Localization of Activation Origin on Patient-Specific Endocardial Surface by the Equivalent Double Layer (EDL) Source...

Area of Science:

• Biomedical Engineering
• Computational Electrophysiology
• Medical Imaging

Background:

• Ventricular arrhythmias require precise localization for effective ablation treatment.
• Current electrocardiographic inverse solutions have limitations in accuracy for endocardial activation mapping.
• Pre-procedure planning can significantly improve ablation success rates.

Purpose of the Study:

• To enhance the accuracy of electrocardiographic inverse solutions for localizing arrhythmia origins on the left-ventricular endocardial surface.
• To implement a sparse Bayesian learning (SBL) approach for improved source localization.
• To validate the proposed method in clinical cases and simulation experiments.

Main Methods:

• Reconstructed endocardial potentials from body-surface electrocardiograms using patient-specific geometry.
• Employed sparse Bayesian learning (SBL) with an equivalent-double-layer (EDL) model for cardiac sources.
• Validated the method using clinical data from three patients and simulation experiments with dipole sources.

Main Results:

• The SBL-based method demonstrated significantly smaller mean localization error compared to previous studies.
• Pooled pacing sites (n=52) showed improved localization accuracy.
• Simulation experiments (n=48) localized source dipoles with a mean error of 9.4 ± 4.5 mm.

Conclusions:

• The Bayesian approach utilizing sparse representation of sources by EDL is a feasible and accurate method for localizing left-ventricular endocardial activation.
• This technique offers potential for improved pre-procedure assessment and guidance in arrhythmia ablation.
• The findings support the clinical utility of advanced inverse solutions in electrophysiology.