Sensitivity analysis of cardiac electrophysiological models using polynomial chaos

Area of Science:

• Biophysics
• Computational Biology
• Mathematical Modeling

Background:

• Mathematical models are crucial for reconstructing experimental data and predicting biological behavior.
• Quantifying parameter sensitivity is essential for determining accuracy and understanding model behavior.
• Stochastic parameter investigation can reveal model limitations and suggest reductions.

Purpose of the Study:

• To present polynomial chaos as an efficient computational method.
• To assess the impact of stochastic parameters on model predictions.
• To evaluate this method on cardiac electrophysiological models.

Main Methods:

• Utilized polynomial chaos expansion.
• Applied to cardiac electrophysiological models.
• Compared computational efficiency against Monte Carlo methods.

Main Results:

• Polynomial chaos provides a computationally efficient alternative to Monte Carlo.
• Demonstrated the impact of stochastic parameters on model predictions.
• Successfully applied to analyze cardiac electrophysiological models.

Conclusions:

• Polynomial chaos is a viable and efficient tool for sensitivity analysis in biophysical models.
• This approach enhances understanding of parameter uncertainty in cardiac electrophysiology.
• Offers a pathway for model refinement and improved predictive accuracy.