ArticlePhysical review. E2017
Entropy measures, entropy estimators, and their performance in quantifying complex dynamics: Effects of artifacts, nonstationarity, and long-range correlations.
Article in Physical review. E, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 63 papers.
What it found
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Who cites it
63 citing papers in PubMed.
- FuzzyEn Compared to SampEn for Evaluation of Dynamic Complexity.Entropy (Basel, Switzerland) · 2026Article
- Autonomic impairment in advanced heart-failure patients revealed by nonlinear heart rate variability measured during exercise.Scientific reports · 2026Article
- EEG complexity analysis using enhanced entropy features for depression detection and severity classification.Journal of neuroengineering and rehabilitation · 2026Article
- Direct causality measures unravel complex networks of cardiovascular dynamics and their modifications with postural stress.PLoS computational biology · 2026Article
- Quantifying coupling and causality in dynamic bivariate systems: a unified framework for time-domain, spectral, and information-theoretic analysis.Frontiers in network physiology · 2025Article
- Mutual information-based teamwork evaluation in real-world environments: an exploratory investigation with professional surgeons.Frontiers in network physiology · 2025Article
- Temporal interaction information and laser evoked responses: preliminary results in fibromyalgia patients with small fibers pathology.Frontiers in network physiology · 2025Article
- Complexity synchronization analysis of neurophysiological data: Theory and methods.Frontiers in network physiology · 2025Article
- Information dynamics of in silico EEG Brain Waves: Insights into oscillations and functions.PLoS computational biology · 2024Article
- Article
- Response inhibition in premotor cortex corresponds to a complex reshuffle of the mesoscopic information network.Network neuroscience (Cambridge, Mass.) · 2024Article
- Nonadditive Entropy Application to Detrended Force Sensor Data to Indicate Balance Disorder of Patients with Vestibular System Dysfunction.Entropy (Basel, Switzerland) · 2023Article
- Information-Theoretic Analysis of Cardio-Respiratory Interactions in Heart Failure Patients: Effects of Arrhythmias and Cardiac Resynchronization Therapy.Entropy (Basel, Switzerland) · 2023Article
- Schizophrenia MEG Network Analysis Based on Kernel Granger Causality.Entropy (Basel, Switzerland) · 2023Article
- Inferring connectivity of an oscillatory network via the phase dynamics reconstruction.Frontiers in network physiology · 2023Review
- Information theoretic evidence for layer- and frequency-specific changes in cortical information processing under anesthesia.PLoS computational biology · 2023Article
- Feasibility of Ultra-Short-Term Analysis of Heart Rate and Systolic Arterial Pressure Variability at Rest and during Stress via Time-Domain and Entropy-Based Measures.Sensors (Basel, Switzerland) · 2022Article
- Measuring habitat complexity and spatial heterogeneity in ecology.Ecology letters · 2022Review
- The Structure of Chaos: An Empirical Comparison of Fractal Physiology Complexity Indices Using NeuroKit2.Entropy (Basel, Switzerland) · 2022Article
- Information Dynamics of Electric Field Intensity before and during the COVID-19 Pandemic.Entropy (Basel, Switzerland) · 2022Article
3 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Entropy measures are widely applied to quantify the complexity of dynamical systems in diverse fields. However, the practical application of entropy methods is challenging, due to the variety of entropy measures and estimators and the complexity of real-world time series, including nonstationarities and long-range correlations (LRC). We conduct a systematic study on the performance, bias, and limitations of three basic measures (entropy, conditional entropy, information storage) and three traditionally used estimators (linear, kernel, nearest neighbor). We investigate the dependence of entropy measures on estimator- and process-specific parameters, and we show the effects of three types of nonstationarities due to artifacts (trends, spikes, local variance change) in simulations of stochastic autoregressive processes. We also analyze the impact of LRC on the theoretical and estimated values of entropy measures. Finally, we apply entropy methods on heart rate variability data from subjects in different physiological states and clinical conditions. We find that entropy measures can only differentiate changes of specific types in cardiac dynamics and that appropriate preprocessing is vital for correct estimation and interpretation. Demonstrating the limitations of entropy methods and shedding light on how to mitigate bias and provide correct interpretations of results, this work can serve as a comprehensive reference for the application of entropy methods and the evaluation of existing studies.
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Registered trials
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