Literature DB >> 29223114

Regional conduction velocity calculation from clinical multichannel electrograms in human atria.

Bhawna Verma1, Tobias Oesterlein2, Axel Loewe2, Armin Luik3, Claus Schmitt3, Olaf Dössel2.   

Abstract

BACKGROUND: During atrial fibrillation, heterogeneities and anisotropies result in a chaotic propagation of the depolarization wavefront. The electrophysiological parameter called conduction velocity (CV) influences the propagation pattern over the atrium. We present a method that determines the regional CV for deformed catheter shapes, which result due to the catheter movement and changing wall contact.
METHODS: The algorithm selects stable catheter positions, finds the local activation times (LAT), considers the wall contact and calculates all CV estimates within the area covered by the catheter. The method is evaluated with simulated data and then applied to four clinical data sets. Both sinus rhythm activity as well as depolarization wavefronts initiated by stimulation are analyzed. The regional CV is compared with the fractionation duration (FD) and peak-to-peak (P2P) voltages. A speed of 0.5 m/s was defined to create the simulated LAT.
RESULTS: After analyzing the simulated LAT with clinical catheter spatial coordinates, the median CV of 0.5 m/s with an interquartile range of 0.22 and exact CV direction vectors were obtained. For clinical cases, the CV magnitude range of 0.08 m/s to 1.0 m/s was obtained. The P2P amplitude of 0.7 mV to 3.7 mV and the mean FD from 40.79ms to 48.66ms was obtained. The correlation of 0.86 was observed between CV and P2P amplitude, and 0.62 between CV and FD.
CONCLUSION: In this paper, a method is presented and validated which calculates the CV for the deformed catheter and changing wall contact. In an exemplary clinical data set correlation between regional CV with FD and the P2P voltage was observed.
Copyright © 2017 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Conduction velocity; Contact electrodes; Intracardiac mapping; Local activation time; Sinus rhythm

Mesh:

Year:  2017        PMID: 29223114     DOI: 10.1016/j.compbiomed.2017.11.017

Source DB:  PubMed          Journal:  Comput Biol Med        ISSN: 0010-4825            Impact factor:   4.589


  11 in total

1.  Mechanism and magnitude of bipolar electrogram directional sensitivity: Characterizing underlying determinants of bipolar amplitude.

Authors:  Stephen Gaeta; Tristram D Bahnson; Craig Henriquez
Journal:  Heart Rhythm       Date:  2019-12-13       Impact factor: 6.343

2.  Learning atrial fiber orientations and conductivity tensors from intracardiac maps using physics-informed neural networks.

Authors:  Thomas Grandits; Simone Pezzuto; Francisco Sahli Costabal; Paris Perdikaris; Thomas Pock; Gernot Plank; Rolf Krause
Journal:  Funct Imaging Model Heart       Date:  2021-06-18

3.  Automated conduction velocity estimation based on isochronal activation of heart chambers.

Authors:  Michela Santurri; Jennifer Bonga; Maurizio Schmid; Filippo Maria Cauti; Francesco Solimene; Marco Polselli; Mauro Bura; Francesco Piccolo; Maurizio Malacrida; Gemma Pelargonio; Francesco Raffaele Spera; Stefano Bianchi; Pietro Rossi
Journal:  J Interv Card Electrophysiol       Date:  2022-09-30       Impact factor: 1.759

4.  CVAR-Seg: An Automated Signal Segmentation Pipeline for Conduction Velocity and Amplitude Restitution.

Authors:  Mark Nothstein; Armin Luik; Amir Jadidi; Jorge Sánchez; Laura A Unger; Eike M Wülfers; Olaf Dössel; Gunnar Seemann; Claus Schmitt; Axel Loewe
Journal:  Front Physiol       Date:  2021-05-24       Impact factor: 4.566

5.  Gaussian process manifold interpolation for probabilistic atrial activation maps and uncertain conduction velocity.

Authors:  Sam Coveney; Cesare Corrado; Caroline H Roney; Daniel O'Hare; Steven E Williams; Mark D O'Neill; Steven A Niederer; Richard H Clayton; Jeremy E Oakley; Richard D Wilkinson
Journal:  Philos Trans A Math Phys Eng Sci       Date:  2020-05-25       Impact factor: 4.226

6.  An Uncertainty Modeling Framework for Intracardiac Electrogram Analysis.

Authors:  Amirhossein Koneshloo; Dongping Du; Yuncheng Du
Journal:  Bioengineering (Basel)       Date:  2020-06-26

7.  Patient-Specific Identification of Atrial Flutter Vulnerability-A Computational Approach to Reveal Latent Reentry Pathways.

Authors:  Axel Loewe; Emanuel Poremba; Tobias Oesterlein; Armin Luik; Claus Schmitt; Gunnar Seemann; Olaf Dössel
Journal:  Front Physiol       Date:  2019-01-14       Impact factor: 4.566

8.  A technique for measuring anisotropy in atrial conduction to estimate conduction velocity and atrial fibre direction.

Authors:  Caroline H Roney; John Whitaker; Iain Sim; Louisa O'Neill; Rahul K Mukherjee; Orod Razeghi; Edward J Vigmond; Matthew Wright; Mark D O'Neill; Steven E Williams; Steven A Niederer
Journal:  Comput Biol Med       Date:  2018-11-01       Impact factor: 4.589

9.  High-Resolution Measurement of Local Activation Time Differences From Bipolar Electrogram Amplitude.

Authors:  Stephen Gaeta; Tristram D Bahnson; Craig Henriquez
Journal:  Front Physiol       Date:  2021-04-22       Impact factor: 4.566

10.  A Computational Study of the Electrophysiological Substrate in Patients Suffering From Atrial Fibrillation.

Authors:  S Pagani; L Dede'; A Frontera; M Salvador; L R Limite; A Manzoni; F Lipartiti; G Tsitsinakis; A Hadjis; P Della Bella; A Quarteroni
Journal:  Front Physiol       Date:  2021-07-08       Impact factor: 4.566

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