A Comparison of Multiscale Permutation Entropy Measures in On-Line Depth of Anesthesia Monitoring. [PDF]
PLoS ONE, 2016 OBJECTIVE:Multiscale permutation entropy (MSPE) is becoming an interesting tool to explore neurophysiological mechanisms in recent years. In this study, six MSPE measures were proposed for on-line depth of anesthesia (DoA) monitoring to quantify the ...
Cui Su +5 more
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Fault Diagnosis for Rolling Element Bearings Based on Feature Space Reconstruction and Multiscale Permutation Entropy [PDF]
Entropy, 2019 Aimed at distinguishing different fault categories of severity of rolling bearings, a novel method based on feature space reconstruction and multiscale permutation entropy is proposed in the study.
Weibo Zhang, Jianzhong Zhou
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Multiscale Weighted Permutation Entropy Analysis of Schizophrenia Magnetoencephalograms [PDF]
Entropy, 2022 Schizophrenia is a neuropsychiatric disease that affects the nonlinear dynamics of brain activity. The primary objective of this study was to explore the complexity of magnetoencephalograms (MEG) in patients with schizophrenia.
Dengxuan Bai +3 more
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Composite Multiscale Transition Permutation Entropy-Based Fault Diagnosis of Bearings
Sensors, 2022 When considering the transition probability matrix of ordinal patterns, transition permutation entropy (TPE) can effectively extract fault features by quantifying the irregularity and complexity of signals.
Jing Guo +4 more
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Multiscale permutation Rényi entropy and its application for EEG signals. [PDF]
PLoS ONE, 2018 There is considerable interest in analyzing the complexity of electroencephalography (EEG) signals. However, some traditional complexity measure algorithms only quantify the complexities of signals, but cannot discriminate different signals very well. To
Yinghuang Yin, Kehui Sun, Shaobo He
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Multiscale Permutation Entropy Based Rolling Bearing Fault Diagnosis [PDF]
Shock and Vibration, 2014 A new rolling bearing fault diagnosis approach based on multiscale permutation entropy (MPE), Laplacian score (LS), and support vector machines (SVMs) is proposed in this paper.
Jinde Zheng, Junsheng Cheng, Yu Yang
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Fault Diagnosis for Rotating Machinery Using Multiscale Permutation Entropy and Convolutional Neural Networks. [PDF]
Entropy (Basel), 2020 In view of the limitations of existing rotating machine fault diagnosis methods in single-scale signal analysis, a fault diagnosis method based on multi-scale permutation entropy (MPE) and multi-channel fusion convolutional neural networks (MCFCNN) is proposed.
Li H +5 more
europepmc +5 more sources
Optimized Dynamic Mode Decomposition via Non-Convex Regularization and Multiscale Permutation Entropy. [PDF]
Entropy (Basel), 2018 Dynamic mode decomposition (DMD) is essentially a hybrid algorithm based on mode decomposition and singular value decomposition, and it inevitably inherits the drawbacks of these two algorithms, including the selection strategy of truncated rank order and wanted mode components.
Dang Z, Lv Y, Li Y, Yi C.
europepmc +5 more sources
On the Statistical Properties of Multiscale Permutation Entropy: Characterization of the Estimator's Variance. [PDF]
Entropy (Basel), 2019 Permutation Entropy (PE) and Multiscale Permutation Entropy (MPE) have been extensively used in the analysis of time series searching for regularities. Although PE has been explored and characterized, there is still a lack of theoretical background regarding MPE. Therefore, we expand the available MPE theory by developing an explicit expression for the
Dávalos A +3 more
europepmc +5 more sources
Improvement of Statistical Performance of Ordinal Multiscale Entropy Techniques Using Refined Composite Downsampling Permutation Entropy [PDF]
Entropy, 2020 Multiscale Permutation Entropy (MPE) analysis is a powerful ordinal tool in the measurement of information content of time series. MPE refinements, such as Composite MPE (cMPE) and Refined Composite MPE (rcMPE), greatly increase the precision of the ...
Antonio Dávalos +3 more
doaj +3 more sources