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

Alba Martin-Yebra, Mikael Henriksson, Monika Butkuviene, Vaidotas Marozas, Andrius Petrenas, Aleksei Savelev, Pyotr G. Platonov, Leif Sornmo

Research output: Chapter in Book/Report/Conference proceedingPaper in conference proceedingpeer-review


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.

Original languageEnglish
Title of host publication2020 Computing in Cardiology, CinC 2020
PublisherIEEE Computer Society
ISBN (Electronic)9781728173825
Publication statusPublished - 2020
Event2020 Computing in Cardiology, CinC 2020 - Rimini, Italy
Duration: 2020 Sept 132020 Sept 16

Publication series

NameComputing in Cardiology
ISSN (Print)2325-8861
ISSN (Electronic)2325-887X


Conference2020 Computing in Cardiology, CinC 2020

Subject classification (UKÄ)

  • Cardiac and Cardiovascular Systems


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