ML and MAP estimation of parameters for the Kalman filter and smoother applied to electrocardiographic imaging

Comparison of dipole-based and potential-based ECGI methods for premature ventricular contraction beat localization with clinical data

Introduction: Localization of premature ventricular contraction (PVC) origin to guide the radiofrequency ablation (RFA) procedure is one of the prominent clinical goals of non-invasive electrocardiographic imaging. However, the results reported in the literature vary significantly depending on the source model and the level of complexity in the forward model. This study aims to compare the paced and spontaneous PVC localization performances of dipole-based and potential-based source models and corresponding inverse methods using the same clinical data and to evaluate the effects of torso inhomogeneities on these performances. Methods: The publicly available EP solution data from the EDGAR data repository (BSPs from a maximum of 240 electrodes) with known pacing locations and the Bratislava data (BSPs in 128 leads) with spontaneous PVCs from patients who underwent successful RFA procedures were used. Homogeneous and inhomogeneous torso models and corresponding forward problem solutions were used to relate sources on the closed epicardial and epicardial-endocardial surfaces. The localization error (LE) between the true and estimated pacing site/PVC origin was evaluated. Results: For paced data, the median LE values were 25.2 and 13.9 mm for the dipole-based and potential-based models, respectively. These median LE values were higher for the spontaneous PVC data: 30.2-33.0 mm for the dipole-based model and 28.9-39.2 mm for the potential-based model. The assumption of inhomogeneities in the torso model did not change the dipole-based solutions much, but using an inhomogeneous model improved the potential-based solutions on the epicardial-endocardial ventricular surface. Conclusion: For the specific task of localization of pacing site/PVC origin, the dipole-based source model is more stable and robust than the potential-based source model. The torso inhomogeneities affect the performances of PVC origin localization in each source model differently. Hence, care must be taken in generating patient-specific geometric and forward models depending on the source model representation used in electrocardiographic imaging (ECGI).

A Novel Data-Adaptive Regression Framework Based on Multivariate Adaptive Regression Splines for Electrocardiographic Imaging

OBJECTIVE

Noninvasive electrocardiographic imaging (ECGI) is a promising tool for revealing crucial cardiac electrical events with diagnostic potential. We propose a novel nonparametric regression framework based on multivariate adaptive regression splines (MARS) for ECGI.

METHODS

The inverse problem was solved by using the regression model trained with body surface potentials (BSP) and corresponding electrograms (EGM). Simulated data as well as experimental data from torso-tank experiments were used as to assess the performance of the proposed method. The robustness of the method to measurement noise and geometric errors were assessed in terms of electrogram reconstruction quality, activation time accuracy, and localization error metrics. The methods were compared with Tikhonov regularization and neural network (NN)-based methods. The resulting mapping functions between the BSPs and EGMs were also used to evaluate the most influential measurement leads.

RESULTS

MARS-based method outperformed Tikhonov regularization in terms of reconstruction accuracy and robustness to measurement noise. The effects of geometric errors were remedied to some extent by enriching the training set composition including model errors. The MARS-based method had a comparable performance with NN-based methods, which require the adjustment of many parameters.

CONCLUSION

MARS-based method successfully discovers the inverse mapping functions between the BSPs and EGMs yielding accurate reconstructions, and quantifies the contribution of each BSP lead.

SIGNIFICANCE

MARS-based method is adaptive, requires fewer parameter adjustments than NN-based methods, and robust to errors. Thus, it can be a feasible data-driven approach for accurately solving inverse imaging problems.