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Journal Abstract Search

178 related items for PubMed ID: 21419400

  • 1. Non-uniform multivariate embedding to assess the information transfer in cardiovascular and cardiorespiratory variability series.
    Faes L, Nollo G, Porta A.
    Comput Biol Med; 2012 Mar; 42(3):290-7. PubMed ID: 21419400
    [Abstract] [Full Text] [Related]

  • 2. Information-based detection of nonlinear Granger causality in multivariate processes via a nonuniform embedding technique.
    Faes L, Nollo G, Porta A.
    Phys Rev E Stat Nonlin Soft Matter Phys; 2011 May; 83(5 Pt 1):051112. PubMed ID: 21728495
    [Abstract] [Full Text] [Related]

  • 3. Lag-specific transfer entropy as a tool to assess cardiovascular and cardiorespiratory information transfer.
    Faes L, Marinazzo D, Montalto A, Nollo G.
    IEEE Trans Biomed Eng; 2014 Oct; 61(10):2556-68. PubMed ID: 24835121
    [Abstract] [Full Text] [Related]

  • 4. Detecting nonlinear causal interactions between dynamical systems by non-uniform embedding of multiple time series.
    Faes L, Nollo G, Erla S, Papadelis C, Braun C, Porta A.
    Annu Int Conf IEEE Eng Med Biol Soc; 2010 Oct; 2010():102-5. PubMed ID: 21095646
    [Abstract] [Full Text] [Related]

  • 5. Assessment of Granger causality by nonlinear model identification: application to short-term cardiovascular variability.
    Faes L, Nollo G, Chon KH.
    Ann Biomed Eng; 2008 Mar; 36(3):381-95. PubMed ID: 18228143
    [Abstract] [Full Text] [Related]

  • 6. Assessing causality in normal and impaired short-term cardiovascular regulation via nonlinear prediction methods.
    Nollo G, Faes L, Antolini R, Porta A.
    Philos Trans A Math Phys Eng Sci; 2009 Apr 13; 367(1892):1423-40. PubMed ID: 19324717
    [Abstract] [Full Text] [Related]

  • 7. Univariate and multivariate conditional entropy measures for the characterization of short-term cardiovascular complexity under physiological stress.
    Valente M, Javorka M, Porta A, Bari V, Krohova J, Czippelova B, Turianikova Z, Nollo G, Faes L.
    Physiol Meas; 2018 Jan 30; 39(1):014002. PubMed ID: 29135467
    [Abstract] [Full Text] [Related]

  • 8. Mixed predictability and cross-validation to assess non-linear Granger causality in short cardiovascular variability series.
    Faes L, Cucino R, Nollo G.
    Biomed Tech (Berl); 2006 Oct 30; 51(4):255-9. PubMed ID: 17061952
    [Abstract] [Full Text] [Related]

  • 9. An adaptive technique for multiscale approximate entropy (MAEbin) threshold (r) selection: application to heart rate variability (HRV) and systolic blood pressure variability (SBPV) under postural stress.
    Singh A, Saini BS, Singh D.
    Australas Phys Eng Sci Med; 2016 Jun 30; 39(2):557-69. PubMed ID: 26939777
    [Abstract] [Full Text] [Related]

  • 10. Prediction of short cardiovascular variability signals based on conditional distribution.
    Porta A, Baselli G, Guzzetti S, Pagani M, Malliani A, Cerutti S.
    IEEE Trans Biomed Eng; 2000 Dec 30; 47(12):1555-64. PubMed ID: 11125590
    [Abstract] [Full Text] [Related]

  • 11. Estimating the decomposition of predictive information in multivariate systems.
    Faes L, Kugiumtzis D, Nollo G, Jurysta F, Marinazzo D.
    Phys Rev E Stat Nonlin Soft Matter Phys; 2015 Mar 30; 91(3):032904. PubMed ID: 25871169
    [Abstract] [Full Text] [Related]

  • 12. Bivariate nonlinear prediction to quantify the strength of complex dynamical interactions in short-term cardiovascular variability.
    Faes L, Nollo G.
    Med Biol Eng Comput; 2006 May 30; 44(5):383-92. PubMed ID: 16937180
    [Abstract] [Full Text] [Related]

  • 13. Mechanisms of causal interaction between short-term RR interval and systolic arterial pressure oscillations during orthostatic challenge.
    Faes L, Nollo G, Porta A.
    J Appl Physiol (1985); 2013 Jun 15; 114(12):1657-67. PubMed ID: 23580598
    [Abstract] [Full Text] [Related]

  • 14. Redundant and synergistic information transfer in cardiovascular and cardiorespiratory variability.
    Faes L, Porta A, Nollo G.
    Annu Int Conf IEEE Eng Med Biol Soc; 2015 Aug 15; 2015():4033-6. PubMed ID: 26737179
    [Abstract] [Full Text] [Related]

  • 15. Towards understanding the complexity of cardiovascular oscillations: Insights from information theory.
    Javorka M, Krohova J, Czippelova B, Turianikova Z, Lazarova Z, Wiszt R, Faes L.
    Comput Biol Med; 2018 Jul 01; 98():48-57. PubMed ID: 29763765
    [Abstract] [Full Text] [Related]

  • 16. Testing frequency-domain causality in multivariate time series.
    Faes L, Porta A, Nollo G.
    IEEE Trans Biomed Eng; 2010 Aug 01; 57(8):1897-906. PubMed ID: 20176533
    [Abstract] [Full Text] [Related]

  • 17. Granger causality in cardiovascular variability series: comparison between model-based and model-free approaches.
    Porta A, Bassani T, Bari V, Guzzetti S.
    Annu Int Conf IEEE Eng Med Biol Soc; 2012 Aug 01; 2012():3684-7. PubMed ID: 23366727
    [Abstract] [Full Text] [Related]

  • 18. Model-free causality analysis of cardiovascular variability detects the amelioration of autonomic control in Parkinson's disease patients undergoing mechanical stimulation.
    Bassani T, Bari V, Marchi A, Tassin S, Dalla Vecchia L, Canesi M, Barbic F, Furlan R, Porta A.
    Physiol Meas; 2014 Jul 01; 35(7):1397-408. PubMed ID: 24875165
    [Abstract] [Full Text] [Related]

  • 19. Nonlinear measures of heart rate time series: influence of posture and controlled breathing.
    Radhakrishna RK, Dutt DN, Yeragani VK.
    Auton Neurosci; 2000 Oct 02; 83(3):148-58. PubMed ID: 11593766
    [Abstract] [Full Text] [Related]

  • 20. Mutual nonlinear prediction of cardiovascular variability series: comparison between exogenous and autoregressive exogenous models.
    Faes L, Porta A, Nollo G.
    Annu Int Conf IEEE Eng Med Biol Soc; 2007 Oct 02; 2007():5955-8. PubMed ID: 18003370
    [Abstract] [Full Text] [Related]

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