Journal articles: 'Multiscale Entropy'

1

Starck, J. L., and F. Murtagh. "Multiscale entropy filtering." Signal Processing 76, no. 2 (July 1999): 147–65. http://dx.doi.org/10.1016/s0165-1684(99)00005-5.

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BAR-YAM, Y. "MULTISCALE COMPLEXITY/ENTROPY." Advances in Complex Systems 07, no. 01 (March 2004): 47–63. http://dx.doi.org/10.1142/s0219525904000068.

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We discuss the role of scale dependence of entropy/complexity and its relationship to component interdependence. The complexity as a function of scale of observation is expressed in terms of subsystem entropies for a system having a description in terms of variables that have the same a priori scale. The sum of the complexity over all scales is the same for any system with the same number of underlying degrees of freedom (variables), even though the complexity at specific scales differs due to the organization/interdependence of these degrees of freedom. This reflects a tradeoff of complexity at different scales of observation. Calculation of this complexity for a simple frustrated system reveals that it is possible for the complexity to be negative. This is consistent with the possibility that observations of a system that include some errors may actually cause, on average, negative knowledge, i.e. incorrect expectations.

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Xu, Fan, Peter Wai Tat TSE, Yan-Jun Fang, and Jia-Qi Liang. "A fault diagnosis method combined with compound multiscale permutation entropy and particle swarm optimization–support vector machine for roller bearings diagnosis." Proceedings of the Institution of Mechanical Engineers, Part J: Journal of Engineering Tribology 233, no. 4 (July 20, 2018): 615–27. http://dx.doi.org/10.1177/1350650118788929.

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A method based on compound multiscale permutation entropy, support vector machine, and particle swarm optimization for roller bearings fault diagnosis was presented in this study. Firstly, the roller bearings vibration signals under different conditions were decomposed into permutation entropy values by the multiscale permutation entropy and compound multiscale permutation entropy methods. The compound multiscale permutation entropy model combined the different graining sequence information under each scale factor. The average value of each scale factor was regarded as the final entropy value in the compound multiscale permutation entropy model. The compound multiscale permutation entropy model suppressed the shortcomings of poor stability caused by the length of the original signals in the multiscale permutation entropy model. Validity and accuracy are considered in the numerical experiments, and then compared with the computational efficiency of the multiscale permutation entropy method. Secondly, the entropy values of the multiscale permutation entropy/compound multiscale permutation entropy under different scales are regarded as the input of the particle swarm optimization–support vector machine models for fulfilling the fault identification, the classification accuracy is used to verify the effectiveness of the multiscale permutation entropy/compound multiscale permutation entropy with particle swarm optimization–support vector machine. Finally, the experimental results show that the classification accuracy of the compound multiscale permutation entropy model is higher than that of the multiscale permutation entropy.

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Ahmed, Mosabber Uddin, and Danilo P. Mandic. "Multivariate Multiscale Entropy Analysis." IEEE Signal Processing Letters 19, no. 2 (February 2012): 91–94. http://dx.doi.org/10.1109/lsp.2011.2180713.

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Humeau-Heurtier, Anne, Chiu-Wen Wu, and Shuen-De Wu. "Refined Composite Multiscale Permutation Entropy to Overcome Multiscale Permutation Entropy Length Dependence." IEEE Signal Processing Letters 22, no. 12 (December 2015): 2364–67. http://dx.doi.org/10.1109/lsp.2015.2482603.

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Grmela, Miroslav, Michal Pavelka, Václav Klika, Bing-Yang Cao, and Nie Bendian. "Entropy and Entropy Production in Multiscale Dynamics." Journal of Non-Equilibrium Thermodynamics 44, no. 3 (July 26, 2019): 217–33. http://dx.doi.org/10.1515/jnet-2018-0059.

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Abstract Heat conduction is investigated on three levels: equilibrium, Fourier, and Cattaneo. The Fourier level is either the point of departure for investigating the approach to equilibrium or the final stage in the investigation of the approach from the Cattaneo level. Both investigations bring to the Fourier level an entropy and a thermodynamics. In the absence of external and internal influences preventing the approach to equilibrium the entropy that arises in the latter investigation is the production of the classical entropy that arises in the former investigation. If the approach to equilibrium is prevented, then the entropy that arises in the investigation of the approach from the Cattaneo level to the Fourier level still brings to the Fourier level the entropy and the thermodynamics even if the classical entropy and the classical thermodynamics are absent. We also note that vanishing total entropy production as a characterization of equilibrium state is insufficient.

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Li Peng, Liu Cheng-Yu, Li Li-Ping, Ji Li-Zhen, Yu Shou-Yuan, and Liu Chang-Chun. "Multiscale multivariate fuzzy entropy analysis." Acta Physica Sinica 62, no. 12 (2013): 120512. http://dx.doi.org/10.7498/aps.62.120512.

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Starck, Jean-Luc, and Eric Pantin. "Multiscale maximum entropy images restoration." Vistas in Astronomy 40, no. 4 (January 1996): 563–69. http://dx.doi.org/10.1016/s0083-6656(96)00042-6.

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9

Humeau-Heurtier, Anne. "Multivariate Generalized Multiscale Entropy Analysis." Entropy 18, no. 11 (November 17, 2016): 411. http://dx.doi.org/10.3390/e18110411.

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Wang, Xianzhi, Shubin Si, Yongbo Li, and Xiaoqiang Du. "An integrated method based on refined composite multivariate hierarchical permutation entropy and random forest and its application in rotating machinery." Journal of Vibration and Control 26, no. 3-4 (November 5, 2019): 146–60. http://dx.doi.org/10.1177/1077546319877711.

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Fault feature extraction of rotating machinery is crucial and challenging due to its nonlinear and nonstationary characteristics. In order to resolve this difficulty, a quality nonlinear fault feature extraction method is required. Hierarchical permutation entropy has been proven to be a promising nonlinear feature extraction method for fault diagnosis of rotating machinery. Compared with multiscale permutation entropy, hierarchical permutation entropy considers the fault information hidden in both high frequency and low frequency components. However, hierarchical permutation entropy still has some shortcomings, such as poor statistical stability for short time series and inability of analyzing multichannel signals. To address such disadvantages, this paper proposes a new entropy method, called refined composite multivariate hierarchical permutation entropy. Refined composite multivariate hierarchical permutation entropy can extract rich fault information hidden in multichannel signals synchronously. Based on refined composite multivariate hierarchical permutation entropy and random forest, a novel fault diagnosis framework is proposed in this paper. The effectiveness of the proposed method is validated using experimental and simulated signals. The results demonstrate that the proposed method outperforms multivariate multiscale fuzzy entropy, refined composite multivariate multiscale fuzzy entropy, multivariate multiscale sample entropy, multivariate multiscale permutation entropy, multivariate hierarchical permutation entropy, and composite multivariate hierarchical permutation entropy in recognizing the different faults of rotating machinery.

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Wu, Zhiyong, and Wei Zhang. "Fractional Refined Composite Multiscale Fuzzy Entropy of International Stock Indices." Entropy 21, no. 9 (September 19, 2019): 914. http://dx.doi.org/10.3390/e21090914.

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Fractional refined composite multiscale fuzzy entropy (FRCMFE), which aims to relieve the large fluctuation of fuzzy entropy (FuzzyEn) measure and significantly discriminate different short-term financial time series with noise, is proposed to quantify the complexity dynamics of the international stock indices in the paper. To comprehend the FRCMFE, the complexity analyses of Gaussian white noise with different signal lengths, the random logarithmic returns and volatility series of the international stock indices are comparatively performed with multiscale fuzzy entropy (MFE), composite multiscale fuzzy entropy (CMFE) and refined composite multiscale fuzzy entropy (RCMFE). The empirical results show that the FRCMFE measure outperforms the traditional methods to some extent.

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Nosonovsky, Michael, and Sven Esche. "A Paradox of Decreasing Entropy in Multiscale Monte Carlo Grain Growth Simulations." Entropy 10, no. 2 (June 16, 2008): 49–54. http://dx.doi.org/10.3390/entropy-e10020049.

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Rizal, Achmad, Risanuri Hidayat, and Hanung Adi Nugroho. "Comparison of Multiscale Entropy for Lung Sound Classification." Indonesian Journal of Electrical Engineering and Computer Science 12, no. 3 (December 1, 2018): 984. http://dx.doi.org/10.11591/ijeecs.v12.i3.pp984-994.

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Lung sound is a biological signal used to determine the health level of the respiratory tract. Various digital signal processing techniques have been developed for the automatic lung sound classification. Entropy is one of the parameters used to measure the biomedical signal complexity. Multiscale entropy is introduced to measure the entropy of a signal at a particular scale range. Over time, various multiscale entropy techniques are used to measure the signal complexity on biological signal and other physical signals. In this paper, a number of multiscale entropy techniques for the lung sound classification are discussed. The results showed that Multiscale Permutation Entropy (MPE) could produce the highest accuracy of 97.98% for five classes of lung sound data. Results achieved for the scale 1-10 producing ten features for each lung sound data. This result is better than other seven entropies. The use of Permutation entropy (PE) on a multiscale scheme was to obtain a better accuracy compared to PE on one scale only

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Jamin, Antoine, and Anne Humeau-Heurtier. "(Multiscale) Cross-Entropy Methods: A Review." Entropy 22, no. 1 (December 29, 2019): 45. http://dx.doi.org/10.3390/e22010045.

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Cross-entropy was introduced in 1996 to quantify the degree of asynchronism between two time series. In 2009, a multiscale cross-entropy measure was proposed to analyze the dynamical characteristics of the coupling behavior between two sequences on multiple scales. Since their introductions, many improvements and other methods have been developed. In this review we offer a state-of-the-art on cross-entropy measures and their multiscale approaches.

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Ye, Maoyou, Xiaoan Yan, and Minping Jia. "Rolling Bearing Fault Diagnosis Based on VMD-MPE and PSO-SVM." Entropy 23, no. 6 (June 16, 2021): 762. http://dx.doi.org/10.3390/e23060762.

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The goal of the paper is to present a solution to improve the fault detection accuracy of rolling bearings. The method is based on variational mode decomposition (VMD), multiscale permutation entropy (MPE) and the particle swarm optimization-based support vector machine (PSO-SVM). Firstly, the original bearing vibration signal is decomposed into several intrinsic mode functions (IMF) by using the VMD method, and the feature energy ratio (FER) criterion is introduced to reconstruct the bearing vibration signal. Secondly, the multiscale permutation entropy of the reconstructed signal is calculated to construct multidimensional feature vectors. Finally, the constructed multidimensional feature vector is fed into the PSO-SVM classification model for automatic identification of different fault patterns of the rolling bearing. Two experimental cases are adopted to validate the effectiveness of the proposed method. Experimental results show that the proposed method can achieve a higher identification accuracy compared with some similar available methods (e.g., variational mode decomposition-based multiscale sample entropy (VMD-MSE), variational mode decomposition-based multiscale fuzzy entropy (VMD-MFE), empirical mode decomposition-based multiscale permutation entropy (EMD-MPE) and wavelet transform-based multiscale permutation entropy (WT-MPE)).

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Dong, Keqiang, and Xiaofang Zhang. "Multiscale Fractional Cumulative Residual Entropy of Higher-Order Moments for Estimating Uncertainty." Fluctuation and Noise Letters 19, no. 04 (July 23, 2020): 2050038. http://dx.doi.org/10.1142/s0219477520500388.

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The fractional cumulative residual entropy is not only a powerful tool for the analysis of complex system, but also a promising way to analyze time series. In this paper, we present an approach to measure the uncertainty of non-stationary time series named higher-order multiscale fractional cumulative residual entropy. We describe how fractional cumulative residual entropy may be calculated based on second-order, third-order, fourth-order statistical moments and multiscale method. The implementation of higher-order multiscale fractional cumulative residual entropy is illustrated with simulated time series generated by uniform distribution on [0, 1]. Finally, we present the application of higher-order multiscale fractional cumulative residual entropy in logistic map time series and stock markets time series, respectively.

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17

Humeau-Heurtier, Anne. "Multiscale Entropy Approaches and Their Applications." Entropy 22, no. 6 (June 10, 2020): 644. http://dx.doi.org/10.3390/e22060644.

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18

Yao Wen-Po, Liu Tie-Bing, Dai Jia-Fei, and Wang Jun. "Multiscale permutation entropy analysis of electroencephalogram." Acta Physica Sinica 63, no. 7 (2014): 078704. http://dx.doi.org/10.7498/aps.63.078704.

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19

Liu, Tiebing, Wenpo Yao, Min Wu, Zhaorong Shi, Jun Wang, and Xinbao Ning. "Multiscale permutation entropy analysis of electrocardiogram." Physica A: Statistical Mechanics and its Applications 471 (April 2017): 492–98. http://dx.doi.org/10.1016/j.physa.2016.11.102.

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Humeau-Heurtier, Anne. "Multivariate refined composite multiscale entropy analysis." Physics Letters A 380, no. 16 (April 2016): 1426–31. http://dx.doi.org/10.1016/j.physleta.2016.02.029.

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21

Costa, Madalena Damasio. "Multiscale entropy analysis and moving targets." Journal of Critical Care 25, no. 3 (September 2010): e8. http://dx.doi.org/10.1016/j.jcrc.2010.05.021.

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22

Lu, Yunfan, and Jun Wang. "Multivariate multiscale entropy of financial markets." Communications in Nonlinear Science and Numerical Simulation 52 (November 2017): 77–90. http://dx.doi.org/10.1016/j.cnsns.2017.04.028.

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23

Javaherian, Mohsen, and Saeid Mollaei. "Multiscale Entropy Analysis of Gravitational Waves." Advances in High Energy Physics 2021 (March 8, 2021): 1–7. http://dx.doi.org/10.1155/2021/6643546.

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The first gravitational-wave (GW) signal was detected in the year 2015 indicating tiny distortions of spacetime caused by accelerated masses. We focused on the GW signals consisting of a peak GW strain of 1.0 × 1 0 − 21 that shows merging pairs of large masses. We applied the generalized entropy known as multiscale entropy to the GW interval time series recorded by different observatories (H1, L1, and V1). This enables us to investigate the behavior of entropies on different scales as a method of studying complexity and organization. We found that the entropies of GW interval data with similar physical properties make the identical manner in different scales. Moreover, the results reveal that the signals collected by each observatory have approximately a similar trend in the multiscale analysis results. According to our findings, although different signals have different values for short-range correlations, the long-range correlations are not noticeable in most of them.

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Chu, Huiqin, Zhiyong Wu, and Wei Zhang. "Refined Composite Multivariate Multiscale Fractional Fuzzy Entropy: Measuring the Dynamical Complexity of Multichannel Financial Data." Complexity 2021 (September 9, 2021): 1–12. http://dx.doi.org/10.1155/2021/8173590.

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Refined composite multivariate multiscale fractional fuzzy entropy (RCmvMFFE), which aims to sensitively discriminate different short noisy multichannel financial data, is proposed as a new measure to quantify the complexity dynamics of multichannel time series in this work. To better comprehend the RCmvMFFE measure, the dynamical complexity analyses of multichannel synthetic dataset are comparatively studied with multivariate multiscale fuzzy entropy (mvMFE), refined composite multivariate multiscale fuzzy entropy (RCmvMFE), and refined composite multivariate multiscale fractional fuzzy entropy (RCmvMFFE). Then, these measures are firstly employed to explore actual multichannel financial index series to the best of our knowledge. The empirical analyses report that RCmvMFFE measure is able to deeply and sensitively dig up the market information hidden in the multichannel financial data and can better discriminate markets in different area compared to the traditional measures to some extent.

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Zhou, Fuming, Xiaoqiang Yang, Jinxing Shen, and Wuqiang Liu. "Fault Diagnosis of Hydraulic Pumps Using PSO-VMD and Refined Composite Multiscale Fluctuation Dispersion Entropy." Shock and Vibration 2020 (August 24, 2020): 1–13. http://dx.doi.org/10.1155/2020/8840676.

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Multiscale fluctuation dispersion entropy (MFDE) has been proposed to measure the dynamic features of complex signals recently. Compared with multiscale sample entropy (MSE) and multiscale fuzzy entropy (MFE), MFDE has higher calculation efficiency and better performance to extract fault features. However, when conducting multiscale analysis, as the scale factor increases, MFDE will become unstable. To solve this problem, refined composite multiscale fluctuation dispersion entropy (RCMFDE) is proposed and used to improve the stability of MFDE. And a new fault diagnosis method for hydraulic pumps using particle swarm optimization variational mode decomposition (PSO-VMD) and RCMFDE is proposed in this paper. Firstly, PSO-VMD is adopted to process the original vibration signals of hydraulic pumps, and the appropriate components are selected and reconstructed to get the denoised vibration signals. Then, RCMFDE is adopted to extract fault information. Finally, particle swarm optimization support vector machine (PSO-SVM) is adopted to distinguish different work states of hydraulic pumps. The experiments prove that the proposed method has higher fault recognition accuracy in comparison with MSE, MFE, and MFDE.

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Choi, Young-Seok. "Information-Theoretical Quantifier of Brain Rhythm Based on Data-Driven Multiscale Representation." BioMed Research International 2015 (2015): 1–8. http://dx.doi.org/10.1155/2015/830926.

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This paper presents a data-driven multiscale entropy measure to reveal the scale dependent information quantity of electroencephalogram (EEG) recordings. This work is motivated by the previous observations on the nonlinear and nonstationary nature of EEG over multiple time scales. Here, a new framework of entropy measures considering changing dynamics over multiple oscillatory scales is presented. First, to deal with nonstationarity over multiple scales, EEG recording is decomposed by applying the empirical mode decomposition (EMD) which is known to be effective for extracting the constituent narrowband components without a predetermined basis. Following calculation of Renyi entropy of the probability distributions of the intrinsic mode functions extracted by EMD leads to a data-driven multiscale Renyi entropy. To validate the performance of the proposed entropy measure, actual EEG recordings from ratsn=9experiencing 7 min cardiac arrest followed by resuscitation were analyzed. Simulation and experimental results demonstrate that the use of the multiscale Renyi entropy leads to better discriminative capability of the injury levels and improved correlations with the neurological deficit evaluation after 72 hours after cardiac arrest, thus suggesting an effective diagnostic and prognostic tool.Corrigendum to “Information-Theoretical Quantifier of Brain Rhythm Based on Data-Driven Multiscale Representation”

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Kazmi, Syed Zaki Hassan, Nazneen Habib, Rabia Riaz, Sanam Shahla Rizvi, Syed Ali Abbas, and Tae-Sun Chung. "Multiscale based nonlinear dynamics analysis of heart rate variability signals." PLOS ONE 15, no. 12 (December 17, 2020): e0243441. http://dx.doi.org/10.1371/journal.pone.0243441.

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Acceleration change index (ACI) is a fast and easy to understand heart rate variability (HRV) analysis approach used for assessing cardiac autonomic control of the nervous systems. The cardiac autonomic control of the nervous system is an example of highly integrated systems operating at multiple time scales. Traditional single scale based ACI did not take into account multiple time scales and has limited capability to classify normal and pathological subjects. In this study, a novel approach multiscale ACI (MACI) is proposed by incorporating multiple time scales for improving the classification ability of ACI. We evaluated the performance of MACI for classifying, normal sinus rhythm (NSR), congestive heart failure (CHF) and atrial fibrillation subjects. The findings reveal that MACI provided better classification between healthy and pathological subjects compared to ACI. We also compared MACI with other scale-based techniques such as multiscale entropy, multiscale permutation entropy (MPE), multiscale normalized corrected Shannon entropy (MNCSE) and multiscale permutation entropy (IMPE). The preliminary results show that MACI values are more stable and reliable than IMPE and MNCSE. The results show that MACI based features lead to higher classification accuracy.

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Marwaha, Puneeta, and Ramesh Kumar Sunkaria. "Cardiac variability time-series analysis by sample entropy and multiscale entropy." International Journal of Medical Engineering and Informatics 7, no. 1 (2015): 1. http://dx.doi.org/10.1504/ijmei.2015.066239.

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Guzmán-Vargas, L., A. Ramírez-Rojas, and F. Angulo-Brown. "Multiscale entropy analysis of electroseismic time series." Natural Hazards and Earth System Sciences 8, no. 4 (August 15, 2008): 855–60. http://dx.doi.org/10.5194/nhess-8-855-2008.

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Abstract. In this work we use the multiscale entropy method to analyse the variability of geo-electric time series monitored in two sites located in Mexico. In our analysis we consider a period of time from January 1995 to December 1995. We systematically calculate the sample entropy of electroseismic time series. Important differences in the entropy profile for several time scales are observed in records from the same station. In particular, a complex behaviour is observed in the vicinity of a M=7.4 EQ occurred on 14 September 1995. Besides, we also compare the changes in the entropy of the original data with their corresponding shuffled version.

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WANG, JING, PENGJIAN SHANG, XIAOJUN ZHAO, and JIANAN XIA. "MULTISCALE ENTROPY ANALYSIS OF TRAFFIC TIME SERIES." International Journal of Modern Physics C 24, no. 02 (February 2013): 1350006. http://dx.doi.org/10.1142/s012918311350006x.

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There has been considerable interest in quantifying the complexity of different time series, such as physiologic time series, traffic time series. However, these traditional approaches fail to account for the multiple time scales inherent in time series, which have yielded contradictory findings when applied to real-world datasets. Then multi-scale entropy analysis (MSE) is introduced to solve this problem which has been widely used for physiologic time series. In this paper, we first apply the MSE method to different correlated series and obtain an interesting relationship between complexity and Hurst exponent. A modified MSE method called multiscale permutation entropy analysis (MSPE) is then introduced, which replaces the sample entropy (SampEn) with permutation entropy (PE) when measuring entropy for coarse-grained series. We employ the traditional MSE method and MSPE method to investigate complexities of different traffic series, and obtain that the complexity of weekend traffic time series differs from that of the workday time series, which helps to classify the series when making predictions.

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Rizal, Achmad, Risanuri Hidayat, and Hanung Adi Nugroho. "Multiscale tsallis entropy for pulmonary crackle detection." International Journal of Advances in Intelligent Informatics 4, no. 3 (November 11, 2018): 192. http://dx.doi.org/10.26555/ijain.v4i3.273.

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Abnormalities in the lungs can be detected from the sound produced by the lungs. Diseases that occur in the lungs or respiratory tract can produce a distinctive lung sound. One of the examples of the lung sound is the pulmonary crackle caused by pneumonia or chronic bronchitis. Various digital signal processing techniques are developed to detect pulmonary crackle sound automatically, such as the measurement of signal complexity using Tsallis entropy (TE). In this study, TE measurements were performed through several orders on the multiscale pulmonary crackle signal. The pulmonary crackle signal was decomposed using the coarse-grained procedure since the lung sound as the biological signal had a multiscale property. In this paper, we used 21 pulmonary crackle sound and 22 normal lung sound for the experiment. The results showed that the second order TE on the scale of 1-15 had the highest accuracy of 97.67%. This result was better compared to the use of multi-order TE from the previous study, which resulted in an accuracy of 95.35%.

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Wu, Shuen-De, Chiu-Wen Wu, Shiou-Gwo Lin, Chun-Chieh Wang, and Kung-Yen Lee. "Time Series Analysis Using Composite Multiscale Entropy." Entropy 15, no. 3 (March 18, 2013): 1069–84. http://dx.doi.org/10.3390/e15031069.

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Costa, M., C. K. Peng, Ary L. Goldberger, and Jeffrey M. Hausdorff. "Multiscale entropy analysis of human gait dynamics." Physica A: Statistical Mechanics and its Applications 330, no. 1-2 (December 2003): 53–60. http://dx.doi.org/10.1016/j.physa.2003.08.022.

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Zhou, Elizabeth Y., Claudia Damiano, John Wilder, and Dirk B. Walther. "Measuring complexity of images using Multiscale Entropy." Journal of Vision 19, no. 10 (September 6, 2019): 96a. http://dx.doi.org/10.1167/19.10.96a.

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Chakravorty, Arghya, Jonathan Higham, and Richard H. Henchman. "Entropy of Proteins Using Multiscale Cell Correlation." Journal of Chemical Information and Modeling 60, no. 11 (September 21, 2020): 5540–51. http://dx.doi.org/10.1021/acs.jcim.0c00611.

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Thuraisingham, Ranjit A., and Georg A. Gottwald. "On multiscale entropy analysis for physiological data." Physica A: Statistical Mechanics and its Applications 366 (July 2006): 323–32. http://dx.doi.org/10.1016/j.physa.2005.10.008.

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Bukovsky, Ivo. "Learning Entropy: Multiscale Measure for Incremental Learning." Entropy 15, no. 12 (September 27, 2013): 4159–87. http://dx.doi.org/10.3390/e15104159.

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Grmela, Miroslav, Giuseppe Grazzini, Umberto Lucia, and L'Hocine Yahia. "Multiscale Mesoscopic Entropy of Driven Macroscopic Systems." Entropy 15, no. 12 (November 19, 2013): 5053–64. http://dx.doi.org/10.3390/e15115053.

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Pan, Junshan, Hanping Hu, Xiang Liu, and Yong Hu. "Multiscale Entropy Analysis on Human Operating Behavior." Entropy 18, no. 1 (December 22, 2015): 3. http://dx.doi.org/10.3390/e18010003.

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Polizzotto, Nicola, Tetsuya Takahashi, Christopher Walker, and Raymond Cho. "Wide Range Multiscale Entropy Changes through Development." Entropy 18, no. 1 (December 29, 2015): 12. http://dx.doi.org/10.3390/e18010012.

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XIA, JIANAN, and PENGJIAN SHANG. "MULTISCALE ENTROPY ANALYSIS OF FINANCIAL TIME SERIES." Fluctuation and Noise Letters 11, no. 04 (December 2012): 1250033. http://dx.doi.org/10.1142/s0219477512500332.

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The paper mainly applies the multiscale entropy (MSE) to analyze the financial time series. The MSE is used to examine the complexity of a quantified system. Based on MSE, we propose multiscale cross-sample entropy (MSCE) to analyze the complexity and correlation of two time series. By comparing with the results, we find that both results present remarkable scaling characterization and the value of each log return of financial time series decreases with a increasing scale factor. From the results of MSE, we also find that the entropy of the Europe markets is lower than that of the Asia, but higher than that of the Americas. It means the MSE can distinguish different areas markets. The results of MSCE show that financial plate have high synchrony with the plate of Electron, IT and Realty. The MSCE can distinguish the highly synchronous plates.

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Alvarez-Ramirez, Jose, Eduardo Rodriguez, and Jesus Alvarez. "A multiscale entropy approach for market efficiency." International Review of Financial Analysis 21 (January 2012): 64–69. http://dx.doi.org/10.1016/j.irfa.2011.12.001.

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Dadashi, Farzin, Daisuke Negishi, Benoit Mariani, Christopher Newman, Aline Bourgeois, and Kamiar Aminian. "Multiscale entropy based features for spasticity diagnoses." Gait & Posture 38 (November 2013): S54—S55. http://dx.doi.org/10.1016/j.gaitpost.2013.07.105.

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Morel, Cristina, and Anne Humeau-Heurtier. "Multiscale permutation entropy for two-dimensional patterns." Pattern Recognition Letters 150 (October 2021): 139–46. http://dx.doi.org/10.1016/j.patrec.2021.06.028.

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Humeau-Heurtier, Anne, Guillaume Mahé, and Pierre Abraham. "Modified multiscale sample entropy computation of laser speckle contrast images and comparison with the original multiscale entropy algorithm." Journal of Biomedical Optics 20, no. 12 (July 28, 2015): 121302. http://dx.doi.org/10.1117/1.jbo.20.12.121302.

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Platiša, Mirjana M., Nikola N. Radovanović, Aleksandar Kalauzi, Goran Milašinović, and Siniša U. Pavlović. "Multiscale Entropy Analysis: Application to Cardio-Respiratory Coupling." Entropy 22, no. 9 (September 18, 2020): 1042. http://dx.doi.org/10.3390/e22091042.

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It is known that in pathological conditions, physiological systems develop changes in the multiscale properties of physiological signals. However, in real life, little is known about how changes in the function of one of the two coupled physiological systems induce changes in function of the other one, especially on their multiscale behavior. Hence, in this work we aimed to examine the complexity of cardio-respiratory coupled systems control using multiscale entropy (MSE) analysis of cardiac intervals MSE (RR), respiratory time series MSE (Resp), and synchrony of these rhythms by cross multiscale entropy (CMSE) analysis, in the heart failure (HF) patients and healthy subjects. We analyzed 20 min of synchronously recorded RR intervals and respiratory signal during relaxation in the supine position in 42 heart failure patients and 14 control healthy subjects. Heart failure group was divided into three subgroups, according to the RR interval time series characteristics (atrial fibrillation (HFAF), sinus rhythm (HFSin), and sinus rhythm with ventricular extrasystoles (HFVES)). Compared with healthy control subjects, alterations in respiratory signal properties were observed in patients from the HFSin and HFVES groups. Further, mean MSE curves of RR intervals and respiratory signal were not statistically different only in the HFSin group (p = 0.43). The level of synchrony between these time series was significantly higher in HFSin and HFVES patients than in control subjects and HFAF patients (p < 0.01). In conclusion, depending on the specific pathologies, primary alterations in the regularity of cardiac rhythm resulted in changes in the regularity of the respiratory rhythm, as well as in the level of their asynchrony.

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Muñoz-Guillermo, María. "Ordinal Patterns in Heartbeat Time Series: An Approach Using Multiscale Analysis." Entropy 21, no. 6 (June 12, 2019): 583. http://dx.doi.org/10.3390/e21060583.

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In this paper, we simultaneously use two different scales in the analysis of ordinal patterns to measure the complexity of the dynamics of heartbeat time series. Rényi entropy and weighted Rényi entropy are the entropy-like measures proposed in the multiscale analysis in which, with the new scheme, four parameters are involved. First, the influence of the variation of the new parameters in the entropy values is analyzed when different groups of subjects (with cardiac diseases or healthy) are considered. Secondly, we exploit the introduction of multiscale analysis in order to detect differences between the groups.

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TSOLIS, GEORGE S., and THOMAS D. XENOS. "FUZZY INTRINSIC MODE ENTROPY FOR DATA FEATURE EXTRACTION." Advances in Adaptive Data Analysis 04, no. 01n02 (April 2012): 1250009. http://dx.doi.org/10.1142/s1793536912500094.

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The notion of fuzzy entropy (FuzzyEn) is extended to the multiscale case by combining FuzzyEn and empirical mode decomposition (EMD). The proposed technique, fuzzy intrinsic entropy (FIMEn) performs better than its predecessor intrinsic monde entropy (IMEn) and it is less dependent on the algorithmic parameters. In a pattern recognition context, FIMEn provides more separable clusters than IMEn when used for feature extraction, thus allowing for less classification error. The above results suggest that the proposed multiscale entropy metric is a very promising technique for evaluating data regularity and can be used effectively for feature extraction in pattern recognition problems.

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Huang, Jing Jing. "Multiscale Diffusion Entropy Analysis on Traffic Index Series." Applied Mechanics and Materials 556-562 (May 2014): 3553–57. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.3553.

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In this paper, we present a multiscale diffusion entropy analysis (DEA) for describing the traffic fractal dynamics with a spectrum of scale exponents . The method combines DEA with moving fitting window to analyze the traffic index (TI) series in different scales and shows more details of scale properties and provides a reliable analysis. We also quantify the effects of weather, traffic peaks on scale spectrum. The results indicate clearly that at large scales, the exponents show large volatility and they all have their own scale patterns. The multiscale DEA method provides new ways to measure the TI series and distinguishes groups in different conditions.

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Zhang, Weibo, and Jianzhong Zhou. "Fault Diagnosis for Rolling Element Bearings Based on Feature Space Reconstruction and Multiscale Permutation Entropy." Entropy 21, no. 5 (May 23, 2019): 519. http://dx.doi.org/10.3390/e21050519.

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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. Firstly, the ensemble empirical mode decomposition algorithm (EEMD) was employed to adaptively decompose the vibration signal into multiple intrinsic mode functions (IMFs), and the representative IMFs which contained rich fault information were selected to reconstruct a feature vector space. Secondly, the multiscale permutation entropy (MPE) was used to calculate the complexity of reconstructed feature space. Finally, the value of multiscale permutation entropy was presented to a support vector machine for fault classification. The proposed diagnostic algorithm was applied to three groups of rolling bearing experiments. The experimental results indicate that the proposed method has better classification performance and robustness than other traditional methods.

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Journal articles on the topic 'Multiscale Entropy'

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