ArticleEntropy (Basel, Switzerland)2022
Estimating Permutation Entropy Variability via Surrogate Time Series.
Article in Entropy (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Robust circular cluster-based statistics for respiration-brain coupling.PLoS computational biology · 2026Article
- Frontal Cortex Entropy Derived From Resting-State fNIRS for Brain Age Prediction in Major Depressive Disorder.Depression and anxiety · 2026Article
- Stationarity assessment of resting state condition via permutation entropy on EEG recordings.Scientific reports · 2025Article
- Revisiting the Characterization of Resting Brain Dynamics with the Permutation Jensen-Shannon Distance.Entropy (Basel, Switzerland) · 2024Article
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Authors and funding
2 authors.
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Abstract
In the last decade permutation entropy (PE) has become a popular tool to analyze the degree of randomness within a time series. In typical applications, changes in the dynamics of a source are inferred by observing changes of PE computed on different time series generated by that source. However, most works neglect the crucial question related to the statistical significance of these changes. The main reason probably lies in the difficulty of assessing, out of a single time series, not only the PE value, but also its uncertainty. In this paper we propose a method to overcome this issue by using generation of surrogate time series. The analysis conducted on both synthetic and experimental time series shows the reliability of the approach, which can be promptly implemented by means of widely available numerical tools. The method is computationally affordable for a broad range of users.
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