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Multivariate multiscale entropy for brain consciousness analysis [PDF]
2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2011 The recently introduced multiscale entropy (MSE) method accounts for long range correlations over multiple time scales and can therefore reveal the complexity of biological signals. The existing MSE algorithm deals with scalar time series whereas multivariate time series are common in experimental and biological systems.
Mosabber Uddin Ahmed +3 more
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Estimating perceived image complexity with Multiscale Entropy
, 2023 The perceived complexity of an image is an image feature that is related to aesthetic pleasure ratings and other subjective visual perceptions. In this study we develop a new computational measure which is largely based on multiscale entropy (MSE ...
Claudia Damiano +3 more
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Coarse-graining and the Haar wavelet transform for multiscale analysis
Bioelectronic Medicine, 2022 Background Multiscale entropy (MSE) has become increasingly common as a quantitative tool for analysis of physiological signals. The MSE computation involves first decomposing a signal into multiple sub-signal ‘scales’ using a coarse-graining algorithm ...
William J. Bosl +2 more
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IEEE Access, 2021 This paper considers the entropy based feature extraction method for the fault diagnosis of rolling bearings in automobile production line, where the fault information is difficult to identify due to the strong nonlinear and non-stationary ...
Chuang Liang, Changzheng Chen
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EntropyHub: An open-source toolkit for entropic time series analysis.
PLoS ONE, 2021 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.
Matthew W Flood, Bernd Grimm
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Entropy, 2018 The present study aimed at testing the hypothesis that application of multiscale cross-approximate entropy (MCAE) analysis in the study of nonlinear coupling behavior of two synchronized time series of different natures [i.e., R-R interval (RRI) and ...
Ming-Xia Xiao +4 more
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Learning Entropy: Multiscale Measure for Incremental Learning [PDF]
Entropy, 2013 First, this paper recalls a recently introduced method of adaptive monitoring of dynamical systems and presents the most recent extension with a multiscale-enhanced approach. Then, it is shown that this concept of real-time data monitoring establishes a novel non-Shannon and non-probabilistic concept of novelty quantification, i.e., Entropy of Learning,
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Applying Improved Multiscale Fuzzy Entropy for Feature Extraction of MI-EEG
Applied Sciences, 2017 Electroencephalography (EEG) is considered the output of a brain and it is a bioelectrical signal with multiscale and nonlinear properties. Motor Imagery EEG (MI-EEG) not only has a close correlation with the human imagination and movement intention but ...
Ming-ai Li +3 more
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Entropy, 2022 The extension of sample entropy methodologies to multivariate signals has received considerable attention, with traditional univariate entropy methods, such as sample entropy (SampEn) and fuzzy entropy (FuzzyEn), introduced to measure the complexity of ...
Hongjian Xiao +2 more
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Coarse-graining Approaches in Univariate Multiscale Sample and Dispersion Entropy [PDF]
, 2018 The evaluation of complexity in univariate signals has attracted considerable attention in recent years. This is often done using the framework of Multiscale Entropy, which entails two basic steps: coarse-graining to consider multiple temporal scales ...
Hamed Azami +3 more
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