ArticleEntropy (Basel, Switzerland)2018
Amplitude- and Fluctuation-Based Dispersion Entropy.
Article in Entropy (Basel, Switzerland), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 papers.
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Who cites it
52 citing papers in PubMed.
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- EEG complexity analysis using enhanced entropy features for depression detection and severity classification.Journal of neuroengineering and rehabilitation · 2026Article
- Multistructural and Multiscale Instability Characterization of Gas-Liquid Two-Phase Flow with MRA-CMESSE.Entropy (Basel, Switzerland) · 2026Article
- Multiscale Characterization of Flow Instability for Gas-Liquid Two-Phase Flow.Entropy (Basel, Switzerland) · 2026Article
- Multiscale dispersion entropy of resting-state EEG in older adults with Alzheimer's disease, mild cognitive impairment, and remitted major depressive disorder.Journal of Alzheimer's disease : JAD · 2026Article
- Review of Recent (2015-2024) Popular Entropy Definitions Applied to Physiological Signals.Entropy (Basel, Switzerland) · 2025Review
- Cognitive brain lateralization through neurovascular coupling in healthy subjects: A statistical complexity analysis.Physiological reports · 2025Article
- Applications of Entropy in Data Analysis and Machine Learning: A Review.Entropy (Basel, Switzerland) · 2024Review
- Universality of Dynamical Symmetries in Chaotic Maps.Entropy (Basel, Switzerland) · 2024Article
- Generalized Gaussian Distribution Improved Permutation Entropy: A New Measure for Complex Time Series Analysis.Entropy (Basel, Switzerland) · 2024Article
- Fault Diagnosis Method for Wind Turbine Gearbox Based on Ensemble-Refined Composite Multiscale Fluctuation-Based Reverse Dispersion Entropy.Entropy (Basel, Switzerland) · 2024Article
- Tsallis Entropy-Based Complexity-IPE Casualty Plane: A Novel Method for Complex Time Series Analysis.Entropy (Basel, Switzerland) · 2024Article
- Fault Diagnosis of Wind Turbine Gearbox Based on Modified Hierarchical Fluctuation Dispersion Entropy of Tan-Sigmoid Mapping.Entropy (Basel, Switzerland) · 2024Article
- Benefits of Zero-Phase or Linear Phase Filters to Design Multiscale Entropy: Theory and Application.Entropy (Basel, Switzerland) · 2024Article
- Exploring the Hidden Complexity: Entropy Analysis in Pulse Oximetry of Female Athletes.Biosensors · 2024Article
- Use of Intrinsic Entropy to Assess the Instantaneous Complexity of Thoracoabdominal Movement Patterns to Indicate the Effect of the Iso-Volume Maneuver Trial on the Performance of the Step Test.Entropy (Basel, Switzerland) · 2023Article
- Refined Composite Multiscale Fuzzy Dispersion Entropy and Its Applications to Bearing Fault Diagnosis.Entropy (Basel, Switzerland) · 2023Article
- Ensemble Improved Permutation Entropy: A New Approach for Time Series Analysis.Entropy (Basel, Switzerland) · 2023Article
- EEG Signal Complexity Measurements to Enhance BCI-Based Stroke Patients' Rehabilitation.Sensors (Basel, Switzerland) · 2023Article
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2 authors.
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Abstract
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 http://dx.doi.org/10.7488/ds/2326.
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