Stratified Multivariate Multiscale Dispersion Entropy for Physiological Signal Analysis

Kafantaris, Evangelos; Lo, Tsz-Yan Milly; Escudero, Javier

doi:10.1109/tbme.2022.3207582

Cited by 6 publications

(3 citation statements)

References 68 publications

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“…Compared with the existing methods, DispEn has the following advantages: 1) DispEn will not have unde ned results when processing short-time signals; 2) DispEn has better robustness to noise; 3) DispEn is much faster than SampEn [10][11][12] . Because of these advantages, DispEn has been successfully applied in different scienti c and engineering elds, including biomedical engineering, mechanical engineering and computer engineering [13][14][15][16] . In the calculation step of DispEn, the initial time series is nally mapped (quantized) to a series of integers after different mathematical transformations and named as class [9] .…”

Section: Rostaghi Et Al Proposed Dispersion Entropy (Dispen) By Combi...mentioning

confidence: 99%

A new fractional fuzzy dispersion entropy and its application in muscle fatigue detection

Baohua,

Yong,

Jingsong

2023

Preprint

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Background The complexity analysis of neuroelectrophysiological signals has been widely applied in the field of biomedical engineering and muscle fatigue detection using the complexity analysis of surface electromyographic (sEMG) signals is one of the hot research topics. Recently, fuzzy dispersion entropy has attracted more and more attention as a new nonlinear dynamics method for complexity analysis which combines the advantages of both dispersion entropy(DispEn) and fuzzy entropy. However, it suffers from limitation of sensitivity to dynamic changes. In this study, fractional fuzzy dispersion entorpy (FFDispEn) is proposed based on DispEn, a new fuzzy membership function and fractional calculus to solve this limitation. Fuzzy membership function is defined based on Euclidean distance between embdding vector and dispersion pattern in this study. Methods Simulated signals generated by 1D Logistic map were used to test the sensitivity of the proposed method to dynamic changes. Moreover, ten subjects were recruited for upper limb muscle fatigue exprienment while sEMG signals of the biceps brachii muscle were recorded. Both simulated signals and sEMG signals were processed using sliding window approach. Sample entropy(SampEn), DispEn and FFDispEn were respectively used to calculate the complexity of each frame. The sensitivity of different algorithms to muscle fatigue process was analyzed using fitting parameters through linear fitting of the complexity of each frame signal. Results The results show that for simulated signals, the larger the fractional order q, the higher the sensitivity to dynamic changes. Moreover, DispEn performs poorly in the sensitivity to dynamic changes compared with FFDispEn. As for the muscle fatigue detection, FFDispEn value shows a clear declining tendency as muscle fatigue progresses and is more sensitive to muscle fatigue compared with SampEn and DispEn. Conclusions This study provides a new useful nonlinear dynamic indicator for sEMG signal preprocessing and muscle fatigue analysis. The proposed method may be useful for physiological and biomedical signal analysis.

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Section: Rostaghi Et Al Proposed Dispersion Entropy (Dispen) By Combi...mentioning

confidence: 99%

A new fractional fuzzy dispersion entropy and its application in muscle fatigue detection

Baohua,

Yong,

Jingsong

2023

Preprint

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“…For multi-channel time series, the multi-scale entropy method can only be applied when the signals are independent and uncorrelated. Inspired by the theory of multidimensional embedding reconstruction, Kafantaris E et al [35] proposed MMDE, which extended single-channel time series evaluation to multi-channel. Compared with single-channel time series evaluation, multi-channel time series evaluation can evaluate the dynamic interrelationship of multi-channel time series from the perspectives of complexity, mutual predictability, and long-term correlation [36].…”

Section: Introductionmentioning

confidence: 99%

Fault diagnosis method using MVMD signal reconstruction and MMDE-GNDO feature extraction and MPA-SVM

Mao,

Zhou,

et al. 2024

Front. Phys.

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To achieve a comprehensive and accurate diagnosis of faults in rolling bearings, a method for diagnosing rolling bearing faults has been proposed. This method is based on Multivariate Variational Mode Decomposition (MVMD) signal reconstruction, Multivariate Multiscale Dispersion Entropy (MMDE)-Generalized Normal Distribution Optimization (GNDO), and Marine predators’ algorithm-based optimization support vector machine (MPA-SVM). Firstly, by using a joint evaluation function (energy*|correlation coefficient|), the multi-channel vibration signals of rolling bearings after MVMD decomposition are denoised and reconstructed. Afterward, MMDE is applied to fuse the information from the reconstructed signal and construct a high-dimensional fault feature set. Following that, GNDO is used to select features and extract a subset of low-dimensional features that are sensitive and easy to classify. Finally, MPA is used to realize the adaptive selection of important parameters in the SVM classifier. Fault diagnosis experiments are carried out using datasets provided by the Case Western Reserve University (CWRU) and Paderborn University (PU). The MVMD signal reconstruction method can effectively filter out the noise components of each channel. MMDE-GNDO can availably mine multi-channel fault features and eliminate redundant (or interference) items. The MPA-SVM classifier can identify faults in different working conditions with an average accuracy of 99.72% and 100%, respectively. The results demonstrate the accuracy, efficiency, and stability of the proposed method.

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“…We call it dispersion complexity-entropy curves(DCEC). DE is an improved entropy method that is widely used in many types of research [29][30][31] for it can avoid the disadvantages in PE and sample entropy(SE) [32]. It also has many improved formats such as reverse DE(RDE) [33] and fluctuation-based DE(FDE) [34].…”

Section: Introductionmentioning

confidence: 99%

Dispersion complexity-entropy curves: an effective method to detect the structures of complex systems

Jiang

Shang

Zhang

2023

Preprint

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Complexity-entropy curves(CEC) is a useful tool to detect the structure of time series. It’s widely applied in many research areas since it can distinguish the chaotic system and the stochastic process well. However, the original permutation complexity-entropy curves(PCEC) based on permutation entropy(PE) has a defect for it can not take means and amplitudes of time series into consideration, which may lead to some errors when distinguishing the systems. In this paper, we propose dispersion complexity-entropy curves(DCEC) to overcome the defects of PCEC. In addition, we expand the curves from two-dimension to threedimension. We first compare DCEC with PCEC by simulated data. The result shows that DCEC are not only more distinguishable but also more robust with the change of parameters when detecting periodic series. Then, we apply our method to real-world data to illustrate its practicability. We propose a creative feature extraction method based on DCEC and combine it with MSVM to diagnose the different types of bearing fault, which obtains perfect results for the accuracy achieves 100%. We also apply DCEC to stock indices in different countries and different periods to analyze the complexity degree of financial markets. The results successfully detect American financial crisis in 2008 and the rapid development of the economy in China during 2014-2018. These demonstrate that DCEC can serve as an effective method to analyze complex systems.

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Stratified Multivariate Multiscale Dispersion Entropy for Physiological Signal Analysis

Cited by 6 publications

References 68 publications

A new fractional fuzzy dispersion entropy and its application in muscle fatigue detection

A new fractional fuzzy dispersion entropy and its application in muscle fatigue detection

Fault diagnosis method using MVMD signal reconstruction and MMDE-GNDO feature extraction and MPA-SVM

Dispersion complexity-entropy curves: an effective method to detect the structures of complex systems

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