The Multiscale Entropy Algorithm and Its Variants: A Review

Multiscale entropy (MSE) is a widely used metric for characterizing the nonlinear dynamics of physiological processes. Significant variability, however, exists in the methodological approaches to MSE which may ultimately impact results and their interpretations. Using publications focused on balance-related center of pressure (COP) dynamics, we highlight sources of methodological heterogeneity that can impact study findings. Seventeen studies were systematically identified that employed MSE for characterizing COP displacement dynamics. We identified five key methodological procedures that varied significantly between studies: (1) data length; (2) frequencies of the COP dynamics analyzed; (3) sampling rate; (4) point matching tolerance and sequence length; and (5) filtering of displacement changes from drifts, fidgets, and shifts. We discuss strengths and limitations of the various approaches employed and supply flowcharts to assist in the decision making process regarding each of these procedures. Our guidelines are intended to more broadly inform the design and analysis of future studies employing MSE for continuous time series, such as COP.

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“…A review of a number of other variants on MSE, each of which has their own strengths is provided in Humeau-Heurtier [39].…”

Section: Determining Required Data Lengthmentioning

confidence: 99%

Multiscale Entropy Analysis of Center-of-Pressure Dynamics in Human Postural Control: Methodological Considerations

Gow

Peng

Wayne

et al. 2015

Entropy

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“…This can be observed by listing the new entropybased algorithms that have recently been proposed to improve and extend one of the most well-known irregularity measures, sample entropy (SampEn 1D ) [1]: see, e.g., [2]- [6]. This growing interest is probably due to the ability of the entropy-based algorithms to analyze large sets of signals [3] and also to their ability -when associated with a multiscale approach -to give information on the system's complexity [3], [7].…”

Section: Introductionmentioning

confidence: 99%

Bidimensional Distribution Entropy to Analyze the Irregularity of Small-Sized Textures

Azami

Escudero

Humeau-Heurtier

2017

IEEE Signal Process. Lett.

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Abstract-Two-dimensional sample entropy (SampEn 2D ) has been recently proposed to quantify the irregularity of textures. However, when dealing with small-sized textures, SampEn 2D may lead to either undefined or unreliable values. Moreover, SampEn 2D is too slow for most real-time applications. To alleviate these deficiencies, we introduce bidimensional distribution entropy (DistrEn 2D ). We evaluate DistrEn 2D on both synthetic and real texture datasets. The results indicate that DistrEn 2D can detect different amounts of white Gaussian and salt and pepper noise, and discriminate periodic from synthesized textures. The results also show that DistrEn 2D distinguishes different kinds of textured surfaces. In addition, DistrEn 2D , unlike SampEn 2D , does not lead to undefined values. Moreover, DistrEn 2D is noticeably faster than SampEn 2D . Overall, DistrEn 2D -as an insensitive feature extraction method to rotation -is expected to be very useful for the analysis of real image textures.

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“…Application of entropy for analysis and classification of numerical algorithms and the corresponding computational complexities is discussed in [24,25]. Paper [26] brings a good review of various multi-scale entropy algorithms and some applications, too. Two-echelon supply chain games and their information characteristics, in particular, are discussed in [27].…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Operational Complexity of Supplier-Customer Systems Measured by Entropy—Case Studies

Lukáš

Hofman

2016

Entropy

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This paper discusses a unified entropy-based approach for the quantitative measurement of operational complexity of company supplier-customer relations. Classical Shannon entropy is utilized. Beside this quantification tool, we also explore the relations between Shannon entropy and (c,d)-entropy in more details. An analytic description of so called iso-quant curves is given, too. We present five case studies, albeit in an anonymous setting, describing various details of general procedures for measuring the operational complexity of supplier-customer systems. In general, we assume a problem-oriented database exists, which contains detailed records of all product forecasts, orders and deliveries both in quantity and time, scheduled and realized, too. Data processing detects important flow variations both in volumes and times, e.g., order-forecast, delivery-order, and actual production-scheduled one. The unifying quantity used for entropy computation is the time gap between actual delivery time and order issue time, which is nothing else but a lead time in inventory control models. After data consistency checks, histograms and empirical distribution functions are constructed. Finally, the entropy, information-theoretic measure of supplier-customer operational complexity, is calculated. Basic steps of the algorithm are mentioned briefly, too. Results of supplier-customer system analysis from selected Czech small and medium-sized enterprises (SMEs) are presented in various computational and managerial decision making details. An enterprise is ranked as SME one, if it has at most 250 employees and its turnover does not exceed 50 million USD per year, or its balance sheet total does not exceed 43 million USD per year, alternatively.

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The Multiscale Entropy Algorithm and Its Variants: A Review

Cited by 265 publications

References 42 publications

Multiscale Entropy Analysis of Center-of-Pressure Dynamics in Human Postural Control: Methodological Considerations

Multiscale Entropy Analysis of Center-of-Pressure Dynamics in Human Postural Control: Methodological Considerations

Bidimensional Distribution Entropy to Analyze the Irregularity of Small-Sized Textures

Operational Complexity of Supplier-Customer Systems Measured by Entropy—Case Studies

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