Modeling of LBBB and RBBB Abnormal Activations of the Heart

Accurate modeling of the heart's electrical activation is essential for simulating Body Surface Potential Maps (BSPMs) and understanding abnormal conduction patterns. This study investigates the simulation of two pathological conduction conditions—Left Bundle Branch Block (LBBB) and Right Bundle Branch Block (RBBB)—using a previously developed human heart model based on Diffusion Tensor Imaging (DTI). Two conduction network models were tested: a manually constructed network and a diffusion volume (DV)-based automated network. Pathological activation was simulated by applying conduction delays to the corresponding bundle branches. The manual model successfully reproduced electrograms consistent with reference vertical ECG leads, while the automated model failed to replicate realistic bundle splitting due to limitations in the region-growing method. These findings underscore the importance of accurate anatomical modeling in simulating abnormal cardiac activation and suggest areas for future improvement in automated conduction modeling.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-08-31
DOI
https://doi.org/10.5281/zenodo.17499803
Primary Topic
Cardiac electrophysiology and arrhythmias
Type
article
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article

Modeling of LBBB and RBBB Abnormal Activations of the Heart

Zenodo (CERN European Organization for Nuclear Research)
Cardiac electrophysiology and arrhythmias
article

Modeling of LBBB and RBBB Abnormal Activations of the Heart

article en

Abstract

Accurate modeling of the heart's electrical activation is essential for simulating Body Surface Potential Maps (BSPMs) and understanding abnormal conduction patterns. This study investigates the simulation of two pathological conduction conditions—Left Bundle Branch Block (LBBB) and Right Bundle Branch Block (RBBB)—using a previously developed human heart model based on Diffusion Tensor Imaging (DTI). Two conduction network models were tested: a manually constructed network and a diffusion volume (DV)-based automated network. Pathological activation was simulated by applying conduction delays to the corresponding bundle branches. The manual model successfully reproduced electrograms consistent with reference vertical ECG leads, while the automated model failed to replicate realistic bundle splitting due to limitations in the region-growing method. These findings underscore the importance of accurate anatomical modeling in simulating abnormal cardiac activation and suggest areas for future improvement in automated conduction modeling.

Zenodo (CERN European Organization for Nuclear Research)
Qatar University (QA)
Openalex Percentile: Top 35%
Cardiac electrophysiology and arrhythmias
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