Brain entropy, fractal dimensions and predictability: A review of complexity measures for EEG in healthy and neuropsychiatric populations
There has been an increasing trend towards the use of complexity analysis in quantifying
neural activity measured by electroencephalography (EEG) signals. On top of revealing …
neural activity measured by electroencephalography (EEG) signals. On top of revealing …
The multiscale entropy algorithm and its variants: A review
A Humeau-Heurtier - Entropy, 2015 - mdpi.com
Multiscale entropy (MSE) analysis was introduced in the 2002 to evaluate the complexity of
a time series by quantifying its entropy over a range of temporal scales. The algorithm has …
a time series by quantifying its entropy over a range of temporal scales. The algorithm has …
Belief entropy-of-entropy and its application in the cardiac interbeat interval time series analysis
H Cui, L Zhou, Y Li, B Kang - Chaos, Solitons & Fractals, 2022 - Elsevier
How to measure the complexity of physiological signals in biological system is an open
problem. Various entropy algorithms have been presented, but most of them fail to account …
problem. Various entropy algorithms have been presented, but most of them fail to account …
COVID-19 and financial market efficiency: Evidence from an entropy-based analysis
This study assesses the market efficiency of S&P 500 Index, gold, Bitcoin and US Dollar
Index during the extreme event of COVID-19 pandemic. Market efficiency is estimated by a …
Index during the extreme event of COVID-19 pandemic. Market efficiency is estimated by a …
Optimized multivariate multiscale slope entropy for nonlinear dynamic analysis of mechanical signals
Y Li, B Tang, S Jiao, Y Zhou - Chaos, Solitons & Fractals, 2024 - Elsevier
Slope entropy (SloEn) is an effective nonlinear dynamic method to represent the complexity
of time series, which has been extensively applied to various mechanical signal processing …
of time series, which has been extensively applied to various mechanical signal processing …
On the use of approximate entropy and sample entropy with centre of pressure time-series
Abstract Background Approximate entropy (ApEn) and sample entropy (SampEn) have been
previously used to quantify the regularity in centre of pressure (COP) time-series in different …
previously used to quantify the regularity in centre of pressure (COP) time-series in different …
Entropy measures in machine fault diagnosis: Insights and applications
Entropy, as a complexity measure, has been widely applied for time series analysis. One
preeminent example is the design of machine condition monitoring and industrial fault …
preeminent example is the design of machine condition monitoring and industrial fault …
Multivariate multi-scale permutation entropy for complexity analysis of Alzheimer's disease EEG
An original multivariate multi-scale methodology for assessing the complexity of
physiological signals is proposed. The technique is able to incorporate the simultaneous …
physiological signals is proposed. The technique is able to incorporate the simultaneous …
Refined multiscale fuzzy entropy based on standard deviation for biomedical signal analysis
Multiscale entropy (MSE) has been a prevalent algorithm to quantify the complexity of
biomedical time series. Recent developments in the field have tried to alleviate the problem …
biomedical time series. Recent developments in the field have tried to alleviate the problem …
Tunable-Q wavelet transform based multivariate sub-band fuzzy entropy with application to focal EEG signal analysis
This paper analyses the complexity of multivariate electroencephalogram (EEG) signals in
different frequency scales for the analysis and classification of focal and non-focal EEG …
different frequency scales for the analysis and classification of focal and non-focal EEG …