Electrocardiographic Imaging of Sinus Rhythm in Pig Hearts Using Bayesian Maximum A Posteriori Estimation

Background: Electrocardiographic imaging (ECGI) has potential to guide physicians to plan treatment strategies. Previously, Bayesian maximum a posteriori (MAP) estimation has been successfully applied to solve this inverse problem for paced data. In this study, we evaluate its effectiveness using experimental data in reconstructing sinus rhythm. Methods: Four datasets from Langendorff-perfused pig hearts, suspended in a human-shaped torso-tank, were used. Each experiment included 3-5 simultaneous electrogram (EGM) and body surface potential (BSP) recordings of 10 beats, in baseline and under dofetilide and pinacidil perfusion. Bayesian MAP estimation and Tikhonov regularization were used to solve the inverse problem. Prior models in MAP were generated using beats from the same recording but excluding the test beat. Pearson's correlation was used to evaluate EGM reconstructions, activation time (AT) maps, and gradient of ATs. Results: In almost all quantitative evaluations and qualitative comparisons of AT maps and epicardial breakthrough sites, MAP outperformed substantially better than Tikhonov regularization. Conclusion: These preliminary results showed that with a "good" prior model, MAP improves over Tikhonov regularization in terms of preventing misdiagnosis of conduction abnormalities associated with arrhythmogenic substrates and identifying epicardial breakthrough sites