Key result
Adaptive finite-element mesh refinement cuts inverse ECG solution errors by ~52% in torso models.
Why the study?
Numerical techniques for solving the ECG inverse problem require strategies to quantify and minimize approximation errors introduced by discretization, which affect problem conditioning and solution accuracy.
Answers are grounded in this paper and cite their sources.
May improve inverse solution accuracy in 2-D models; leaves open clinical translation and 3-D validation.
Successful employment of numerical techniques for the solution of forward and inverse ECG problems requires the ability to both quantify and minimize approximation errors introduced as part of the discretization process. Our objective is to develop discretization and refinement strategies involving hybrid-shaped finite elements so as to minimize approximation errors for the ECG inverse problem. We examine both the ill-posedness of the mathematical inverse problem and the ill-conditioning of the discretized system in order to propose strategies specifically designed for the ECG inverse problem. We demonstrate that previous discretization and approximation strategies may worsen the properties of the inverse problem approximation. We then demonstrate the efficacy of our strategies on both a simplified and a realistic 2-D torso model.
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Wang et al. (2009) studied ECG inverse problem. Adaptive finite-element discretization and refinement strategies (volume and normal direction) vs. Uniform or coarse mesh refinements was evaluated on Inverse solution relative error and max-gradient location error. Adaptive finite-element refinement strategies, particularly increasing volume conductor resolution, improved the transfer matrix condition and reduced inverse solution errors (e.g., from 8.81% to 4.19%) in 2-D torso models.
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