Multiscale Entropy: Recent Advances

Hu, Mengqi; Liang, Hualou

doi:10.1007/978-3-319-58709-7_4

Cited by 9 publications

(3 citation statements)

References 75 publications

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“…This allows straightforward comparisons of the MSE results and for discussing their interpretations related to AD. It should be noted that there are other new entropic metrics and modified variants in the MSE family (see [ 25 , 81 , 82 , 83 , 84 , 85 , 86 ] for recent review articles), such as multiscale dispersion entropy [ 87 , 88 ], multiscale fuzzy entropy [ 89 , 90 ], and multiscale fluctuation dispersion entropy [ 91 ]. Each has respective methodological assumptions and principles, strengths and advantages, and shortcomings and limitations, as recently well-discussed by Azami et al in [ 85 ].…”

Section: Discussionmentioning

confidence: 99%

Brain Dynamics Altered by Photic Stimulation in Patients with Alzheimer’s Disease and Mild Cognitive Impairment

Low

Chen

et al. 2021

Entropy

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Individuals with mild cognitive impairment (MCI) are at high risk of developing Alzheimer’s disease (AD). Repetitive photic stimulation (PS) is commonly used in routine electroencephalogram (EEG) examinations for rapid assessment of perceptual functioning. This study aimed to evaluate neural oscillatory responses and nonlinear brain dynamics under the effects of PS in patients with mild AD, moderate AD, severe AD, and MCI, as well as healthy elderly controls (HC). EEG power ratios during PS were estimated as an index of oscillatory responses. Multiscale sample entropy (MSE) was estimated as an index of brain dynamics before, during, and after PS. During PS, EEG harmonic responses were lower and MSE values were higher in the AD subgroups than in HC and MCI groups. PS-induced changes in EEG complexity were less pronounced in the AD subgroups than in HC and MCI groups. Brain dynamics revealed a “transitional change” between MCI and Mild AD. Our findings suggest a deficiency in brain adaptability in AD patients, which hinders their ability to adapt to repetitive perceptual stimulation. This study highlights the importance of combining spectral and nonlinear dynamical analysis when seeking to unravel perceptual functioning and brain adaptability in the various stages of neurodegenerative diseases.

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Section: Discussionmentioning

confidence: 99%

Brain Dynamics Altered by Photic Stimulation in Patients with Alzheimer’s Disease and Mild Cognitive Impairment

Low

Chen

et al. 2021

Entropy

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“…Motivated by MSEn, several modified multiple-scale entropy measures have been developed through various scaleextraction frameworks and notions of single-scale entropy algorithms [13], [14]. Multiple-scale entropy measures can be classified into three main groups according to their operational principles:…”

Section: E Multiple-scale Entropy Measuresmentioning

confidence: 99%

“…A modified entropy definition, named Multiscale Entropy (MSEn), was proposed to estimate entropy over a range of scales enabled by a coarse-graining procedure [12]. Since then, various definitions of scale-extraction mechanisms were proposed leading to an increasing number of multiple-scale entropy measures, in which single-scale entropy measures provide the basis of entropy estimation under the multiplescale framework [13], [14].…”

Section: Introductionmentioning

confidence: 99%

Entropy Measures in Machine Fault Diagnosis: Insights and Applications

Huo

Martínez-García

Zhang

et al. 2020

IEEE Trans. Instrum. Meas.

136

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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 diagnostic systems. The occurrence of failures in a machine will typically lead to non-linear characteristics in the measurements, caused by instantaneous variations, which can increase the complexity in the system response. Entropy measures are suitable to quantify such dynamic changes in the underlying process, distinguishing between different system conditions. However, notions of entropy are defined differently in various contexts (e.g., information theory and dynamical systems theory), which may confound researchers in the applied sciences. In this paper, we have systematically reviewed the theoretical development of some fundamental entropy measures and clarified the relations among them. Then, typical entropy-based applications of machine fault diagnostic systems are summarized. Further, insights into possible applications of the entropy measures are explained, as to where and how these measures can be useful towards future datadriven fault diagnosis methodologies. Finally, potential research trends in this area are discussed, with the intent of improving online entropy estimation and expanding its applicability to a wider range of intelligent fault diagnostic systems.

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