Literature DB >> 19566278

Normal heartbeat series are nonchaotic, nonlinear, and multifractal: new evidence from semiparametric and parametric tests.

Richard T Baillie1, Aydin A Cecen, Cahit Erkal.   

Abstract

We present new evidence that normal heartbeat series are nonchaotic, nonlinear, and multifractal. In addition to considering the largest Lyapunov exponent and the correlation dimension, the results of the parametric and semiparametric estimation of the long memory parameter (long-range dependence) unambiguously reveal that the underlying process is nonstationary, multifractal, and has strong nonlinearity.

Mesh：

Year:  2009        PMID： 19566278     DOI： 10.1063/1.3152006

Source DB:  PubMed          Journal:  Chaos        ISSN： 1054-1500            Impact factor:   3.642


  6 in total

1.  Characterizing nonlinear heartbeat dynamics within a point process framework.

Authors:  Zhe Chen; Emery N Brown; Riccardo Barbieri
Journal:  IEEE Trans Biomed Eng       Date:  2010-02-17       Impact factor: 4.538

Review 2.  New insights into anterior cruciate ligament deficiency and reconstruction through the assessment of knee kinematic variability in terms of nonlinear dynamics.

Authors:  Leslie M Decker; Constantina Moraiti; Nicholas Stergiou; Anastasios D Georgoulis
Journal:  Knee Surg Sports Traumatol Arthrosc       Date:  2011-03-29       Impact factor: 4.342

3.  Beyond HRV: attractor reconstruction using the entire cardiovascular waveform data for novel feature extraction.

Authors:  Philip J Aston; Mark I Christie; Ying H Huang; Manasi Nandi
Journal:  Physiol Meas       Date:  2018-03-01       Impact factor: 2.833

4.  Estimation of instantaneous complex dynamics through Lyapunov exponents: a study on heartbeat dynamics.

Authors:  Gaetano Valenza; Luca Citi; Riccardo Barbieri
Journal:  PLoS One       Date:  2014-08-29       Impact factor: 3.240

5.  A simple method for detecting chaos in nature.

Authors:  Daniel Toker; Friedrich T Sommer; Mark D'Esposito
Journal:  Commun Biol       Date:  2020-01-03

6.  Discriminating chaotic and stochastic time series using permutation entropy and artificial neural networks.

Authors:  B R R Boaretto; R C Budzinski; K L Rossi; T L Prado; S R Lopes; C Masoller
Journal:  Sci Rep       Date:  2021-08-04       Impact factor: 4.379
  6 in total

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