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Numerical sensitivity analysis of a variational data assimilation procedure for cardiac conductivities
Alessandro Barone1, Flavio Fenton2, Alessandro Veneziani3
1Department of Mathematics and Computer Science, Emory University, Atlanta, Georgia 30322, USA.
Chaos (Woodbury, N.Y.)
|October 2, 2017
Summary
Accurate cardiac conductivity estimation is vital for computational electro-cardiology. This study develops a data assimilation method to refine conductivity values, improving defibrillation shock simulations.
Area of Science:
- Computational electro-cardiology
- Biophysics
- Medical imaging and simulation
Background:
- Accurate cardiac conductivity values are crucial for computational electro-cardiology, but experimental data show significant discrepancies.
- Disagreements in conductivity values and ratios impact the simulation of electrical potential propagation, especially during defibrillation.
- Data assimilation offers a rigorous method to integrate experimental data with numerical simulations.
Purpose of the Study:
- To develop and validate a parameter estimation procedure for cardiac conductivity tensors using variational data assimilation.
- To assess critical experimental factors, including the number and location of measurement sites, for accurate in silico estimations.
- To evaluate the computational efficiency and accuracy of combining Monodomain and Bidomain models for parameter estimation.
Main Methods:
- Variational data assimilation framework minimizing the misfit between simulations and experimental data.
- Utilizing the Bidomain and Monodomain models as the underlying mathematical constraints for parameter estimation.
- Conducting extensive numerical simulations to test the impact of measurement site characteristics.
Main Results:
- Identified optimal lower and upper bounds for the number of measurement sites to ensure accurate and non-redundant parameter estimation.
- Demonstrated that site location is generally non-critical for well-designed experiments.
- Showcased the effectiveness of combining Monodomain and Bidomain models for computational efficiency.
Conclusions:
- The developed data assimilation procedure provides a practical approach for accurate cardiac conductivity estimation.
- The findings offer guidance on experimental design, particularly regarding the number of measurement sites.
- Combining Monodomain and Bidomain models presents a computationally efficient strategy for improving transmembrane potential accuracy in real-world applications.
