Authors:
Valentina D. A. Corino
1
;
Frida Sandberg
2
;
Federico Lombardi
3
;
Luca T. Mainardi
1
and
Leif Sörnmo
2
Affiliations:
1
Politecnico di Milano, Italy
;
2
Lund University, Sweden
;
3
University of Milan, Italy
Keyword(s):
Atrial Fibrillation, Atrioventricular Node, Statistical Modeling, Maximum Likelihood Estimation.
Related
Ontology
Subjects/Areas/Topics:
Applications and Services
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Cardiovascular Signals
;
Computer Vision, Visualization and Computer Graphics
;
Medical Image Detection, Acquisition, Analysis and Processing
;
Physiological Processes and Bio-Signal Modeling, Non-Linear Dynamics
Abstract:
This paper introduces a number of advancements of our recently proposed model of atrioventricular (AV) node function during atrial fibrillation (AF). The model is defined by parameters characterizing the arrival rate of atrial impulses, the probability of an impulse choosing either one of the two AV nodal pathways, the refractory periods of these pathways, and their prolongation. In the updated model, the characterization of AV nodal pathways is made more detailed and the number of pathways is determined by the Bayesian information criterion. The performance is evaluated on ECG data acquired from twenty-five AF patients during rest and head-up tilt test. The results show that the refined AV node model provides significantly better fit than did the original model. During tilt, the AF frequency increased (6:25±0:58 Hz vs. 6:32±0:61 Hz, p < 0:05, rest vs. tilt) and the prolongation of the refractory periods decreased for both pathways (slow pathway: 0:23±0:20 s vs. 0:11±0:10 s, p < 0:00
1, rest vs. tilt; fast pathway: 0:24±0:31 s vs. 0:16±0:19 s, p < 0:05, rest vs. tilt). These results show that AV node characteristics can be assessed noninvasively for the purpose of quantifying changes induced by autonomic stimulation.
(More)