Cross-fuzzy entropy: A new method to test pattern synchrony of bivariate time series

Xie, Huimin; Zheng, Yongping; Guo, Jian; Chen, Xin

doi:10.1016/j.ins.2010.01.004

Cited by 68 publications

(60 citation statements)

References 35 publications

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“…The linear coupling measurements are mainly the correlation coefficient (CC) in time-domain and coherence function (CF) in frequency-domain [29]. The nonlinear ones include, e.g., mutual information (MI) [28], cross entropy approaches such as cross-conditional entropy (XCE) [20,30,31], cross-sample entropy (XSampEn) [23,[31][32][33][34] and cross-fuzzy entropy (XFuzzyEn) [23,[33][34][35], as well as synchronization approaches (generalized synchronization [36], phase synchronization [37,38], and event synchronization [39], etc., to be specific). In order to objectively examine the electromechanical coupling, comprehensive analyses and comparisons based on the aforementioned coupling methods should be conducted.…”

Section: Introductionmentioning

confidence: 99%

Measuring Electromechanical Coupling in Patients with Coronary Artery Disease and Healthy Subjects

Ji¹,

Liu

et al. 2016

Entropy

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Coronary artery disease (CAD) is the most common cause of death globally. To detect CAD noninvasively at an early stage before clinical symptoms occur is still nowadays challenging. Analysis of the variation of heartbeat interval (RRI) opens a new avenue for evaluating the functional change of cardiovascular system which is accepted to occur at the subclinical stage of CAD. In addition, systolic time interval (STI) and diastolic time interval (DTI) also show potential. There may be coupling in these electromechanical time series due to their physiological connection. However, to the best of our knowledge no publication has systematically investigated how can the coupling be measured and how it changes in CAD patients. In this study, we enrolled 39 CAD patients and 36 healthy subjects and for each subject the electrocardiogram (ECG) and photoplethysmography (PPG) signals were recorded simultaneously for 5 min. The RRI series, STI series, and DTI series were constructed, respectively. We used linear cross correlation (CC), coherence function (CF), as well as nonlinear mutual information (MI), cross conditional entropy (XCE), cross sample entropy (XSampEn), and cross fuzzy entropy (XFuzzyEn) to analyse the bivariate RRI-DTI coupling, RRI-STI coupling, and STI-DTI coupling, respectively. Our results suggest that the linear CC and CF generally have no significant difference between the two groups for all three types of bivariate coupling. The MI only shows weak change in RRI-DTI coupling. By comparison, the three entropy-based coupling measurements show significantly decreased coupling in CAD patients except XSampEn for RRI-DTI coupling (less significant) and XCE for STI-DTI and RRI-STI coupling (not significant). Additionally, the XFuzzyEn performs best as it was still significant if we further applied the Bonferroni correction in our statistical analysis. Our study indicates that the intrinsic electromechanical coupling is most probably nonlinear and can better be measured by nonlinear entropy-based measurements especially the XFuzzyEn. Besides, CAD patients are accompanied by a loss of electromechanical coupling. Our results suggest that cardiac electromechanical coupling may potentially serve as a noninvasive diagnostic tool for CAD.

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Section: Introductionmentioning

confidence: 99%

Measuring Electromechanical Coupling in Patients with Coronary Artery Disease and Healthy Subjects

Ji¹,

Liu

et al. 2016

Entropy

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“…Classification method Refrence ID GP (Holland, 1975;Koza, 1994;Poli et al, 2008) GA (Koza, 1994;Michalewicz, 1996;Raikova & Aladjov, 2002;Wang, Yan, Hu, Xie & Wang, 2006) ANN (Bishop, 1995;Xie et al, 2010;) Fuzzy systems (Chan et al, 2000;Kiryu & Yamashita, 2007;Takagi & Sugeno, 1985) LDA (Balakrishnama & Ganapathiraju, 2010;Fisher, 1936) Support Vector Machine (Gunn, 1998Hsu et al, 2003) One Clause at a Time (Torvik et al, 1999) Cross validation (Kohavi, 1995;McLachlan et al, 2004) Confusion matrix (Kohavi & Provost, 1998) …”

Section: Classification Methodsmentioning

confidence: 99%

“…The use of fuzzy systems, as described in section 3.3, is also a classification method that has been used in muscle fatigue research as a fatigue index, showing better results than conventional fatigue indices (Kiryu & Yamashita, 2007). Xie et al used a fuzzy approximate entropy analysis of sEMG signals (Xie et al, 2009) and a cross-fuzzy entropy (Xie et al, 2010) as means by which to assess muscle fatigue. As mentioned in section 3.1, GP, a specialisation within the field of GAs (Holland, 1975) and based on Darwin's theory of evolution, finds the best suited computer program to perform a set task.…”

Section: Research On Classification Of Emg Signalsmentioning

confidence: 99%

sEMG Techniques to Detect and Predict Localised Muscle Fatigue

Almulla¹,

Sepúlveda²,

Colley³

2012

EMG Methods for Evaluating Muscle and Nerve Function

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“…In contrast FuzzyEn employs an exponential function to give a continuous degree of similarity between vectors based on their closeness [18]. As a natural extension of FuzzyEn, C-FuzzyEn has been introduced in order to assess the degree of similarity between two different signals using fuzzy entropy [27,28]. The C-FuzzyEn of two time series of N points,…”

Section: Cross Fuzzy Entropymentioning

confidence: 99%

Fuzzy entropy based nonnegative matrix factorization for muscle synergy extraction

Jelfs

Tin

et al. 2016

2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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The concept of muscle synergies has proven to be an effective method for representing patterns of muscle activation. The number of degrees of freedom to be controlled are reduced while also providing a flexible platform for producing detailed movements using synergies as building blocks. It has previously been shown that small components of movement are crucial to producing precise and coordinated movement. Methods which focus on the variance of the data make it possible to overlook these small components in the synergy extraction process. However, algorithms which address the inherent complexity in the neuromuscular system are lacking. To that end we propose a new nonnegative matrix factorization algorithm which employs a cross fuzzy entropy similarity measure, thus, extracting muscle synergies which preserve the complexity of the recorded muscular data. The performance of the proposed algorithm is illustrated on representative EMG data.

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Cross-fuzzy entropy: A new method to test pattern synchrony of bivariate time series

Cited by 68 publications

References 35 publications

Measuring Electromechanical Coupling in Patients with Coronary Artery Disease and Healthy Subjects

Measuring Electromechanical Coupling in Patients with Coronary Artery Disease and Healthy Subjects

sEMG Techniques to Detect and Predict Localised Muscle Fatigue

Fuzzy entropy based nonnegative matrix factorization for muscle synergy extraction

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