Complexity in congestive heart failure: A time-frequency approach

Banerjee, Santo; Palit, Sanjay Kumar; Mukherjee, Sayan; Ariffin, Muhammad Rezal Kamel; Rondoni, Lamberto

doi:10.1063/1.4941374

Cited by 29 publications

(18 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The healthy human heart is complex, since it has many interacting subunits to keep the whole system (heart) active. A cardiac heart is less complex, since it fails to working properly with each interacting subunits [40,41]. Several other type of complexity can be investigated in laser and optics [34], ecological and biological models and networks [40,41], pseudo random number generations [35], economy and politics [39], fractional dynamics [42], etc.…”

mentioning

confidence: 99%

Complexity, Chaos and Fluctuations

Banerjee

Colangeli

2017

Eur. Phys. J. Spec. Top.

Self Cite

View full text Add to dashboard Cite

This EPJ special topics issue is a collection of contributions about some recent developments in statistical mechanics and dynamical complexity. The various articles report results on statistical fluctuations, non equilibrium models, chaos and nonlinear interactions, complexity and its various associated measures.The long-standing endeavor of Statistical Mechanics is to establish a mathematical framework which allows to encompass different levels of description of a many-particle system: (i) the microscopic setting, stemming from the Boltzmann-Gibbs statistical ensemble theory [1, 2]; (ii) the mesoscopic scale, typically described in the framework of kinetic theory [3-5], interacting particle systems [6][7][8][9][10][11][12] or even more general stochastic processes [13,14]; (iii) the macroscopic level, which is the standard set-up in contexts such as the derivation of hydrodynamic equations, turbulence [15][16][17], Irreversible Thermodynamics [18], etc. Equilibrium phenomena have been investigated and understood much more thoroughly than non-equilibrium ones. At present, the equilibrium theory may be considered complete, for what concerns the microscopic foundations of equilibrium thermodynamics, including the theory of phase transitions and critical phenomena. On the other hand, in spite of the long-standing research activity, Nonequilibrium Statistical Mechanics is still an open field of research. One of the reasons is that nonequilibrium phenomena are so numerous, diverse and complex that attaining a unified description may still appear as an elusive ambition. In particular, away from equilibrium one cannot bypass the analysis of the microscopic dynamics even in the study of stationary states, that are the simplest beyond equilibrium.Nevertheless, relevant progresses were made in this field in the last decades, cf.[19]. We refer, for instance, to the discovery of the Fluctuation Relations for deterministic and stochastic dynamics [20][21][22]. Much effort was needed, in particular, to identify the minimal mathematical ingredients as well as the physical mechanisms lying beneath the validity of such relations [23][24][25]. Another result, whose mathematical theory was originally dug out in the sixties of the last century [26,27] and was then gradually a

show abstract

mentioning

confidence: 99%

Complexity, Chaos and Fluctuations

Banerjee

Colangeli

2017

Eur. Phys. J. Spec. Top.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Among them, the most prominent one is entropy measure, for its simplicity and convenience implementation in modern computers. Entropy methods, such as approximate entropy (AppEn) [11,12], sample entropy (SampEn) [12,13], permutation entropy (PermEn) [14][15][16], and fuzzy entropy (FuzzyEn) [13,17,18], have been successively introduced to explore the dynamical complexity hidden in financial and physiological data, which are commonly described as short and noisy series [19][20][21]. Among them, AppEn measures regularity or randomness of a series through constructing difference of mean logarithmic function of the probability of vector pairs within tolerance r of template vectors with two adjacent integer embedding dimensions [11].…”

Section: Introductionmentioning

confidence: 99%

Refined Composite Multivariate Multiscale Fractional Fuzzy Entropy: Measuring the Dynamical Complexity of Multichannel Financial Data

Chu

Zhang

2021

Complexity

View full text Add to dashboard Cite

Refined composite multivariate multiscale fractional fuzzy entropy (RCmvMFFE), which aims to sensitively discriminate different short noisy multichannel financial data, is proposed as a new measure to quantify the complexity dynamics of multichannel time series in this work. To better comprehend the RCmvMFFE measure, the dynamical complexity analyses of multichannel synthetic dataset are comparatively studied with multivariate multiscale fuzzy entropy (mvMFE), refined composite multivariate multiscale fuzzy entropy (RCmvMFE), and refined composite multivariate multiscale fractional fuzzy entropy (RCmvMFFE). Then, these measures are firstly employed to explore actual multichannel financial index series to the best of our knowledge. The empirical analyses report that RCmvMFFE measure is able to deeply and sensitively dig up the market information hidden in the multichannel financial data and can better discriminate markets in different area compared to the traditional measures to some extent.

show abstract

“…It can identify the disorder in the phase space trajectories by utilizing the concept of Shannon entropy [25]. Several entropy measures have been proposed to quantify the complexity [26][27][28][29][30][31][32][33][34][35][36][37]. Recurrence-based entropy is one of the effective measures that can be applied in any dimensional system [38,39].…”

Section: Introductionmentioning

confidence: 99%

In Search of Chaos and Complexity of a Cognitive Language-Learning System

Luo

Mukherjee²

2020

Complexity

Self Cite

View full text Add to dashboard Cite

In this article, we investigate the long-term dynamics of a known cognitive-based language-learning system under the variation of a system parameter. Stability of the equilibrium points is studied. Period root to chaos is investigated by bifurcation analysis. A Lyapunov analysis is performed to verify the complex dynamics in the system. Existence of chaos is confirmed by 0-1 test. A noise-induced cognitive phenomenon is proposed under the effect of power noise. Chaotic and nonchaotic dynamics are explored in the noise-induced system. Furthermore, disorder as well as complexity, are investigated for both the systems using the concept of weighted recurrence. The whole analysis can be effective to understand the dynamical features and nonlinear structure of the cognitive language-learning model.

show abstract

Complexity in congestive heart failure: A time-frequency approach

Cited by 29 publications

References 42 publications

Complexity, Chaos and Fluctuations

Complexity, Chaos and Fluctuations

Refined Composite Multivariate Multiscale Fractional Fuzzy Entropy: Measuring the Dynamical Complexity of Multichannel Financial Data

In Search of Chaos and Complexity of a Cognitive Language-Learning System

Contact Info

Product

Resources

About