Use of Mutual Information and Transfer Entropy to Assess Interaction between Parasympathetic and Sympathetic Activities of Nervous System from HRV

Zheng, Lianrong; Pan, Weifeng; Li, Yifan; Luo, Daiyi; Wang, Qian; Liu, Guanzheng

doi:10.3390/e19090489

Cited by 32 publications

(29 citation statements)

References 43 publications

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“…In this simulation, TE( X → Y ) and TE( Y → X ) were calculated for each pair of simulated data to choose a proper value of α with time lag τ set to 1 [ 20 ]. Figure 2 shows the performance of the simulation when α varies from 1 to 7.…”

Section: Resultsmentioning

confidence: 99%

“…It has been reported that CHF is a chronic cardiovascular syndrome which is characterized by an imbalance of activity in the autonomic nervous system [ 24 , 37 , 38 ]. The HRV analysis is a widely used method to investigate the ANS modulation of the heart [ 20 , 39 , 40 ]. To investigate the balance of ANS activity, the LF/HF ratio is a more robust value in HRV frequency domain analysis.…”

Section: Discussionmentioning

confidence: 99%

“…More and more analysis of information transfer in neurophysiology and cardiovascular physiology [ 16 , 17 , 18 , 19 ] revealed TE as a powerful tool. Zheng et al used TE among the low-frequency (LF) component and high-frequency (HF) components to study the interaction between SNS and PNS in sleep apnea patients [ 20 ].…”

Section: Introductionmentioning

confidence: 99%

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The Interaction Analysis between the Sympathetic and Parasympathetic Systems in CHF by Using Transfer Entropy Method

Luo

Pan

et al. 2018

Entropy

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Congestive heart failure (CHF) is a cardiovascular disease associated with autonomic dysfunction, where sympathovagal imbalance was reported in many studies using heart rate variability (HRV). To learn more about the dynamic interaction in the autonomic nervous system (ANS), we explored the directed interaction between the sympathetic nervous system (SNS) and the parasympathetic nervous system (PNS) with the help of transfer entropy (TE). This article included 24-h RR interval signals of 54 healthy subjects (31 males and 23 females, 61.38 ± 11.63 years old) and 44 CHF subjects (8 males and 2 females, 19 subjects’ gender were unknown, 55.51 ± 11.44 years old, 4 in class I, 8 in class II and 32 in class III~IV, according to the New York Heart Association Function Classification), obtained from the PhysioNet database and then segmented into 5-min non-overlapping epochs using cubic spline interpolation. For each segment in the normal group and CHF group, frequency-domain features included low-frequency (LF) power, high-frequency (HF) power and LF/HF ratio were extracted as classical estimators of autonomic activity. In the nonlinear domain, TE between LF and HF were calculated to quantify the information exchanging between SNS and PNS. Compared with the normal group, an extreme decrease in LF/HF ratio (p = 0.000) and extreme increases in both TE(LF→HF) (p = 0.000) and TE(HF→LF) (p = 0.000) in the CHF group were observed. Moreover, both in normal and CHF groups, TE(LF→HF) was a lot greater than TE(HF→LF) (p = 0.000), revealing that TE was able to distinguish the difference in the amount of directed information transfer among ANS. Extracted features were further applied in discriminating CHF using IBM SPSS Statistics discriminant analysis. The combination of the LF/HF ratio, TE(LF→HF) and TE(HF→LF) reached the highest screening accuracy (83.7%). Our results suggested that TE could serve as a complement to traditional index LF/HF in CHF screening.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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The Interaction Analysis between the Sympathetic and Parasympathetic Systems in CHF by Using Transfer Entropy Method

Luo

Pan

et al. 2018

Entropy

Self Cite

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“…Entropy measures, including the measures discussed in the previous section, are useful in explaining the variability in univariate time series. [57][58][59][60] The concept of entropy is extended to the Transfer entropy 61 and Mutual information, 62,63 which are discussed in the following section. The concept of Transfer entropy is applicable to uncovering the information flows between the systems, bivariate analysis of time series under uncertainties such as financial time series, 28 and multivariate analysis of time series in various disciplines such as physiology, 64 neuroscience, 65 ecology, bionomics, and neurology.…”

Section: Sample Entropymentioning

confidence: 99%

A review of entropy measures for uncertainty quantification of stochastic processes

Namdari

2019

Advances in Mechanical Engineering

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Entropy is originally introduced to explain the inclination of intensity of heat, pressure, and density to gradually disappear over time. Based on the concept of entropy, the Second Law of Thermodynamics, which states that the entropy of an isolated system is likely to increase until it attains its equilibrium state, is developed. More recently, the implication of entropy has been extended beyond the field of thermodynamics, and entropy has been applied in many subjects with probabilistic nature. The concept of entropy is applicable and useful in characterizing the behavior of stochastic processes since it represents the uncertainty, ambiguity, and disorder of the processes without being restricted to the forms of the theoretical probability distributions. In order to measure and quantify the entropy, the existing probability of every event in the stochastic process must be determined. Different entropy measures have been studied and presented including Shannon entropy, Renyi entropy, Tsallis entropy, Sample entropy, Permutation entropy, Approximate entropy, and Transfer entropy. This review surveys the general formulations of the uncertainty quantification based on entropy as well as their various applications. The results of the existing studies show that entropy measures are powerful predictors for stochastic processes with uncertainties. In addition, we examine the stochastic process of lithium-ion battery capacity data and attempt to determine the relation between the changes in battery capacity over different cycles and two entropy measures: Sample entropy and Approximate entropy.

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“…The MI is a measurement of the dependence between two random variables, in the sense that it specifies the amount of information that can be obtained from one random variable from knowing the other [ 56 ]. This concept is closely related to the entropy concept defined in Information Theory for a single random variable, which defines the amount of information that is explained by that variable.…”

Section: Statistical Analysis Of Environmental Measurement Dynamicmentioning

confidence: 99%

Multiparametric Monitoring in Equatorian Tomato Greenhouses (III): Environmental Measurement Dynamics

Erazo-Rodas

Sandoval-Moreno

Muñoz-Romero

et al. 2018

Sensors

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World population growth currently brings unequal access to food, whereas crop yields are not increasing at a similar rate, so that future food demand could be unmet. Many recent research works address the use of optimization techniques and technological resources on precision agriculture, especially in large demand crops, including climatic variables monitoring using wireless sensor networks (WSNs). However, few studies have focused on analyzing the dynamics of the environmental measurement properties in greenhouses. In the two companion papers, we describe the design and implementation of three WSNs with different technologies and topologies further scrutinizing their comparative performance, and a detailed analysis of their energy consumption dynamics is also presented, both considering tomato greenhouses in the Andean region of Ecuador. The three WSNs use ZigBee with star topology, ZigBee with mesh topology (referred to here as DigiMesh), and WiFi with access point topology. The present study provides a systematic and detailed analysis of the environmental measurement dynamics from multiparametric monitoring in Ecuadorian tomato greenhouses. A set of monitored variables (including CO2, air temperature, and wind direction, among others) are first analyzed in terms of their intrinsic variability and their short-term (circadian) rhythmometric behavior. Then, their cross-information is scrutinized in terms of scatter representations and mutual information analysis. Based on Bland–Altman diagrams, good quality rhythmometric models were obtained at high-rate sampling signals during four days when using moderate regularization and preprocessing filtering with 100-coefficient order. Accordingly, and especially for the adjustment of fast transition variables, it is appropriate to use high sampling rates and then to filter the signal to discriminate against false peaks and noise. In addition, for variables with similar behavior, a longer period of data acquisition is required for the adequate processing, which makes more precise the long-term modeling of the environmental signals.

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Use of Mutual Information and Transfer Entropy to Assess Interaction between Parasympathetic and Sympathetic Activities of Nervous System from HRV

Cited by 32 publications

References 43 publications

The Interaction Analysis between the Sympathetic and Parasympathetic Systems in CHF by Using Transfer Entropy Method

The Interaction Analysis between the Sympathetic and Parasympathetic Systems in CHF by Using Transfer Entropy Method

A review of entropy measures for uncertainty quantification of stochastic processes

Multiparametric Monitoring in Equatorian Tomato Greenhouses (III): Environmental Measurement Dynamics

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