scholarly journals Association of Atrial Fibrillation Episode Duration With Arrhythmia Recurrence Following Ablation

2020 ◽  
Vol 3 (7) ◽  
pp. e208748 ◽  
Author(s):  
Jason G. Andrade ◽  
Marc W. Deyell ◽  
Atul Verma ◽  
Laurent Macle ◽  
Jean Champagne ◽  
...  
2017 ◽  
Vol 26 (1) ◽  
pp. 109-113 ◽  
Author(s):  
Michał Peller ◽  
Piotr Lodziński ◽  
Krzysztof Ozierański ◽  
Agata Tymińska ◽  
Paweł Balsam ◽  
...  

2021 ◽  
Vol 34 ◽  
pp. 100791
Author(s):  
Victoria Jansson ◽  
Lennart Bergfeldt ◽  
Jonas Schwieler ◽  
Göran Kennebäck ◽  
Aigars Rubulis ◽  
...  

Circulation ◽  
2014 ◽  
Vol 130 (suppl_2) ◽  
Author(s):  
Rod S Passman ◽  
Jodi L Koehler ◽  
Paul D Ziegler

Introduction: Initial episodes of atrial fibrillation (AF) detected following a cryptogenic stroke (CS) may be brief in duration and the clinical relevance of such episodes is uncertain. Hypothesis: We investigated whether an initial brief episode of AF was predictive of subsequent long duration AF episodes in CS patients (pts) with an insertable cardiac monitor (ICM). Methods: CS pts (n=208, age 61.6±11.3 years, 66% male) randomized to the ICM arm of the CRYSTAL-AF study and inserted with a device (Reveal® XT) were followed for 21±9 months. AF episodes (>30 seconds) were independently adjudicated and the first adjudicated AF episode was classified as brief (<1 hour) or long (≥1 hour). The incidence of subsequent long duration AF episodes among pts with an initially brief episode was computed. The impact of episode duration on prescription of oral anticoagulation (OAC) therapy was also assessed. Results: Among 36 pts with an adjudicated AF episode for which duration information was available, the initial episode was classified as brief in 18 (50%) pts and long in 18 (50%) pts. Among those with initially brief episodes, 10 (56%) experienced only subsequent brief episodes while 8 (44%) went on to experience at least one long AF episode. The median time between the initial brief episode and first long AF episode was 75 days [interquartile range: 27-624 days]. OAC was prescribed in 7/10 pts (70%) with only brief AF episodes compared to 26/26 pts (100%) with at least one long episode of AF (p=0.017). Conclusion: Initial AF episodes in pts with CS are equally likely to be of short or long duration. However, nearly half of CS pts with initially brief episodes of AF subsequently have long duration episodes detected much later via prolonged monitoring with ICMs. Therefore, early detection of brief AF episodes may merit more rigorous monitoring of AF with ICMs since physicians were significantly more likely to prescribe OAC for secondary stroke prevention in response to longer duration episodes.


2004 ◽  
Vol 43 (01) ◽  
pp. 94-98 ◽  
Author(s):  
S. Mota ◽  
F. J. Toro ◽  
A. F. Díaz ◽  
F. J. Fernández ◽  
E. Ros

Summary Objectives: The objective of the paper is to describe an automatic algorithm for Paroxysmal Atrial Fibrillation (PAF) Detection, based on parameters extracted from ECG traces with no atrial fibrillation episode. The modular automatic classification algorithm for PAF diagnosis is developed and evaluated with different parameter configurations. Methods: The database used in this study was provided by Physiobank for The Computers in Cardiology Challenge 2001. Each ECG file in this database was translated into a 48 parameter vector. The modular classification algorithm used for PAF diagnosis was based on the nearest K-neighbours. Several configuration options were evaluated to optimize the classification performance. Results: Different configurations of the proposed modular classification algorithm were tested. The uni-parametric approach achieved a top classification rate value of 76%. A multi-parametric approach was configured using the 5 parameters with highest discrimination power, and a top classification rate of 80% was achieved; different functions to typify the parameters were tested. Finally, two automatic parametric scanning strategies, Forward and Backward methods, were adopted. The results obtained with these approaches achieved a top classification rate of 92%. Conclusions: A modular classification algorithm based on the nearest K-neighbours was designed. The classification performance of the algorithm was evaluated using different parameter configurations, typification functions and number of K-neighbors. The automatic parametric scanning techniques achieved much better results than previously tested configurations.


2021 ◽  
Author(s):  
Monika Butkuviene ◽  
Andrius Petrenas ◽  
Andrius Solosenko ◽  
Alba Martin-Yebra ◽  
Vaidotas Marozas ◽  
...  

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