ArticleEntropy (Basel, Switzerland)2024
Multi-Frequency Entropy for Quantifying Complex Dynamics and Its Application on EEG Data.
Article in Entropy (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- 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
- A Novel Multi-Scale Entropy Approach for EEG-Based Lie Detection with Channel Selection.Entropy (Basel, Switzerland) · 2025Article
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Authors and funding
11 authors.
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Abstract
Multivariate entropy algorithms have proven effective in the complexity dynamic analysis of electroencephalography (EEG) signals, with researchers commonly configuring the variables as multi-channel time series. However, the complex quantification of brain dynamics from a multi-frequency perspective has not been extensively explored, despite existing evidence suggesting interactions among brain rhythms at different frequencies. In this study, we proposed a novel algorithm, termed multi-frequency entropy (mFreEn), enhancing the capabilities of existing multivariate entropy algorithms and facilitating the complexity study of interactions among brain rhythms of different frequency bands. Firstly, utilizing simulated data, we evaluated the mFreEn's sensitivity to various noise signals, frequencies, and amplitudes, investigated the effects of parameters such as the embedding dimension and data length, and analyzed its anti-noise performance. The results indicated that mFreEn demonstrated enhanced sensitivity and reduced parameter dependence compared to traditional multivariate entropy algorithms. Subsequently, the mFreEn algorithm was applied to the analysis of real EEG data. We found that mFreEn exhibited a good diagnostic performance in analyzing resting-state EEG data from various brain disorders. Furthermore, mFreEn showed a good classification performance for EEG activity induced by diverse task stimuli. Consequently, mFreEn provides another important perspective to quantify complex dynamics.
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