ArticlePloS one2021
EntropyHub: An open-source toolkit for entropic time series analysis.
Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers.
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Who cites it
43 citing papers in PubMed.
- Exploratory Non-invasive Estimation of Cervical Dilation from Electrohysterography and Maternal Data: A Machine-Learning Proof-of-Concept Study.Annals of biomedical engineering · 2026Article
- Associations between pollutants, the regularity of daily sex hormone patterns, and hormone profiles over a complete menstrual cycle.Reproductive toxicology (Elmsford, N.Y.) · 2026Article
- Electroencephalography-Based Clustering Reveals Robust Neurophysiological Subtypes in Parkinson's Disease.Movement disorders : official journal of the Movement Disorder Society · 2026Article
- Sleep Deprivation in Mice: Looking Beyond the Slow Wave Rebound.Journal of sleep research · 2026Article
- Temporal Properties of Cardiorespiratory Coupling in Patients with Heart Failure During the Circadian Cycle.Entropy (Basel, Switzerland) · 2026Article
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- Monocytes are biological sensors of aging and frailty in humans.bioRxiv : the preprint server for biology · 2026Article
- Complexity Analysis of Skin Nerve Activity for Quantitative Assessment of Acute Sympathetic Nervous System Activation.Sensors (Basel, Switzerland) · 2026Article
- Regional and cannabis-related differences in prefrontal multiscale entropy of resting-state EEG.Clinical neurophysiology practice · 2026Article
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- Quantifying emotion-dependent brain-eye interactions during audiovisual emotional stimulation.Frontiers in systems neuroscience · 2026Article
- EEG entropy modulation as a biomarker of emotion regulation and resilience.IBRO neuroscience reports · 2025Review
- Measuring associations between hormonal entropy, the prevalence of vasomotor symptoms, and menstrual dynamics.American journal of physiology. Endocrinology and metabolism · 2025Article
- Using a Composite Summary of Daily Sex Hormones to Gauge Time Until Menopause: A Focus on Pregnanediol Glucuronide (PDG).The Journal of clinical endocrinology and metabolism · 2025Article
- Cognitive brain lateralization through neurovascular coupling in healthy subjects: A statistical complexity analysis.Physiological reports · 2025Article
- Development of Non-Invasive Continuous Glucose Prediction Models Using Multi-Modal Wearable Sensors in Free-Living Conditions.Sensors (Basel, Switzerland) · 2025Article
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- Using Electroencephalography to Advance Mindfulness Science: A Survey of Emerging Methods and Approaches.Biological psychiatry. Cognitive neuroscience and neuroimaging · 2025Review
- Detection and Severity Classification of Sleep Apnea Using Continuous Wearable SpOSensors (Basel, Switzerland) · 2025Article
- Single-neuron spiking variability in hippocampus dynamically tracks sensory content during memory formation in humans.Nature communications · 2025Article
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2 authors.
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No grant is acknowledged in the PubMed record.
Abstract
An increasing number of studies across many research fields from biomedical engineering to finance are employing measures of entropy to quantify the regularity, variability or randomness of time series and image data. Entropy, as it relates to information theory and dynamical systems theory, can be estimated in many ways, with newly developed methods being continuously introduced in the scientific literature. Despite the growing interest in entropic time series and image analysis, there is a shortage of validated, open-source software tools that enable researchers to apply these methods. To date, packages for performing entropy analysis are often run using graphical user interfaces, lack the necessary supporting documentation, or do not include functions for more advanced entropy methods, such as cross-entropy, multiscale cross-entropy or bidimensional entropy. In light of this, this paper introduces EntropyHub, an open-source toolkit for performing entropic time series analysis in MATLAB, Python and Julia. EntropyHub (version 0.1) provides an extensive range of more than forty functions for estimating cross-, multiscale, multiscale cross-, and bidimensional entropy, each including a number of keyword arguments that allows the user to specify multiple parameters in the entropy calculation. Instructions for installation, descriptions of function syntax, and examples of use are fully detailed in the supporting documentation, available on the EntropyHub website- www.EntropyHub.xyz. Compatible with Windows, Mac and Linux operating systems, EntropyHub is hosted on GitHub, as well as the native package repository for MATLAB, Python and Julia, respectively. The goal of EntropyHub is to integrate the many established entropy methods into one complete resource, providing tools that make advanced entropic time series analysis straightforward and reproducible.
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