Startseite Mixed predictability and cross-validation to assess non-linear Granger causality in short cardiovascular variability series
Artikel
Lizenziert
Nicht lizenziert Erfordert eine Authentifizierung

Mixed predictability and cross-validation to assess non-linear Granger causality in short cardiovascular variability series

  • Luca Faes , Roberta Cucino und Giandomenico Nollo
Veröffentlicht/Copyright: 25. Oktober 2006
Veröffentlichen auch Sie bei De Gruyter Brill
Biomedical Engineering / Biomedizinische Technik
Aus der Zeitschrift Band 51 Heft 4

Abstract

A method to evaluate the direction and strength of causal interactions in bivariate cardiovascular and cardiorespiratory series is presented. The method is based on quantifying self and mixed predictability of the two series using nearest-neighbour local linear approximation. It returns two causal coupling indexes measuring the relative improvement in predictability along direct and reverse directions, and a directionality index indicating the preferential direction of interaction. The method was implemented through a cross-validation approach that allowed quantification of directionality without constraining the embedding of the series, and fully exploited the available data to maximise the prediction accuracy. Validation on short simulated bivariate time series demonstrated the ability of the method to capture different degrees of unidirectional and bidirectional interaction. Moreover, application to representative examples of heart rate, systolic arterial pressure and respiration series allowed the inference of causal relationships related to known physiological mechanisms and experimental conditions.


Corresponding author: Luca Faes, Laboratorio Biosegnali, Dipartimento di Fisica, Università di Trento, via Sommarive 14, 38050 Povo, Trento, Italy Phone: +39-0461-882041 Fax: +39-0461-881696

References

1 Granger CWJ. Investigating causal relations by econometric models and cross-spectral methods. Econometrica1969; 37: 424–438.10.2307/1912791Suche in Google Scholar

2 Schafer C, Rosenblum MG, Abel HH, Kurths J. Synchronization in the human cardiorespiratory system. Phys Rev E1999; 60: 857–870.10.1103/PhysRevE.60.857Suche in Google Scholar

3 Faes L, Porta A, Cucino R, Cerutti S, Antolini R, Nollo G. Causal transfer function analysis to describe closed loop interactions between cardiovascular and cardiorespiratory variability signals. Biol Cybern2004; 90: 390–399.10.1007/s00422-004-0488-0Suche in Google Scholar

4 Nollo G, Faes L, Porta A, Antolini R, Ravelli F. Exploring directionality in spontaneous heart period and systolic pressure variability interactions in humans. Implications in baroreflex gain evaluation. Am J Physiol2005; 288: H1777–H1785.10.1152/ajpheart.00594.2004Suche in Google Scholar

5 Chen Y, Rangarajan G, Feng J, Ding M. Analyzing multiple nonlinear time series with extended Granger causality. Phys Lett A2004; 324: 26–35.10.1016/j.physleta.2004.02.032Suche in Google Scholar

6 Theiler J, Eubank S, Longtin A, Galdrikian B, Farmer JD. Testing for nonlinearity in time series: the method of surrogate data. Physica D1992; 58: 77–94.10.1016/0167-2789(92)90102-SSuche in Google Scholar

7 Schiff SJ, So P, Chang T, Burke RE, Sauer T. Detecting dynamical interdependence and generalized synchrony through mutual prediction in a neural ensemble. Phys Rev E1996; 54: 6708–6724.10.1103/PhysRevE.54.6708Suche in Google Scholar

8 Wiesenfeldt M, Parlitz U, Lauterborn W. Mixed state analysis of multivariate time series. Int J Bifurcat Chaos2001; 11: 2217–2226.10.1142/S0218127401003231Suche in Google Scholar

9 Bhattacharya J, Pereda E, Petsche H. Effective detection of coupling in short and noisy bivariate data. IEEE Trans Syst Man Cybernet B2003; 33: 85–95.10.1109/TSMCB.2003.808175Suche in Google Scholar PubMed

10 Quian Quiroga R, Arnhold J, Grassberger P. Learning driver-response relationships from synchronization patterns. Phys Rev E2000; 61: 5142–5148.10.1103/PhysRevE.61.5142Suche in Google Scholar PubMed

11 Schreiber T. Interdisciplinary application of nonlinear time series methods. Phys Rep1999; 308: 1–64.10.1016/S0370-1573(98)00035-0Suche in Google Scholar

12 Malliani A. Principles of cardiovascular neural regulation in health and disease. 1st edition. Norwell, MA: Kluwer Academic Publishers 2000.10.1007/978-1-4615-4383-1Suche in Google Scholar

13 Hirsch JA, Bishop B. Respiratory sinus arrhythmia in humans: how breathing pattern modulates heart rate. Am J Physiol1981; 241: H620–H629.10.1152/ajpheart.1981.241.4.H620Suche in Google Scholar PubMed

Published Online: 2006-10-25
Published in Print: 2006-10-01

©2006 by Walter de Gruyter Berlin New York

Artikel in diesem Heft

  1. ESGCO 2006 Conference and Meeting of the European Study Group on Cardiovascular Oscillations, Jena, Germany, May 15–17, 2006
  2. Cardiovascular Oscillations: from methods and models to clinical applications
  3. Circadian and ultradian rhythms in heart rate variability
  4. Influence of age, body mass index, and blood pressure on the carotid intima-media thickness in normotensive and hypertensive patients
  5. Multivariate and multidimensional analysis of cardiovascular oscillations in patients with heart failure
  6. Multivariate and multiorgan analysis of cardiorespiratory variability signals: the CAP sleep case
  7. Role of the autonomic nervous system in generating non-linear dynamics in short-term heart period variability
  8. Non-linear dynamic analysis of the cardiac rhythm during transient myocardial ischemia
  9. Complex autonomic dysfunction in cardiovascular, intensive care, and schizophrenic patients assessed by autonomic information flow
  10. Low HRV entropy is strongly associated with myocardial infarction
  11. Revisiting the potential of time-domain indexes in short-term HRV analysis
  12. Fractal dimension in health and heart failure
  13. Spatiotemporal correlation analyses: a new procedure for standardisation of DC magnetocardiograms
  14. Changes in heart rate variability of athletes during a training camp
  15. The missing link between cardiovascular rhythm control and myocardial cell modeling
  16. Model of the sino-atrial and atrio-ventricular nodes of the conduction system of the human heart
  17. Modelling long-term heart rate variability: an ARFIMA approach
  18. Clinical correlates of non-linear indices of heart rate variability in chronic heart failure patients
  19. Recurrence analysis of nocturnal heart rate in sleep apnea patients
  20. Normalized correlation dimension for heart rate variability analysis
  21. Complexity of heart rate fluctuations in near-term sheep and human fetuses during sleep
  22. Differences between heart rate and blood pressure variability in schizophrenia
  23. Influence of sympathetic vascular regulation on heart-rate scaling structure: spinal cord lesion as a model of progressively impaired autonomic control
  24. Increase in regularity of fetal heart rate variability with age
  25. Fetal heart rate variability in growth restricted fetuses
  26. Frequency modulation between low- and high-frequency components of the heart rate variability spectrum
  27. Mixed predictability and cross-validation to assess non-linear Granger causality in short cardiovascular variability series
  28. Assessment of spatial organization in the atria during paroxysmal atrial fibrillation and adrenergic stimulation
  29. Attenuated autonomic function in multiple organ dysfunction syndrome across three age groups
  30. Central vasopressin V1a and V1b receptors modulate the cardiovascular response to air-jet stress in conscious rats
  31. Heart rate asymmetry by Poincaré plots of RR intervals
  32. Analyses of cardiovascular oscillations for enhanced diagnosis and risk stratification in cardiac diseases and disorders
Heruntergeladen am 7.11.2025 von https://www.degruyterbrill.com/document/doi/10.1515/BMT.2006.050/html
Button zum nach oben scrollen