Introduction: Electrocardiographic Imaging is a non-invasive technique that requires cardiac Imaging for the reconstruction of cardiac electrical activity. In this study, we explored imageless ECGI by quantifying the errors of using heart meshes with either an inaccurate location inside the thorax or an inaccurate geometry.
Methods: Multiple‑lead body surface recordings of 25 atrial fibrillation (AF) patients were recorded. Cardiac atrial meshes were obtained by segmentation of medical images obtained for each patient. ECGI was computed with each patient's segmented atrial mesh and compared with the ECGI obtained under errors in the atrial mesh used for ECGI estimation. We modeled both the uncertainty in the location of the atria inside the thorax by artificially translating the atria inside the thorax and the geometry of the atrial mesh by using an atrial mesh in a reference database. ECGI signals obtained with the actual meshes and the translated or estimated meshes were compared in terms of their correlation coefficients, relative difference measurement star, and errors in the dominant frequency (DF) estimation in epicardial nodes.
Results: CC between ECGI signals obtained after translating the actual atrial meshes from the original position by 1 cm was above 0.97. CC between ECGIs obtained with patient specific atrial geometry and estimated atrial geometries was 0.93 ± 0.11. Mean errors in DF estimation using an estimated atrial mesh were 7.6 ± 5.9%.
Conclusion: Imageless ECGI can provide a robust estimation of cardiac electrophysiological parameters such as activation rates even during complex arrhythmias. Furthermore, it can allow more widespread use of ECGI in clinical practice.
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http://dx.doi.org/10.1016/j.jelectrocard.2022.12.007 | DOI Listing |
JACC Clin Electrophysiol
November 2024
Department of Cardiology, Institute of Science Tokyo, Tokyo, Japan.
Background: Conventional endocardial mapping cannot fully elucidate Marshall bundle (MB)-related atrial tachycardia (AT).
Objectives: This study aimed to clarify the clinical and electrophysiological characteristics of MB-related AT definitively diagnosed using catheter insertion.
Methods: Forty-eight patients with AT who had previously undergone mitral isthmus ablation were enrolled in this study.
Nat Commun
November 2024
William Harvey Research Institute, Queen Mary University London, Charterhouse Square, London, UK.
Circ Arrhythm Electrophysiol
November 2024
Department of Physics and Astronomy, Ghent University, Belgium (R.V.d.A., N.C., A.S.B., B.V., S.L., K.D., A.O., T.N., S.H., N.V.).
Comput Methods Programs Biomed
December 2024
School of Physics, Georgia Institute of Technology, Atlanta, GA, USA.
Background And Objective: Numerical simulations are valuable tools for studying cardiac arrhythmias. Not only do they complement experimental studies, but there is also an increasing expectation for their use in clinical applications to guide patient-specific procedures. However, numerical studies that solve the reaction-diffusion equations describing cardiac electrical activity remain challenging to set up, are time-consuming, and in many cases, are prohibitively computationally expensive for long studies.
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October 2024
UCL Mechanical Engineering, University College of London, London, United Kingdom.
Introduction: Statistical shape analysis (SSA) with clustering is often used to objectively define and categorise anatomical shape variations. However, studies until now have often focused on simplified anatomical reconstructions, despite the complexity of studied anatomies. This work aims to provide insights on the anatomical detail preservation required for SSA of highly diverse and complex anatomies, with particular focus on the left atrial appendage (LAA).
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