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Entropy 2017, 19(1), 2; doi:10.3390/e19010002

A Multivariate Multiscale Fuzzy Entropy Algorithm with Application to Uterine EMG Complexity Analysis

1
Department of Electrical and Electronic Engineering, Imperial College, London SW7 2AZ, UK
2
Department of Electrical and Electronic Engineering, University of Dhaka, Dhaka 1000, Bangladesh
3
Centre for Perinatal Neuroscience, Department of Paediatrics, Imperial College, London W12 0HS, UK
*
Author to whom correspondence should be addressed.
Academic Editor: Anne Humeau-Heurtier
Received: 25 October 2016 / Revised: 15 December 2016 / Accepted: 19 December 2016 / Published: 22 December 2016
(This article belongs to the Special Issue Multivariate Entropy Measures and Their Applications)
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Abstract

The recently introduced multivariate multiscale entropy (MMSE) has been successfully used to quantify structural complexity in terms of nonlinear within- and cross-channel correlations as well as to reveal complex dynamical couplings and various degrees of synchronization over multiple scales in real-world multichannel data. However, the applicability of MMSE is limited by the coarse-graining process which defines scales, as it successively reduces the data length for each scale and thus yields inaccurate and undefined entropy estimates at higher scales and for short length data. To that cause, we propose the multivariate multiscale fuzzy entropy (MMFE) algorithm and demonstrate its superiority over the MMSE on both synthetic as well as real-world uterine electromyography (EMG) short duration signals. Based on MMFE features, an improvement in the classification accuracy of term-preterm deliveries was achieved, with a maximum area under the curve (AUC) value of 0.99. View Full-Text
Keywords: multivariate fuzzy entropy; multiscale complexity; uterine EMG multivariate fuzzy entropy; multiscale complexity; uterine EMG
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Ahmed, M.U.; Chanwimalueang, T.; Thayyil, S.; Mandic, D.P. A Multivariate Multiscale Fuzzy Entropy Algorithm with Application to Uterine EMG Complexity Analysis. Entropy 2017, 19, 2.

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