Sign series entropy analysis of short-term heart rate variability

Bian, Chunhua; Si, Jiangbo; Wu, Xu-Hui; Shao, Jin-Yu; Ning, Xinbao; Wang, DongJin

doi:10.1007/s11434-009-0398-6

Cited by 19 publications

(13 citation statements)

References 22 publications

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“…Therefore, we have the ability to obtain different frequency signals by using wavelet transform. Entropy can describe the chaos levels of a signal as well as measure the level of complexity in the nonlinear dynamics area [9,12]. Zero entropy denotes that the system is well regulated, while Y. Xin et al / PAF recognition based on multi-scale Rényi entropy of ECG S191 infinity entropy means that the system is completely chaotic.…”

Section: Methodsmentioning

confidence: 99%

“…Rudolf Clausius developed entropy, which has been used to signal the analysis area to characterize the complexity of a system or signal [16]. The analysis of fractal structure and complexity of HRV signals based on entropy [5,9,12] and other nonlinear methods have been extremely popular throughout the world. Wavelet transform methods lacked the analysis of the chaos of HRV signals.…”

Section: Introductionmentioning

confidence: 99%

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Paroxysmal atrial fibrillation recognition based on multi-scale Rényi entropy of ECG

Yi¹,

Zhao²,

Mu³

et al. 2017

THC

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Abstract. BACKGROUND: Atrial fibrillation (AF) is a common type of arrhythmia disease, which has a high morbidity and can lead to some serious complications. The ability to detect and in turn prevent AF is extremely significant to the patient and clinician. OBJECTIVE: Using ECG to detect AF and develop a robust and effective algorithm is the primary objective of this study. METHODS: Some studies show that after AF occurs, the regulatory mechanism of vagus nerve and sympathetic nerve will change. Each R-R interval will be absolutely unequal. After studying the physiological mechanism of AF, we will calculate the Rényi entropy of the wavelet coefficients of heart rate variability (HRV) in order to measure the complexity of PAF signals, as well as extract the multi-scale features of paroxysmal atrial fibrillation (PAF). RESULTS: The data used in this study is obtained from MIT-BIH PAF Prediction Challenge Database and the correct rate in classifying PAF patients from normal persons is 92.48%. CONCLUSIONS: The results of this experiment proved that AF could be detected by using this method and, in turn, provide opinions for clinical diagnosis.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Paroxysmal atrial fibrillation recognition based on multi-scale Rényi entropy of ECG

Yi¹,

Zhao²,

Mu³

et al. 2017

THC

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“…The calculation of sign series entropy [5] is first to carry out symbolic sequence. In order to represent the potential changes of the original EEG signals, its symbolic process is : Figure 1: symbolic process of EEG signals R (J) =0、1、2 represent the electric potential.…”

Section: Methods Of Sign Series Entropymentioning

confidence: 99%

Beta wave of sleep electroencephalogram analysis based on sign series entropy

Zhao¹,

Li²,

Yang³

et al. 2016

Proceedings of the 2nd International Conference on Advances in Mechanical Engineering and Industrial Informatics (AMEII 2016)

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Abstract. Sleep and wake EEG have some differences. After studying their brain waves and calculating the sign series entropy, we use the T test for the detection of sleep and wake EEG data to figure out whether they are different. After beta waves being filtered out by the filter, we calculate the entropy of the sign series. The results of T test show that the beta waves in the state of sleep and wake are different.

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“…based on Combined with multiscale [3] and the symbol sequence entropy concept (sign series entropy, SSEA) [4] , Multiscale entropy sequence of symbols algorithms have been proposed. In this paper, the complexity analysis method based on Multiscale Sign Series Entropy Analysis was used to calculate the complexity of the occupation stress of the nurses and the students.…”

Section: Introductionmentioning

confidence: 99%

Occupation stress analysis based on Multiscale sign series entropy analysis

Jiang¹,

Wang²,

Hou³

et al. 2016

Proceedings of the 2016 International Forum on Management, Education and Information Technology Application

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Abstract. With the rapid development of society and the economic globalization, organizations occur various reforms and system innovations, which makes people feel stressful increasingly. The pressure of long term excessive work causes occupation stress. The effect of occupation stress on health is aroused the whole society's concern. As Theorel said that "woke pressure is occupation stress that is popular in the whole world". Occupation stress is evolving as the new occupation hazard factor. The paper uses Multiscale sign series entropy analysis to analyse the occupation stress between nurses and students. Research shows that the occupation stress of nurses is higher than the students. So the occupation stress of the nurse is larger than the student. The algorithm can help clinical diagnosis.

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Sign series entropy analysis of short-term heart rate variability

Cited by 19 publications

References 22 publications

Paroxysmal atrial fibrillation recognition based on multi-scale Rényi entropy of ECG

Paroxysmal atrial fibrillation recognition based on multi-scale Rényi entropy of ECG

Beta wave of sleep electroencephalogram analysis based on sign series entropy

Occupation stress analysis based on Multiscale sign series entropy analysis

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