A Multivariate Multiscale Fuzzy Entropy Algorithm with Application to Uterine EMG Complexity Analysis
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 rea...
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doaj-c17bc5d007a5422e81fb3e33cdb4e9ae2020-11-25T00:18:25ZengMDPI AGEntropy1099-43002016-12-01191210.3390/e19010002e19010002A Multivariate Multiscale Fuzzy Entropy Algorithm with Application to Uterine EMG Complexity AnalysisMosabber U. Ahmed0Theerasak Chanwimalueang1Sudhin Thayyil2Danilo P. Mandic3Department of Electrical and Electronic Engineering, Imperial College, London SW7 2AZ, UKDepartment of Electrical and Electronic Engineering, Imperial College, London SW7 2AZ, UKCentre for Perinatal Neuroscience, Department of Paediatrics, Imperial College, London W12 0HS, UKDepartment of Electrical and Electronic Engineering, Imperial College, London SW7 2AZ, UKThe 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.http://www.mdpi.com/1099-4300/19/1/2multivariate fuzzy entropymultiscale complexityuterine EMG |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Mosabber U. Ahmed Theerasak Chanwimalueang Sudhin Thayyil Danilo P. Mandic |
spellingShingle |
Mosabber U. Ahmed Theerasak Chanwimalueang Sudhin Thayyil Danilo P. Mandic A Multivariate Multiscale Fuzzy Entropy Algorithm with Application to Uterine EMG Complexity Analysis Entropy multivariate fuzzy entropy multiscale complexity uterine EMG |
author_facet |
Mosabber U. Ahmed Theerasak Chanwimalueang Sudhin Thayyil Danilo P. Mandic |
author_sort |
Mosabber U. Ahmed |
title |
A Multivariate Multiscale Fuzzy Entropy Algorithm with Application to Uterine EMG Complexity Analysis |
title_short |
A Multivariate Multiscale Fuzzy Entropy Algorithm with Application to Uterine EMG Complexity Analysis |
title_full |
A Multivariate Multiscale Fuzzy Entropy Algorithm with Application to Uterine EMG Complexity Analysis |
title_fullStr |
A Multivariate Multiscale Fuzzy Entropy Algorithm with Application to Uterine EMG Complexity Analysis |
title_full_unstemmed |
A Multivariate Multiscale Fuzzy Entropy Algorithm with Application to Uterine EMG Complexity Analysis |
title_sort |
multivariate multiscale fuzzy entropy algorithm with application to uterine emg complexity analysis |
publisher |
MDPI AG |
series |
Entropy |
issn |
1099-4300 |
publishDate |
2016-12-01 |
description |
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. |
topic |
multivariate fuzzy entropy multiscale complexity uterine EMG |
url |
http://www.mdpi.com/1099-4300/19/1/2 |
work_keys_str_mv |
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