Atrioventricular nodal function during atrial fibrillation: Model building and robust estimation

Valentina D A Corino, Frida Sandberg, Federico Lombardi, Luca T. Mainardi, Leif Sörnmo

Research output: Contribution to journalArticlepeer-review


Statistical modeling of atrioventricular (AV) nodal function during atrial fibrillation (AF) is revisited for the purpose of defining model properties and improving parameter estimation. The characterization of AV nodal pathways is made more detailed and the number of pathways is now determined by the Bayesian information criterion, rather than just producing a probability as was previously done. Robust estimation of the shorter refractory period (i.e., of the slow pathway) is accomplished by a Hough-based technique which is applied to a Poincaré plot of RR intervals. The performance is evaluated on simulated data as well as on ECG data acquired from AF patients during rest and head-up tilt test. The simulation results suggest that the refractory period of the slow pathway can be accurately estimated even in the presence of many artifacts. They also show that the number of pathways can be accurately determined. The results from ECG data show that the refined AV node model provides significantly better fit than did the original model, increasing from 85 ± 5% to 88 ± 4% during rest, and from 86 ± 5% to 87 ± 3% during tilt. When assessing the effect of sympathetic stimulation, the AF frequency increased significantly during tilt (6.25 ± 0.58 Hz vs. 6.32 ± 0.61 Hz, p <0.05, rest vs. tilt) and the prolongation of the refractory periods of both pathways decreased significantly (slow pathway: 0.23 ± 0.20 s vs. 0.11 ± 0.10 s, p <0.001, rest vs. tilt; fast pathway: 0.24 ± 0.31 s vs. 0.16 ± 0.19 s, p <0.05, rest vs. tilt). The results show that AV node characteristics can be assessed noninvasively for the purpose of quantifying changes induced by autonomic stimulation.

Original languageEnglish
Pages (from-to)1017-1025
Number of pages9
JournalBiomedical Signal Processing and Control
Issue number6
Publication statusPublished - Nov 2013


  • Atrial fibrillation
  • Atrioventricular node
  • Bayesian information criterion
  • Maximum likelihood estimation
  • Pathway selection
  • Statistical modeling

ASJC Scopus subject areas

  • Health Informatics
  • Signal Processing


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