Literature DB >> 23559023

Electroanatomical characterization of atrial microfibrosis in a histologically detailed computer model.

Fernando O Campos1, Thomas Wiener, Anton J Prassl, Rodrigo Weber dos Santos, Damian Sanchez-Quintana, Helmut Ahammer, Gernot Plank, Ernst Hofer.   

Abstract

Fibrosis is thought to play an important role in the formation and maintenance of atrial fibrillation (AF). The propensity of fibrosis to increase AF vulnerability depends not only on its amount, its texture plays a crucial role as well. While the detection of fibrotic tissue patches in the atria with extracellular recordings is feasible based on the analysis of electrogram fractionation, as used in clinical practice to identify ablation targets, the classification of fibrotic texture is a more challenging problem. This study seeks to establish a method for the electroanatomical characterization of the fibrotic textures based on the analysis of electrogram fractionation. The proposed method exploits the dependence of fractionation patterns on the incidence direction of wavefronts which differs significantly as a function of texture. A histologically detailed computer model of the right atrial isthmus was developed for testing the method. A stimulation protocol was conceived which generated various incidence directions for any given recording site where electrograms were computed. A classification method is derived then for discriminating three types of fibrosis, no fibrosis (control), diffuse, and patchy fibrosis. Simulation results showed that electrogram fractionation and amplitudes and their dependence upon incidence direction allow a robust discrimination between different classes of fibrosis. Finally, to minimize the technical effort, sensitivity analysis was performed to identify a minimum number of incidence directions required for robust classification.

Entities:  

Mesh:

Year:  2013        PMID: 23559023      PMCID: PMC3786039          DOI: 10.1109/TBME.2013.2256359

Source DB:  PubMed          Journal:  IEEE Trans Biomed Eng        ISSN: 0018-9294            Impact factor:   4.538


  39 in total

1.  Cardiac near-field morphology during conduction around a microscopic obstacle--a computer simulation study.

Authors:  G Plank; E Vigmond; L J Leon; E Hofer
Journal:  Ann Biomed Eng       Date:  2003-11       Impact factor: 3.934

2.  Use of cardiac electric near-field measurements to determine activation times.

Authors:  G Plank; E Hofer
Journal:  Ann Biomed Eng       Date:  2003-10       Impact factor: 3.934

3.  Topology and conduction in the inferior right atrial isthmus measured in rabbit hearts.

Authors:  R Arnold; T Wiener; D Sanchez-Quintana; E Hofer
Journal:  Conf Proc IEEE Eng Med Biol Soc       Date:  2011

Review 4.  New ideas about atrial fibrillation 50 years on.

Authors:  Stanley Nattel
Journal:  Nature       Date:  2002-01-10       Impact factor: 49.962

5.  Is there a relationship between complex fractionated atrial electrograms recorded during atrial fibrillation and sinus rhythm fractionation?

Authors:  Laszlo Saghy; David J Callans; Fermin Garcia; David Lin; Francis E Marchlinski; Michael Riley; Sanjay Dixit; Wendy S Tzou; Harris M Haqqani; Robert Pap; Steven Kim; Edward P Gerstenfeld
Journal:  Heart Rhythm       Date:  2011-09-23       Impact factor: 6.343

6.  Activation delay after premature stimulation in chronically diseased human myocardium relates to the architecture of interstitial fibrosis.

Authors:  T Kawara; R Derksen; J R de Groot; R Coronel; S Tasseron; A C Linnenbank; R N Hauer; H Kirkels; M J Janse; J M de Bakker
Journal:  Circulation       Date:  2001-12-18       Impact factor: 29.690

7.  A mathematical evaluation of the core conductor model.

Authors:  J Clark; R Plonsey
Journal:  Biophys J       Date:  1966-01       Impact factor: 4.033

8.  The terminal crest: morphological features relevant to electrophysiology.

Authors:  D Sánchez-Quintana; R H Anderson; J A Cabrera; V Climent; R Martin; J Farré; S Y Ho
Journal:  Heart       Date:  2002-10       Impact factor: 5.994

9.  A new approach for catheter ablation of atrial fibrillation: mapping of the electrophysiologic substrate.

Authors:  Koonlawee Nademanee; John McKenzie; Erol Kosar; Mark Schwab; Buncha Sunsaneewitayakul; Thaveekiat Vasavakul; Chotikorn Khunnawat; Tachapong Ngarmukos
Journal:  J Am Coll Cardiol       Date:  2004-06-02       Impact factor: 24.094

10.  Decomposition of fractionated local electrograms using an analytic signal model based on sigmoid functions.

Authors:  Thomas Wiener; Fernando O Campos; Gernot Plank; Ernst Hofer
Journal:  Biomed Tech (Berl)       Date:  2012-10       Impact factor: 1.411

View more
  17 in total

Review 1.  Lessons from computer simulations of ablation of atrial fibrillation.

Authors:  Vincent Jacquemet
Journal:  J Physiol       Date:  2016-03-04       Impact factor: 5.182

2.  An Inverse Eikonal Method for Identifying Ventricular Activation Sequences from Epicardial Activation Maps.

Authors:  Thomas Grandits; Karli Gillette; Aurel Neic; Jason Bayer; Edward Vigmond; Thomas Pock; Gernot Plank
Journal:  J Comput Phys       Date:  2020-07-03       Impact factor: 3.553

3.  Reentry and Ectopic Pacemakers Emerge in a Three-Dimensional Model for a Slab of Cardiac Tissue with Diffuse Microfibrosis near the Percolation Threshold.

Authors:  Sergio Alonso; Rodrigo Weber Dos Santos; Markus Bär
Journal:  PLoS One       Date:  2016-11-22       Impact factor: 3.240

4.  Rabbit-specific computational modelling of ventricular cell electrophysiology: Using populations of models to explore variability in the response to ischemia.

Authors:  Philip Gemmell; Kevin Burrage; Blanca Rodríguez; T Alexander Quinn
Journal:  Prog Biophys Mol Biol       Date:  2016-06-16       Impact factor: 3.667

5.  An efficient finite element approach for modeling fibrotic clefts in the heart.

Authors:  Caroline Mendonca Costa; Fernando O Campos; Anton J Prassl; Rodrigo Weber dos Santos; Damián Sánchez-Quintana; Helmut Ahammer; Ernst Hofer; Gernot Plank
Journal:  IEEE Trans Biomed Eng       Date:  2014-03       Impact factor: 4.538

6.  Using Machine Learning to Characterize Atrial Fibrotic Substrate From Intracardiac Signals With a Hybrid in silico and in vivo Dataset.

Authors:  Jorge Sánchez; Giorgio Luongo; Mark Nothstein; Laura A Unger; Javier Saiz; Beatriz Trenor; Armin Luik; Olaf Dössel; Axel Loewe
Journal:  Front Physiol       Date:  2021-07-05       Impact factor: 4.566

7.  Simulation of Ectopic Pacemakers in the Heart: Multiple Ectopic Beats Generated by Reentry inside Fibrotic Regions.

Authors:  Bruno Gouvêa de Barros; Rodrigo Weber dos Santos; Marcelo Lobosco; Sergio Alonso
Journal:  Biomed Res Int       Date:  2015-10-25       Impact factor: 3.411

Review 8.  Computational Approaches to Understanding the Role of Fibroblast-Myocyte Interactions in Cardiac Arrhythmogenesis.

Authors:  Tashalee R Brown; Trine Krogh-Madsen; David J Christini
Journal:  Biomed Res Int       Date:  2015-10-25       Impact factor: 3.411

9.  Automated Texture Analysis and Determination of Fibre Orientation of Heart Tissue: A Morphometric Study.

Authors:  Bernhard Zach; Ernst Hofer; Martin Asslaber; Helmut Ahammer
Journal:  PLoS One       Date:  2016-08-09       Impact factor: 3.240

10.  Microheterogeneity-induced conduction slowing and wavefront collisions govern macroscopic conduction behavior: A computational and experimental study.

Authors:  Tanmay A Gokhale; Huda Asfour; Shravan Verma; Nenad Bursac; Craig S Henriquez
Journal:  PLoS Comput Biol       Date:  2018-07-16       Impact factor: 4.475

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.