and P. Kidmose and Mandic, {D. P.}",

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doi = "10.1109/icassp.2012.6288770",

language = "English",

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

publisher = "IEEE",

note = "IEEE International Conference on Acoustics, Speech and Signal Processing ; Conference date: 25-03-2012 Through 30-03-2012",

}

Ahmed, MU, Rehman, N, Looney, D, Rutkowski, TM, Kidmose, P & Mandic, DP 2012, Multivariate entropy analysis with data-driven scales. in 2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, pp. 3901-3904, IEEE International Conference on Acoustics, Speech and Signal Processing, Kyoto, Japan, 25/03/2012. https://doi.org/10.1109/icassp.2012.6288770, https://doi.org/10.1109/ICASSP.2012.6288770

TY - GEN

T1 - Multivariate entropy analysis with data-driven scales

AU - Ahmed, M. U.

AU - Rehman, N.

AU - Looney, D.

AU - Rutkowski, T. M.

AU - Kidmose, P.

AU - Mandic, D. P.

PY - 2012/3/1

Y1 - 2012/3/1

N2 - 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.

AB - 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.

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U2 - 10.1109/icassp.2012.6288770

DO - 10.1109/icassp.2012.6288770

M3 - Article in proceedings

AN - SCOPUS:84867610380

SN - 9781467300469

SP - 3901

EP - 3904

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

PB - IEEE

T2 - IEEE International Conference on Acoustics, Speech and Signal Processing

Y2 - 25 March 2012 through 30 March 2012

ER -

Multivariate entropy analysis with data-driven scales

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