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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)

Abstract

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.

Keywords

Atrial fibrillation Atrioventricular node Statistical modeling Maximum likelihood estimation 

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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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