Evaluation of fifteen algorithms for the resolution of the electrocardiography imaging inverse problem using ex-vivo and in-silico data
hal.structure.identifier | Modélisation et calculs pour l'électrophysiologie cardiaque [CARMEN] | |
dc.contributor.author | KAROUI, Amel | |
hal.structure.identifier | IHU-LIRYC | |
dc.contributor.author | BEAR, Laura | |
hal.structure.identifier | Université de Bordeaux [UB] | |
hal.structure.identifier | Modélisation et calculs pour l'électrophysiologie cardiaque [CARMEN] | |
hal.structure.identifier | IHU-LIRYC | |
dc.contributor.author | MIGERDITICHAN, Pauline | |
hal.structure.identifier | Modélisation et calculs pour l'électrophysiologie cardiaque [CARMEN] | |
hal.structure.identifier | IHU-LIRYC | |
dc.contributor.author | ZEMZEMI, Nejib | |
dc.date.accessioned | 2024-04-04T03:04:16Z | |
dc.date.available | 2024-04-04T03:04:16Z | |
dc.date.issued | 2018-11-28 | |
dc.identifier.issn | 1664-042X | |
dc.identifier.uri | https://oskar-bordeaux.fr/handle/20.500.12278/193138 | |
dc.description.abstractEn | The electrocardiographic imaging inverse problem is ill-posed. Regularization has to be applied to stabilize the problem and solve for a realistic solution. Here, we assess different regularization methods for solving the inverse problem. In this study, we assess i) zero order Tikhonov regularization (ZOT) in conjunction with the Method of Fundamental Solutions (MFS), ii) ZOT regularization using the Finite Element Method (FEM) and iii) the L1-Norm regularization of the current density on the heart surface combined with FEM. Moreover, we apply different approaches for computing the optimal regularization parameter, all based on the Generalized Singular Value Decomposition (GSVD). These methods include Generalized Cross Validation (GCV), Robust Generalized Cross Validation (RGCV), ADPC, U-Curve and Composite REsidual and Smoothing Operator (CRESO) methods. Both simulated and experimental data are used for this evaluation. Results show that the RGCV approach provides the best results to determine the optimal regularization parameter using both the FEM-ZOT and the FEM-L1-Norm. However for the MFS-ZOT, the GCV outperformed all the other regularization parameter choice methods in terms of relative error and correlation coefficient. Regarding the epicardial potential reconstruction, FEM-L1-Norm clearly outperforms the other methods using the simulated data but, using the experimental data, FEM based methods perform as well as MFS. Finally, the use of FEM-L1-Norm combined with RGCV provides robust results in the pacing site localization. | |
dc.language.iso | en | |
dc.publisher | Frontiers | |
dc.subject.en | Regularization Parameter Choice | |
dc.subject.en | Fundamental 20 Solutions Method | |
dc.subject.en | Pacing 21 Site Localization | |
dc.subject.en | Electrocardiography | |
dc.subject.en | Finite Elemet Method | |
dc.subject.en | Generalized Singular Value Decomposition | |
dc.subject.en | Tikhonov Regularization | |
dc.subject.en | L1-Norm Regularization | |
dc.subject.en | Inverse Problem | |
dc.subject.en | Robust Generalized Cross Validation | |
dc.title.en | Evaluation of fifteen algorithms for the resolution of the electrocardiography imaging inverse problem using ex-vivo and in-silico data | |
dc.type | Article de revue | |
dc.subject.hal | Informatique [cs]/Modélisation et simulation | |
dc.subject.hal | Mathématiques [math]/Optimisation et contrôle [math.OC] | |
dc.subject.hal | Sciences du Vivant [q-bio]/Ingénierie biomédicale/Imagerie | |
bordeaux.journal | Frontiers in Physiology | |
bordeaux.page | 1708 | |
bordeaux.volume | 9 | |
bordeaux.hal.laboratories | Institut de Mathématiques de Bordeaux (IMB) - UMR 5251 | * |
bordeaux.institution | Université de Bordeaux | |
bordeaux.institution | Bordeaux INP | |
bordeaux.institution | CNRS | |
bordeaux.peerReviewed | oui | |
hal.identifier | hal-01923763 | |
hal.version | 1 | |
hal.popular | non | |
hal.audience | Internationale | |
hal.origin.link | https://hal.archives-ouvertes.fr//hal-01923763v1 | |
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