Ensemble entropy: a low bias approach for data analysis

Azami, H, Sanei, S ORCID: https://orcid.org/0000-0002-3437-2801 and Rajji, TK, 2022. Ensemble entropy: a low bias approach for data analysis. Knowledge-Based Systems: 109876. ISSN 0950-7051

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Abstract

To quantify the data irregularity of data, there are a number of entropy measures each with its own advantages and disadvantages. In this pilot study, a new concept, namely ensemble entropy, is introduced and used to generate more stable and low bias signal patterns for entropy estimation. We propose ensemble versions of sample entropy (SampEn), permutation entropy, dispersion entropy (DispEn), fluctuation DispEn (FDispEn) based on the combination of different parameters initialization for a original entropy method. Also, ensemble Shannon and conditional entropy methods based on the entropy values obtained by different entropy algorithms. We applied the techniques to different synthetic and three biomedical datasets to investigate the behaviour of the ensemble methods on the changes in the data dynamics. The results suggest that ensemble approaches are able to distinguish different kinds of noises and the degrees of randomness in our generated MIX process. Ensemble SampEn, unlike SampEn, does not result in undefined values for short signals. Ensemble DispEn needs a smaller number of samples for distinguishing different kinds of noise. The majority of ensemble methods result in larger differences between younger and older subjects using their RR intervals as well as healthy young vs. elderly children using their walking stride interval data based on Hedges’ g effect size. The ensemble algorithms lead to more stable results (lower coefficients of variations) for the synthetic data (different kinds of noises and mixed processes) and discriminated different types of physiological signals better than their corresponding original entropy approaches. The Matlab code used in this paper will be available at https://github.com/HamedAzami/ upon publication.

Item Type:	Journal article
Publication Title:	Knowledge-Based Systems
Creators:	Azami, H., Sanei, S. and Rajji, T.K.
Publisher:	Elsevier
Date:	10 September 2022
ISSN:	0950-7051
Identifiers:	Number Type 10.1016/j.knosys.2022.109876 DOI 1596528 Other
Divisions:	Schools > School of Science and Technology
Record created by:	Jonathan Gallacher
Date Added:	12 Sep 2022 08:21
Last Modified:	10 Sep 2023 03:00
URI:	https://irep.ntu.ac.uk/id/eprint/47011

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