Multiscale entropy-based methods for heart rate variability complexity analysis

Silva, Luiz Eduardo Virgílio; Cabella, Brenno; Neves, Ubiraci Pereira da Costa; Murta, Leonardo

doi:10.1016/j.physa.2014.12.011

Cited by 42 publications

(41 citation statements)

References 26 publications

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“…Therefore, we illustrate the relationship between complexity and disorder as an inverted U shape where the maximal complexity value (as measured by EoE index) appears in the middle of extreme order and disorder. This finding is novel since it is consistent with the hypothesis in many studies on complexity [1][2][3][4][5][6][7][8] but has never been demonstrated in real data sets. Table 1 lists the specificity of EoE on the Healthy group and the sensitivities of EoE on the congestive heart failure (CHF) as well as the atrial fibrillation (AF) groups for the 237 sets of short time series with each of 70, 300, and 500 data points, separately, at τ = 5 and s 1 = 55.…”

Section: Inverted U Curvesupporting

confidence: 88%

“…The overall sensitivities and specificity are comparable with those computed by other methods [8,20,21]. However, the EoE method is able to separate healthy subjects from CHF and AF patients at one time with less data points.…”

Section: Accuracy Of Eoesupporting

confidence: 68%

“…As a result, MSE robustly separates healthy group and pathologic groups with AF and congestive heart failure (CHF). Similar separation effect can be obtained from the succeeding analyses such as entropy of the degree distribution (EDD) of the network [17] and generalized sample entropy based method [8]. Their works demonstrate that complexity is a reflection of adaptability and degree of health.…”

Section: Introductionsupporting

confidence: 55%

“…As there are complex nonlinear interactions that regulate a healthy physiologic signal, the signal is, therefore, constantly changing and hard-to-predict [1], and the resulting complex behavior is observed to be different from either a highly random or a very regular one. Thus, it was hypothesized that there exists a measure of complexity that is maximal for systems intermediate between extreme order and disorder [1][2][3][4][5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

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Entropy of Entropy: Measurement of Dynamical Complexity for Biological Systems

Chang

Wei

Huang

et al. 2017

Entropy

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Healthy systems exhibit complex dynamics on the changing of information embedded in physiologic signals on multiple time scales that can be quantified by employing multiscale entropy (MSE) analysis. Here, we propose a measure of complexity, called entropy of entropy (EoE) analysis. The analysis combines the features of MSE and an alternate measure of information, called superinformation, useful for DNA sequences. In this work, we apply the hybrid analysis to the cardiac interbeat interval time series. We find that the EoE value is significantly higher for the healthy than the pathologic groups. Particularly, short time series of 70 heart beats is sufficient for EoE analysis with an accuracy of 81% and longer series of 500 beats results in an accuracy of 90%. In addition, the EoE versus Shannon entropy plot of heart rate time series exhibits an inverted U relationship with the maximal EoE value appearing in the middle of extreme order and disorder.

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Section: Inverted U Curvesupporting

confidence: 88%

Section: Accuracy Of Eoesupporting

confidence: 68%

Section: Introductionsupporting

confidence: 55%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Entropy of Entropy: Measurement of Dynamical Complexity for Biological Systems

Chang

Wei

Huang

et al. 2017

Entropy

View full text Add to dashboard Cite

show abstract

“…For instance, Kaffashi F et al [2] analyzed complexity of EEG signals during sleep with skin-to-skin contact. More recently, Silva L E V et al [3] and Mukherjee S et al [4] measured complexity of heart rate variability and heart failure, respectively. Complexity measure of chaotic systems is another focus of interest since it can elegantly describe dynamics and complexity of chaotic systems.…”

Section: Introductionmentioning

confidence: 99%