Capturing the Influence of Conduction Velocity on Epicardial Activation Patterns Using Uncertainty Quantification.

Area of Science:

• Computational biology
• Cardiac electrophysiology
• Biomedical engineering

Background:

• Accurate simulation of cardiac electrical activity is crucial for understanding heart function and disease.
• Individual variability in parameters like conduction velocity (CV) can affect simulation outcomes.
• Disease states can alter physiological CV, further complicating predictive modeling.

Purpose of the Study:

• To investigate the impact of uncertainty in conduction velocities (CVs) on cardiac electrophysiology simulations.
• To quantify the effects of CV variability across different stimulation sites on the left ventricular (LV) free wall.
• To assess the role of physiological CV variability in both healthy and diseased cardiac states.

Main Methods:

• Bi-ventricular activation simulations were performed to map propagation variability.
• Uncertainty quantification techniques, specifically polynomial chaos expansion (PCE), were employed.
• PCE was used to efficiently explore stochastic variations and reduce computational load via an emulator.

Main Results:

• Conduction velocity (CV) within healthy physiological ranges had a small effect on overall activation times.
• Variation coefficients differed based on stimulation site (LV endocardium, midmyocardium, epicardium).
• Activation time variation was low near early activation sites and increased towards termination sites.

Conclusions:

• While CV variability plays a small role in healthy ranges, it can be significant depending on the stimulation location.
• The impact of CV variability is more pronounced towards the end of cardiac activation.
• Understanding CV variability is important for accurate cardiac simulations in both healthy and diseased conditions.