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

Corino, Valentina D. A.; Sandberg, Frida LU ; Lombardi, Federico; Mainardi, Luca T. and Sörnmo, Leif LU (2013) In Biomedical Signal Processing and Control 8(6). p.1017-1025
Abstract
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 Poincare 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... (More)
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 Poincare 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 +/- 0.19s, 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. (C) 2012 Elsevier Ltd. All rights reserved. (Less)
Please use this url to cite or link to this publication:
author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Atrial fibrillation, Atrioventricular node, Statistical modeling, Maximum likelihood estimation, Pathway selection, Bayesian information, criterion
in
Biomedical Signal Processing and Control
volume
8
issue
6
pages
1017 - 1025
publisher
Elsevier
external identifiers
  • wos:000329885000060
  • scopus:84886556826
ISSN
1746-8094
DOI
10.1016/j.bspc.2012.10.006
language
English
LU publication?
yes
id
b6c9cdc9-fd8d-4fa0-96a0-04199e6585e1 (old id 4319312)
date added to LUP
2014-02-25 15:41:05
date last changed
2019-02-20 05:27:39
@article{b6c9cdc9-fd8d-4fa0-96a0-04199e6585e1,
  abstract     = {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 Poincare 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 &lt;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 &lt;0.001, rest vs. tilt; fast pathway: 0.24 +/- 0.31 s vs. 0.16 +/- 0.19s, p &lt;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. (C) 2012 Elsevier Ltd. All rights reserved.},
  author       = {Corino, Valentina D. A. and Sandberg, Frida and Lombardi, Federico and Mainardi, Luca T. and Sörnmo, Leif},
  issn         = {1746-8094},
  keyword      = {Atrial fibrillation,Atrioventricular node,Statistical modeling,Maximum likelihood estimation,Pathway selection,Bayesian information,criterion},
  language     = {eng},
  number       = {6},
  pages        = {1017--1025},
  publisher    = {Elsevier},
  series       = {Biomedical Signal Processing and Control},
  title        = {Atrioventricular nodal function during atrial fibrillation: Model building and robust estimation},
  url          = {http://dx.doi.org/10.1016/j.bspc.2012.10.006},
  volume       = {8},
  year         = {2013},
}