Quantifying the complexity of human colonic pressure signals using an entropy measure

Quantifying the complexity of human colonic pressure signals using an entropy measure AbstractStudying the complexity of human colonic pressure signals is important in understanding this intricate, evolved, dynamic system. This article presents a method for quantifying the complexity of colonic pressure signals using an entropy measure. As a self-adaptive non-stationary signal analysis algorithm, empirical mode decomposition can decompose a complex pressure signal into a set of intrinsic mode functions (IMFs). Considering that IMF2, IMF3, and IMF4 represent crucial characteristics of colonic motility, a new signal was reconstructed with these three signals. Then, the time entropy (TE), power spectral entropy (PSE), and approximate entropy (AE) of the reconstructed signal were calculated. For subjects with constipation and healthy individuals, experimental results showed that the entropies of reconstructed signals between these two classes were distinguishable. Moreover, the TE, PSE, and AE can be extracted as features for further subject classification. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Biomedical Engineering / Biomedizinische Technik de Gruyter

Quantifying the complexity of human colonic pressure signals using an entropy measure

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Publisher
de Gruyter
Copyright
©2016 by De Gruyter
ISSN
1862-278X
eISSN
1862-278X
D.O.I.
10.1515/bmt-2015-0026
Publisher site
See Article on Publisher Site

Abstract

AbstractStudying the complexity of human colonic pressure signals is important in understanding this intricate, evolved, dynamic system. This article presents a method for quantifying the complexity of colonic pressure signals using an entropy measure. As a self-adaptive non-stationary signal analysis algorithm, empirical mode decomposition can decompose a complex pressure signal into a set of intrinsic mode functions (IMFs). Considering that IMF2, IMF3, and IMF4 represent crucial characteristics of colonic motility, a new signal was reconstructed with these three signals. Then, the time entropy (TE), power spectral entropy (PSE), and approximate entropy (AE) of the reconstructed signal were calculated. For subjects with constipation and healthy individuals, experimental results showed that the entropies of reconstructed signals between these two classes were distinguishable. Moreover, the TE, PSE, and AE can be extracted as features for further subject classification.

Journal

Biomedical Engineering / Biomedizinische Technikde Gruyter

Published: Feb 1, 2016

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