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Model-Based Characterization of Atrial Fibrillation Episodes and its Clinical Association

Martin-Yebra, Alba LU ; Henriksson, Mikael LU ; Butkuviene, Monika ; Marozas, Vaidotas ; Petrenas, Andrius ; Savelev, Aleksei ; Platonov, Pyotr G. LU and Sornmo, Leif LU (2020) 2020 Computing in Cardiology, CinC 2020 In Computing in Cardiology 2020-September.
Abstract

Studies investigating risk factors associated with atrial fibrillation (AF) have mostly focused on AF presence and burden, disregarding the temporal distribution of AF episodes although such information can be relevant. In the present study, the alternating, bivariate Hawkes model was used to characterize paroxysmal AF episode patterns. Two parameters: the intensity ratio µ, describing the dominating rhythm (AF or non-AF) and the exponential decay ß 1, providing information on clustering, were investigated in relation to AF burden and atrial echocardiographic measurements. Both µ and ß1were weakly correlated with atrial volume (r=0.19 and r=0.34, respectively), whereas µ was correlated with atrial strain (r=-0.74, p=0.1) and AF burden... (More)

Studies investigating risk factors associated with atrial fibrillation (AF) have mostly focused on AF presence and burden, disregarding the temporal distribution of AF episodes although such information can be relevant. In the present study, the alternating, bivariate Hawkes model was used to characterize paroxysmal AF episode patterns. Two parameters: the intensity ratio µ, describing the dominating rhythm (AF or non-AF) and the exponential decay ß 1, providing information on clustering, were investigated in relation to AF burden and atrial echocardiographic measurements. Both µ and ß1were weakly correlated with atrial volume (r=0.19 and r=0.34, respectively), whereas µ was correlated with atrial strain (r=-0.74, p=0.1) and AF burden (r=0.68, p=0.05). Weak correlation between ß1 and AF burden was found (r=0.29). Atrial structural remodeling is associated with changes in AF characteristics, often manifested as episodes of increasing duration, thus µ may reflect the degree of atrial electrical and structural remodeling. Moreover, clustering information (ß1) is complementary information to AF burden, which may be useful for understanding arrhythmia progression and risk assessment of ischemic stroke.

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author
; ; ; ; ; ; and
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
host publication
2020 Computing in Cardiology, CinC 2020
series title
Computing in Cardiology
volume
2020-September
article number
9344171
publisher
IEEE Computer Society
conference name
2020 Computing in Cardiology, CinC 2020
conference location
Rimini, Italy
conference dates
2020-09-13 - 2020-09-16
external identifiers
  • scopus:85100920355
ISSN
2325-8861
2325-887X
ISBN
9781728173825
DOI
10.22489/CinC.2020.232
language
English
LU publication?
yes
id
cdabf6f8-5bab-4a43-97fd-88227a5b97ae
date added to LUP
2021-03-05 11:02:21
date last changed
2022-07-20 18:33:31
@inproceedings{cdabf6f8-5bab-4a43-97fd-88227a5b97ae,
  abstract     = {{<p>Studies investigating risk factors associated with atrial fibrillation (AF) have mostly focused on AF presence and burden, disregarding the temporal distribution of AF episodes although such information can be relevant. In the present study, the alternating, bivariate Hawkes model was used to characterize paroxysmal AF episode patterns. Two parameters: the intensity ratio µ, describing the dominating rhythm (AF or non-AF) and the exponential decay ß 1, providing information on clustering, were investigated in relation to AF burden and atrial echocardiographic measurements. Both µ and ß1were weakly correlated with atrial volume (r=0.19 and r=0.34, respectively), whereas µ was correlated with atrial strain (r=-0.74, p=0.1) and AF burden (r=0.68, p=0.05). Weak correlation between ß1 and AF burden was found (r=0.29). Atrial structural remodeling is associated with changes in AF characteristics, often manifested as episodes of increasing duration, thus µ may reflect the degree of atrial electrical and structural remodeling. Moreover, clustering information (ß1) is complementary information to AF burden, which may be useful for understanding arrhythmia progression and risk assessment of ischemic stroke.</p>}},
  author       = {{Martin-Yebra, Alba and Henriksson, Mikael and Butkuviene, Monika and Marozas, Vaidotas and Petrenas, Andrius and Savelev, Aleksei and Platonov, Pyotr G. and Sornmo, Leif}},
  booktitle    = {{2020 Computing in Cardiology, CinC 2020}},
  isbn         = {{9781728173825}},
  issn         = {{2325-8861}},
  language     = {{eng}},
  publisher    = {{IEEE Computer Society}},
  series       = {{Computing in Cardiology}},
  title        = {{Model-Based Characterization of Atrial Fibrillation Episodes and its Clinical Association}},
  url          = {{http://dx.doi.org/10.22489/CinC.2020.232}},
  doi          = {{10.22489/CinC.2020.232}},
  volume       = {{2020-September}},
  year         = {{2020}},
}