Computerized optimization of biventricular pacing using body surface potential map.

Area of Science:

• Biomedical Engineering
• Computational Cardiology
• Medical Imaging

Background:

• Biventricular pacing (BVP) is crucial for managing heart failure but requires optimal parameter settings.
• Individual patient variability necessitates personalized approaches to pacemaker optimization.
• Body surface potential mapping (BSPM) offers a non-invasive method to assess cardiac electrophysiology and pathology.

Purpose of the Study:

• To develop and validate a personalized computational heart model for optimizing BVP parameters.
• To simulate cardiac dyssynchrony and myocardial infarction (MI) using patient-specific data.
• To identify optimal electrode configurations for BVP by minimizing QRS duration errors.

Main Methods:

• Utilized BSPM data to parameterize a computational heart model.
• Simulated cardiac electrophysiology with the ten Tusscher cell model and excitation propagation using adaptive cellular automata.
• Optimized electrode positions by minimizing QRS duration error between simulated and clinical ECGs.

Main Results:

• The personalized computational model successfully simulated cardiac dyssynchrony and MI.
• Simulated optimal electrode positions were comparable to clinically determined ones.
• QRS duration reduction errors between simulated and measured ECGs showed a consistent offset of 15 ms.

Conclusions:

• A personalized computational heart model is an effective tool for BVP therapy planning.
• This approach can lead to improved outcomes for patients with congestive heart failure.
• Non-invasive BSPM combined with computational modeling offers a promising strategy for BVP optimization.