Results 51 to 60 of about 389,267 (294)
Univariate and Multivariate Generalised Multiscale Entropy to Characterise EEG Signals in Alzheimer’s Disease [PDF]
, 2019 Alzheimer’s disease (AD) is a degenerative brain disorder leading to memory loss and changes in other cognitive abilities. The complexity of electroencephalogram (EEG) signals may help to characterise AD.
Simons, Samantha +3 more
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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
doaj +2 more sources
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 ...
Asadi, Amir R., Abbé, Emmanuel
openaire +2 more sources
, 2017 Please see the updated Matlab codes for "Refined Composite Multiscale Dispersion Entropy and its Application to Biomedical Signals" at https://doi.org/10.7488/ds/1982 . Multiscale entropy (MSE) is a widely-used tool for the analysis of biomedical signals.
Azami, Hamed, Escudero, Javier
core +1 more source
Multiscale entropy analysis of astronomical time series
, 2022 Context. The multiscale entropy assesses the complexity of a signal across different timescales. It originates from the biomedical domain and was recently successfully used to characterize light curves as part of a supervised machine learning framework ...
A. Tkachenko +3 more
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Jixie chuandong, 2021 When the gearbox fails， its vibration signal is unstable and nonlinear. The commonly applied gearbox fault diagnosis methods are almost all based on the single-channel vibration signal analysis，which is easy to cause the loss of fault information，so the ...
Fuming Zhou +4 more
doaj
Sensors, 2021 We propose a novel fault-diagnosis approach for rolling bearings by integrating variational mode decomposition (VMD), refined composite multiscale dispersion entropy (RCMDE), and support vector machine (SVM) optimized by a sparrow search algorithm (SSA).
Jie Lv +3 more
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Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective To determine whether myelin‐sensitive quantitative MRI reveals microstructural abnormalities in normal‐appearing cortex (NACtx) in myelin oligodendrocyte glycoprotein antibody–associated disease (MOGAD), indicating that conventional MRI underestimates remission residual cortical injury.
Valentina Camera +20 more
wiley +1 more source
Advanced Engineering Materials, EarlyView.Low‐cycle fatigue damage in Mn–Mo–Ni reactor pressure vessel steel is examined using a combined electron backscatter diffraction and positron annihilation lifetime spectroscopy approach. The study correlates texture evolution, dislocation substructure development, and vacancy‐type defect formation across uniform, necked, and fracture regions, providing
Apu Sarkar +2 more
wiley +1 more source