Statistical Modeling of Atrioventricular Nodal Function During Atrial Fibrillation Focusing on the Refractory Period Estimation

  • Valentina D. A. CorinoEmail author
  • Frida Sandberg
  • Federico Lombardi
  • Luca T. Mainardi
  • Leif Sörnmo
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 452)


We have recently proposed a statistical AV node model defined by a set of parameters characterizing the arrival rate of atrial impulses, the probability of an impulse passing through the fast or the slow pathway, the refractory periods of the pathways, and the prolongation of refractory periods. All parameters are estimated from the RR interval series using maximum likelihood (ML) estimation, except for the mean arrival rate of atrial impulses which is estimated by the AF frequency derived from the f-waves. In this chapter, we compare four different methods, based either on the Poincaré plot or ML estimation, for determining the refractory period of the slow pathway. Simulation results show better performance of the ML estimator, especially in the presence of artifacts due to premature ventricular beats or misdetected beats. The performance was also evaluated on ECG data acquired from 26 AF patients during rest and head-up tilt test. During tilt, the AF frequency increased (\(6.08 \pm 1.03\) Hz vs. \(6.20 \pm 0.99\) Hz, \(p<0.05\), rest vs. tilt) and the refractory periods of both pathways decreased (slow pathway: \(0.43 \pm 0.12\) s vs. \(0.38 \pm 0.12\) s, \(p=0.001\), rest vs. tilt; fast pathway: \(0.55\pm 0.14\) s vs. \(0.47\pm 0.11\) s, \(p<0.05\), rest vs. tilt). These results show that AV node characteristics can be assessed non-invasively to quantify changes induced by autonomic stimulation.


Atrial fibrillation Atrioventricular node Statistical modeling Maximum likelihood estimation 


  1. 1.
    Fuster, V., Rydén, L.E., Cannom, D.S., Crijns, H.J., Curtis, A.B., et al.: ACC/AHA/ESC 2006 guidelines for the management of patients with atrial fibrillation: a report of the American College of Cardiology/American Heart Association task force on practice guidelines and the European Society of Cardiology committee for practice guidelines. Circ. 114(2006), e257–e354 (2006)Google Scholar
  2. 2.
    Climent, A., de la Salud Guillem, M., Husser, D., Castells, F., Millet, J., Bollmann, A.: Poincaré surface profiles of RR intervals: a novel noninvasive method for the evaluation of preferential AV nodal conduction during atrial fibrillation. IEEE Trans. Biomed. Eng. 56, 433–442 (2009)CrossRefGoogle Scholar
  3. 3.
    Climent, A., Guillem, M., Husser, D., Castells, F., Millet, J., Bollmann, A.: Role of atrial rate as a factor modulating ventricular response during atrial fibrillation. PACE 15, 1–8 (2010)Google Scholar
  4. 4.
    Masè, M., Glass, L., Disertoric, M., Ravelli, F.: The AV synchrogram: a novel approach to quantify atrioventricular coupling during atrial arrhythmias. Biomed. Signal Proc. Control 8, 1008–1016 (2013)CrossRefGoogle Scholar
  5. 5.
    Jørgensen, P., Schäfer, C., Guerra, P.G., Talajic, M., Nattel, S., Glass, L.: A mathematical model of human atrioventricular nodal function incorporating concealed conduction. Bull. Math. Biol. 64, 1083–1099 (2002)CrossRefGoogle Scholar
  6. 6.
    Mangin, L., Vinet, A., Page, P., Glass, L.: Effects of antiarrhythmic drug therapy on atrioventricular nodal function during atrial fibrillation in humans. Europace 7, S71–S82 (2005)CrossRefGoogle Scholar
  7. 7.
    Rashidi, A., Khodarahmi, I.: Nonlinear modeling of the atrioventricular node physiology in atrial fibrillation. J. Theor. Biol. 232, 545–549 (2005)CrossRefGoogle Scholar
  8. 8.
    Lian, J., Müssig, D., Lang, V.: Computer modeling of ventricular rhythm during atrial fibrillation and ventricular pacing. IEEE Trans. Biomed. Eng. 53, 1512–1520 (2006)CrossRefGoogle Scholar
  9. 9.
    Lian, J., Müssig, D.: Heart rhythm and cardiac pacing: an integrated dual-chamber heart and pacer model. Ann. Biomed. Eng. 37, 64–81 (2009)CrossRefGoogle Scholar
  10. 10.
    Corino, V.D.A., Sandberg, F., Mainardi, L.T., Sörnmo, L.: An atrioventricular node model for analysis of the ventricular response during atrial fibrillation. IEEE Trans. Biomed. Eng. 58, 3386–3395 (2011)CrossRefGoogle Scholar
  11. 11.
    Sandberg, F., Stridh, M., Sörnmo, L.: Frequency tracking of atrial fibrillation using hidden Markov models. IEEE Trans. Biomed. Eng. 55, 502–511 (2008)CrossRefGoogle Scholar
  12. 12.
    Corino, V.D.A., Sandberg, F., Mainardi, L.T., Sörnmo, L.: Atrioventricular nodal function during atrial fibrillation: model building and robust estimation. Biomed. Signal Proc. Control 8, 1017–1025 (2013)CrossRefGoogle Scholar
  13. 13.
    Corino, V.D.A., Sandberg, F., Mainardi, L.T., Sörnmo, L.: Statistical modeling of the atrioventricular node during atrial fibrillation: data length and estimator performance. In: Proceedings of 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). vol. 35, pp. 2567–2570 (2013)Google Scholar
  14. 14.
    Van den Bergh, F., Engelbrecht, A.P.: A cooperative approach to particle swarm optimization. IEEE Trans. Evol. Comput. 8, 225–239 (2004)CrossRefGoogle Scholar
  15. 15.
    Niu, B., Zhu, Y., He, X., Wu, H.: MCPSO: a multi-swarm cooperative particle swarm optimizer. Appl. Math. Comput. 2, 1050–1062 (2007)CrossRefGoogle Scholar
  16. 16.
    Brennan, M., Palaniswami, M., Kamen, P.: Do existing measures of Poincaré plot geometry reflect nonlinear features of heart rate variability? IEEE Trans. Biomed. Eng. 48, 1342–1347 (2001)CrossRefGoogle Scholar
  17. 17.
    Anan, T., Araki, K.S.H., Nakamura, M.: Arrhythmia analysis by successive RR plotting. J. Electrocardiol. 23, 243–248 (1990)CrossRefGoogle Scholar
  18. 18.
    Hayano, J., Sakata, S., Okada, A., Mukai, S., Fujinami, T.: Circadian rhythms of atrioventricular conduction properties in chronic atrial fibrillation with and without heart failure-relation between mean heart rate and measures of heart rate variability. J. Am. Coll. Cardiol. 31, 158–166 (1998)CrossRefGoogle Scholar
  19. 19.
    Corino, V.D.A., Climent, A., Mainardi, L.T., Bollmann, A.: Analysis of ventricular response during atrial fibrillation. In: Mainardi, L.T., Sörnmo, L., Cerutti, S. (eds.) Understanding Atrial Fibrillation: The Signal Processing Contribution. Morgan and Claypool (2008)Google Scholar
  20. 20.
    Corino, V.D.A., Sandberg, F., Mainardi, L.T., Sörnmo, L.: Non-invasive, robust estimation of refractory period of atrioventricular node during atrial fibrillation. Int. J. Bioelectromagnetism 15, 41–46 (2013)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Valentina D. A. Corino
    • 1
    Email author
  • Frida Sandberg
    • 2
  • Federico Lombardi
    • 3
  • Luca T. Mainardi
    • 1
  • Leif Sörnmo
    • 2
  1. 1.Dipartimento di Elettronica, Informazione e BioingegneriaPolitecnico di MilanoMilanItaly
  2. 2.Department of Biomedical Engineering and Center for Integrative Electrocardiology (CIEL)Lund UniversityLundSweden
  3. 3.UOC Malattie Cardiovascolari, Fondazione IRCCS Ospedale Maggiore Policlinico, Dipartimento di Scienze Cliniche e di ComunitàUniversity of MilanMilanItaly

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