Next Article in Journal
Some Inequalities Combining Rough and Random Information
Next Article in Special Issue
Multivariate Matching Pursuit Decomposition and Normalized Gabor Entropy for Quantification of Preictal Trends in Epilepsy
Previous Article in Journal
Prior and Posterior Linear Pooling for Combining Expert Opinions: Uses and Impact on Bayesian Networks—The Case of the Wayfinding Model
Previous Article in Special Issue
Correntropy Based Matrix Completion

Amplitude- and Fluctuation-Based Dispersion Entropy

School of Engineering, Institute for Digital Communications, University of Edinburgh, Edinburgh EH9 3FB, UK
Author to whom correspondence should be addressed.
Entropy 2018, 20(3), 210;
Received: 2 November 2017 / Revised: 5 February 2018 / Accepted: 13 March 2018 / Published: 20 March 2018
(This article belongs to the Special Issue Entropy in Signal Analysis)
Dispersion entropy (DispEn) is a recently introduced entropy metric to quantify the uncertainty of time series. It is fast and, so far, it has demonstrated very good performance in the characterisation of time series. It includes a mapping step, but the effect of different mappings has not been studied yet. Here, we investigate the effect of linear and nonlinear mapping approaches in DispEn. We also inspect the sensitivity of different parameters of DispEn to noise. Moreover, we develop fluctuation-based DispEn (FDispEn) as a measure to deal with only the fluctuations of time series. Furthermore, the original and fluctuation-based forbidden dispersion patterns are introduced to discriminate deterministic from stochastic time series. Finally, we compare the performance of DispEn, FDispEn, permutation entropy, sample entropy, and Lempel–Ziv complexity on two physiological datasets. The results show that DispEn is the most consistent technique to distinguish various dynamics of the biomedical signals. Due to their advantages over existing entropy methods, DispEn and FDispEn are expected to be broadly used for the characterization of a wide variety of real-world time series. The MATLAB codes used in this paper are freely available at View Full-Text
Keywords: nonlinear analysis; permutation entropy; dispersion entropy; fluctuation-based dispersion entropy; forbidden patterns nonlinear analysis; permutation entropy; dispersion entropy; fluctuation-based dispersion entropy; forbidden patterns
Show Figures

Figure 1

MDPI and ACS Style

Azami, H.; Escudero, J. Amplitude- and Fluctuation-Based Dispersion Entropy. Entropy 2018, 20, 210.

AMA Style

Azami H, Escudero J. Amplitude- and Fluctuation-Based Dispersion Entropy. Entropy. 2018; 20(3):210.

Chicago/Turabian Style

Azami, Hamed, and Javier Escudero. 2018. "Amplitude- and Fluctuation-Based Dispersion Entropy" Entropy 20, no. 3: 210.

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Back to TopTop