[PDF][PDF] Standard multiscale entropy reflects spectral power at mismatched temporal scales: 1 What's signal irregularity got to do with it? 2
JQ Kosciessa123, NA Kloosterman12, DD Garrett12 - 2019 - researchgate.net
34 Highlights 35• Multiscale sample entropy (MSE) links to spectral power via an internal
similarity criterion. 36• Counterintuitively, traditional MSE implementations lead to slow …
similarity criterion. 36• Counterintuitively, traditional MSE implementations lead to slow …
[PDF][PDF] Multiscale entropy analysis (MSE)
1 Background Multiscale entropy (MSE) analysis [1, 2] is a new method of measuring the
complexity of finite length time series. This computational tool can be applied both to …
complexity of finite length time series. This computational tool can be applied both to …
Standard multiscale entropy reflects neural dynamics at mismatched temporal scales: What's signal irregularity got to do with it?
JQ Kosciessa, NA Kloosterman… - PLoS computational …, 2020 - journals.plos.org
Multiscale Entropy (MSE) is used to characterize the temporal irregularity of neural time
series patterns. Due to its' presumed sensitivity to non-linear signal characteristics, MSE is …
series patterns. Due to its' presumed sensitivity to non-linear signal characteristics, MSE is …
Coarse-graining approaches in univariate multiscale sample and dispersion entropy
H Azami, J Escudero - Entropy, 2018 - mdpi.com
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 …
recent years. This is often done using the framework of Multiscale Entropy, which entails two …
Multivariate multiscale cosine similarity entropy
H Xiao, T Chanwimalueang… - ICASSP 2022-2022 IEEE …, 2022 - ieeexplore.ieee.org
The rapid development in sensor technology has made it convenient to acquire data from
multi-channel systems but has also high-lighted the need for the analysis of nonlinear …
multi-channel systems but has also high-lighted the need for the analysis of nonlinear …
Multiscale entropy profiling to estimate complexity of heart rate dynamics
In the analysis of signal regularity from a physiological system such as the human heart,
Approximate entropy (HA) and Sample entropy (HS) have been the most popular statistical …
Approximate entropy (HA) and Sample entropy (HS) have been the most popular statistical …
Multiscale sample entropy of cardiovascular signals: does the choice between fixed-or varying-tolerance among scales influence its evaluation and interpretation?
P Castiglioni, P Coruzzi, M Bini, G Parati, A Faini - Entropy, 2017 - mdpi.com
Multiscale entropy (MSE) quantifies the cardiovascular complexity evaluating Sample
Entropy (SampEn) on coarse-grained series at increasing scales τ. Two approaches exist …
Entropy (SampEn) on coarse-grained series at increasing scales τ. Two approaches exist …
Dispersion entropy: A measure for time-series analysis
M Rostaghi, H Azami - IEEE Signal Processing Letters, 2016 - ieeexplore.ieee.org
One of the most powerful tools to assess the dynamical characteristics of time series is
entropy. Sample entropy (SE), though powerful, is not fast enough, especially for long …
entropy. Sample entropy (SE), though powerful, is not fast enough, especially for long …
A novel multivariate sample entropy algorithm for modeling time series synchronization
Approximate and sample entropy (AE and SE) provide robust measures of the deterministic
or stochastic content of a time series (regularity), as well as the degree of structural richness …
or stochastic content of a time series (regularity), as well as the degree of structural richness …
Range entropy: A bridge between signal complexity and self-similarity
Approximate entropy (ApEn) and sample entropy (SampEn) are widely used for temporal
complexity analysis of real-world phenomena. However, their relationship with the Hurst …
complexity analysis of real-world phenomena. However, their relationship with the Hurst …