Results 31 to 40 of about 10,393 (254)
Multiscale based nonlinear dynamics analysis of heart rate variability signals.
PLoS ONE, 2020 Acceleration change index (ACI) is a fast and easy to understand heart rate variability (HRV) analysis approach used for assessing cardiac autonomic control of the nervous systems.
Syed Zaki Hassan Kazmi +5 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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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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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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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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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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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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Maximum Multiscale Entropy and Neural Network Regularization
CoRR, 2020 A well-known result across information theory, machine learning, and statistical physics shows that the maximum entropy distribution under a mean constraint has an exponential form called the Gibbs-Boltzmann distribution. This is used for instance in density estimation or to achieve excess risk bounds derived from single-scale entropy regularizers (Xu ...
Amir-Reza Asadi, Emmanuel Abbe
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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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