Literature DB >> 17540663

Abrupt changes in fibrillatory wave characteristics at the termination of paroxysmal atrial fibrillation in humans.

Simona Petrutiu1, Alan V Sahakian, Steven Swiryn.   

Abstract

AIMS: We investigated the process of spontaneous termination of atrial fibrillation (AF) to determine its time course from the surface ECG. METHODS AND
RESULTS: We studied fibrillatory waves in Holter recordings of paroxysmal and sustained AF. Following QRS-T cancellation dominant frequencies (DFs) were computed and the relationship of DF to termination was scrutinized. For 57 episodes of paroxysmal AF (PAF) in 24 patients, DF ranged from 4.4 to 6.5 Hz (5.2 +/- 0.4 Hz) compared to 5.8 to 7.4 Hz (6.6 +/- 0.6 Hz) for sustained AF recordings. Comparison of the atrial frequency of the ultimate to the penultimate second demonstrated a drop in frequency in 51 of 57 episodes, P < 0.00001. No comparable change was seen at longer time periods. Moments of comparably low frequency without termination were only occasionally seen in patients with PAF but not in patients with sustained AF.
CONCLUSION: Low frequency fibrillation was found to be much more likely to terminate. Frequency changes preceding spontaneous termination were abrupt, in contrast to the gradual frequency drop reported with drug-induced termination. The analysis of fibrillatory wave characteristics and their change over time might be used to target specific moments for pacing therapy in patients with AF.

Entities:  

Mesh:

Year:  2007        PMID: 17540663     DOI: 10.1093/europace/eum096

Source DB:  PubMed          Journal:  Europace        ISSN: 1099-5129            Impact factor:   5.214


  21 in total

1.  Estimating the time scale and anatomical location of atrial fibrillation spontaneous termination in a biophysical model.

Authors:  Laurent Uldry; Vincent Jacquemet; Nathalie Virag; Lukas Kappenberger; Jean-Marc Vesin
Journal:  Med Biol Eng Comput       Date:  2012-01-21       Impact factor: 2.602

2.  CSE database: extended annotations and new recommendations for ECG software testing.

Authors:  Radovan Smíšek; Lucie Maršánová; Andrea Němcová; Martin Vítek; Jiří Kozumplík; Marie Nováková
Journal:  Med Biol Eng Comput       Date:  2016-12-31       Impact factor: 2.602

3.  A Framework for Patient State Tracking by Classifying Multiscalar Physiologic Waveform Features.

Authors:  Benjamin Vandendriessche; Mustafa Abas; Thomas E Dick; Kenneth A Loparo; Frank J Jacono
Journal:  IEEE Trans Biomed Eng       Date:  2017-03-17       Impact factor: 4.538

4.  A Super Fast Algorithm for Estimating Sample Entropy.

Authors:  Weifeng Liu; Ying Jiang; Yuesheng Xu
Journal:  Entropy (Basel)       Date:  2022-04-08       Impact factor: 2.738

5.  Right atrial organization and wavefront analysis in atrial fibrillation.

Authors:  Ulrike Richter; Andreas Bollmann; Daniela Husser; Martin Stridh
Journal:  Med Biol Eng Comput       Date:  2009-10-15       Impact factor: 2.602

6.  Autonomic influence on atrial fibrillatory process: head-up and head-down tilting.

Authors:  Sten Östenson; Valentina D A Corino; Jonas Carlsson; Pyotr G Platonov
Journal:  Ann Noninvasive Electrocardiol       Date:  2016-09-09       Impact factor: 1.468

7.  A Computational Study on the Relation between Resting Heart Rate and Atrial Fibrillation Hemodynamics under Exercise.

Authors:  Matteo Anselmino; Stefania Scarsoglio; Andrea Saglietto; Fiorenzo Gaita; Luca Ridolfi
Journal:  PLoS One       Date:  2017-01-11       Impact factor: 3.240

8.  Mechanisms of stochastic onset and termination of atrial fibrillation studied with a cellular automaton model.

Authors:  Yen Ting Lin; Eugene T Y Chang; Julie Eatock; Tobias Galla; Richard H Clayton
Journal:  J R Soc Interface       Date:  2017-03       Impact factor: 4.118

9.  Meeting the unmet needs of clinicians from AI systems showcased for cardiology with deep-learning-based ECG analysis.

Authors:  Yonatan Elul; Aviv A Rosenberg; Assaf Schuster; Alex M Bronstein; Yael Yaniv
Journal:  Proc Natl Acad Sci U S A       Date:  2021-06-15       Impact factor: 11.205

10.  AFibNet: an implementation of atrial fibrillation detection with convolutional neural network.

Authors:  Bambang Tutuko; Siti Nurmaini; Alexander Edo Tondas; Muhammad Naufal Rachmatullah; Annisa Darmawahyuni; Ria Esafri; Firdaus Firdaus; Ade Iriani Sapitri
Journal:  BMC Med Inform Decis Mak       Date:  2021-07-14       Impact factor: 2.796

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.