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.
Jianzhong Zhou +2 more
exaly +5 more sources
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, Wenpo Yao
exaly +6 more sources
Bearing Fault Diagnosis Based on Multiscale Permutation Entropy and Support Vector Machine [PDF]
Entropy, 2012 Bearing fault diagnosis has attracted significant attention over the past few decades. It consists of two major parts: vibration signal feature extraction and condition classification for the extracted features.
Shuen-De Wu +2 more
exaly +6 more sources
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.
Tiangang Zou, Yongbo Li, Gui Lin
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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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Entropy, 2023 The study focuses on the fault signals of rolling bearings, which are characterized by nonlinearity, periodic impact, and low signal-to-noise ratio.
Qiang Yuan +5 more
exaly +4 more sources
On the Genuine Relevance of the Data-Driven Signal Decomposition-Based Multiscale Permutation Entropy. [PDF]
Entropy (Basel), 2022 Ordinal pattern-based approaches have great potential to capture intrinsic structures of dynamical systems, and therefore, they continue to be developed in various research fields. Among these, the permutation entropy (PE), defined as the Shannon entropy of ordinal probabilities, is an attractive time series complexity measure.
Jabloun M, Ravier P, Buttelli O.
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 +6 more sources
IEEE Access, 2019 Fault diagnosis of rotating machinery is vital to identify incipient failures and avoid unexpected downtime in industrial systems. This paper proposes a new rolling bearing fault diagnosis method by integrating the fine-to-coarse multiscale permutation ...
Zhiqiang Huo +2 more
exaly +6 more sources