TY - GEN
T1 - Electrocardiographic Imaging of Sinus Rhythm in Pig Hearts Using Bayesian Maximum A Posteriori Estimation
AU - Dogrusoz, Y.S.
AU - Dubois, R.
AU - Abell, E.
AU - Cluitmans, M.
AU - Bear, L.R.
N1 - Funding Information:
This work was supported in part by the Scientific and Technological Research Council of Turkey grant number 118E244, the French National Research Agency under Contract ANR-10-IAHU04-LIRYC, and the Netherlands Organization for Scientific Research (TTW16772).
Publisher Copyright:
© 2021 Creative Commons.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
KW - NONINVASIVE RECONSTRUCTION
KW - REGULARIZATION
U2 - 10.23919/cinc53138.2021.9662881
DO - 10.23919/cinc53138.2021.9662881
M3 - Conference article in proceeding
T3 - Computing in Cardiology Conference
BT - 2021 COMPUTING IN CARDIOLOGY (CINC)
PB - IEEE
T2 - Conference on Computing in Cardiology (CinC)
Y2 - 12 September 2021 through 15 September 2021
ER -