A conduction velocity adapted eikonal model for electrophysiology problems with re-excitability evaluation.

Area of Science:

• Computational physiology
• Cardiac electrophysiology modeling

Background:

• Detailed computational models of heart electrophysiology are valuable for studying cardiac pathologies but are limited by high computational costs and long simulation times.
• Existing models often lack the speed and accuracy required for clinical applications.

Purpose of the Study:

• To develop a novel multi-front eikonal algorithm coupled with the Mitchell-Schaeffer (MS) ionic model to improve the efficiency and accuracy of cardiac electrophysiology simulations.
• To adapt the conduction velocity (CV) based on local tissue electrical state, including transmembrane potential, ionic variables, action potential duration (APD), and diastolic interval (DI).

Main Methods:

• Developed a multi-front eikonal algorithm that dynamically adjusts conduction velocity (CV).
• Coupled the eikonal algorithm with the Mitchell-Schaeffer (MS) ionic model to compute local electrophysiological variables (transmembrane potential, ionic variables, APD, DI).
• Implemented an analytical expression for CV restitution based on computed local DI to adapt CV.
• Performed simulations on a 3D tissue slab and a realistic heart geometry, comparing results with the standard monodomain equation.

Main Results:

• The proposed model demonstrated significantly higher accuracy compared to the standard eikonal model.
• The model successfully adapted CV based on the local electrophysiological state and tissue activation frequency.
• Simulations were performed on a clinical time scale, indicating improved computational efficiency.

Conclusions:

• The novel multi-front eikonal algorithm coupled with the MS ionic model provides a more accurate and computationally efficient approach to simulating cardiac electrophysiology.
• This model's ability to simulate heart electrophysiology on a clinical time scale makes it a promising candidate for computer-guided interventions such as radio-frequency ablation.