A Summary: Quantifying the Complexity of Financial Markets Using Composite and Multivariate Multiscale Entropy

2021 ◽  
pp. 139-155
Author(s):  
Yunfan Lu ◽  
Zhiyong Zheng
Keyword(s):  
Financial Markets ◽  
Multiscale Entropy ◽  
Multivariate Multiscale Entropy

scholarly journals Application in Feature Extraction of AE Signal for Rolling Bearing in EEMD and Cloud Similarity Measurement

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Long Han ◽  
Chengwei Li ◽  
Liqun Shen
Keyword(s):  
Feature Extraction ◽  
Simulation Experiment ◽  
Rolling Bearing ◽  
Multiscale Entropy ◽  
Similarity Measurement ◽  
Damage And Fracture ◽  
Fault Features ◽  
Information Method ◽  
Ae Signal ◽  
Multivariate Multiscale Entropy

Due to the powerful ability of EEMD algorithm in noising, it is usually applied to feature extraction of fault signal of rolling bearing. But the selective correctness of sensitive IMF after decomposition can directly influence the correctness of feature extraction of fault signal. In order to solve the problem, the paper firstly proposes a new method on selecting sensitive IMF based on Cloud Similarity Measurement. By comparing this method in simulation experiment with the traditional mutual information method, it is obvious that the proposed method has overcome the misjudgment in the traditional method and it has higher accuracy, by factually collecting the normal, damage, and fracture fault AE signal of the inner ring of rolling bearing as samples, which will be decomposed by EEMD algorithm in the experiments. It uses Cloud Similarity Measurement to select sensitive IMF which can reflect the fault features. Finally, it sets the Multivariate Multiscale Entropy (MME) of sensitive IMF as the eigenvalue of original signal; then it is classified by the SVM to determine the fault types exactly. The results of the experiments show that the selected sensitive IMF based on Cloud Similarity Measurement is effective; it can help to improve the accuracy of the fault diagnosis and feature extraction.

scholarly journals Resistance Training Exercise Program for Intervention to Enhance Gait Function in Elderly Chronically Ill Patients: Multivariate Multiscale Entropy for Center of Pressure Signal Analysis

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Ming-Shu Chen ◽  
Bernard C. Jiang
Keyword(s):  
Resistance Training ◽  
Chronic Diseases ◽  
Postural Stability ◽  
Center Of Pressure ◽  
Chronically Ill ◽  
The Elderly ◽  
Multiscale Entropy ◽  
Gait Function ◽  
Multivariate Multiscale Entropy ◽  
Chronically Ill Patients

Falls are unpredictable accidents, and the resulting injuries can be serious in the elderly, particularly those with chronic diseases. Regular exercise is recommended to prevent and treat hypertension and other chronic diseases by reducing clinical blood pressure. The “complexity index” (CI), based on multiscale entropy (MSE) algorithm, has been applied in recent studies to show a person’s adaptability to intrinsic and external perturbations and widely used measure of postural sway or stability. The multivariate multiscale entropy (MMSE) was advanced algorithm used to calculate the complexity index (CI) values of the center of pressure (COP) data. In this study, we applied the MSE & MMSE to analyze gait function of 24 elderly, chronically ill patients (44% female; 56% male; mean age,67.56±10.70years) with either cardiovascular disease, diabetes mellitus, or osteoporosis. After a 12-week training program, postural stability measurements showed significant improvements. Our results showed beneficial effects of resistance training, which can be used to improve postural stability in the elderly and indicated that MMSE algorithms to calculate CI of the COP data were superior to the multiscale entropy (MSE) algorithm to identify the sense of balance in the elderly.

scholarly journals Multivariate Multiscale Entropy Applied to Center of Pressure Signals Analysis: An Effect of Vibration Stimulation of Shoes

Entropy ◽  
2012 ◽  
Vol 14 (11) ◽  
pp. 2157-2172 ◽  
Author(s):  
Qin Wei ◽  
Dong-Hai Liu ◽  
Kai-Hong Wang ◽  
Quan Liu ◽  
Maysam Abbod ◽  
...  
Keyword(s):  
Center Of Pressure ◽  
Multiscale Entropy ◽  
Multivariate Multiscale Entropy ◽  
Stimulation Of

scholarly journals Multivariate Multiscale Entropy: An Approach to Estimating Vigilance of Driver

2021 ◽  
Author(s):  
Kawser Ahammed ◽  
Mosabber Uddin Ahmed
Keyword(s):  
Detection System ◽  
Sample Entropy ◽  
Multiscale Entropy ◽  
Support Vector ◽  
Eeg Signals ◽  
Cognitive States ◽  
The Difference ◽  
New Feature ◽  
Multivariate Multiscale Entropy ◽  
Multivariate Sample

Abstract Various driver’s vigilance estimation techniques currently exist in literature. But none of them detects the vigilance of driver in complexity domain. As a result, we have proposed the recently introduced multivariate multiscale entropy (MMSE) method to fill this research gap. In this research, we have applied the MMSE technique to differential entropy features of electroencephalogram (EEG) and electrooculogram (EOG) signals for detecting vigilance of driver in complexity domain. The MMSE has also been employed to PERCLOS (Percentage of Eye Closure) values to analyse cognitive states (awake, tired and drowsy) in complexity domain. The contribution of this research is to show how a new feature called MMSE can efficiently classify the awake, tired and drowsy state of the driver in complexity domain. Another contribution is to demonstrate the distinguishing ability of the MMSE by validating it with applying multivariate sample entropy feature of cognitive states to support vector machine (SVM). The experimental MMSE analysis curves show statistically significant differences (p < 0.01) in terms of complexity among brain EEG signals, forehead EEG signals and EOG signals. Moreover, the difference in the multivariate sample entropy across all scales in awake (1.0828 ± 0.4664), tired (0.7841 ± 0.3183) and drowsy (0.2938 ± 0.1664) states are statistically significant (p <0.01). Also, the SVM, a machine learning technique, has discriminated the cognitive states with the promising classification accuracy of 76.2%. As a result, the MMSE analysis of cognitive states can be implemented practically for vigilance detection by building a programmable vigilance detection system.

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