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Evaluation of fifteen algorithms for the resolution of the electrocardiography imaging inverse problem using ex-vivo and in-silico data

hal.structure.identifierModélisation et calculs pour l'électrophysiologie cardiaque [CARMEN]
dc.contributor.authorKAROUI, Amel
hal.structure.identifierIHU-LIRYC
dc.contributor.authorBEAR, Laura
hal.structure.identifierUniversité de Bordeaux [UB]
hal.structure.identifierModélisation et calculs pour l'électrophysiologie cardiaque [CARMEN]
hal.structure.identifierIHU-LIRYC
dc.contributor.authorMIGERDITICHAN, Pauline
hal.structure.identifierModélisation et calculs pour l'électrophysiologie cardiaque [CARMEN]
hal.structure.identifierIHU-LIRYC
dc.contributor.authorZEMZEMI, Nejib
dc.date.accessioned2024-04-04T03:04:16Z
dc.date.available2024-04-04T03:04:16Z
dc.date.issued2018-11-28
dc.identifier.issn1664-042X
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/193138
dc.description.abstractEnThe 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.isoen
dc.publisherFrontiers
dc.subject.enRegularization Parameter Choice
dc.subject.enFundamental 20 Solutions Method
dc.subject.enPacing 21 Site Localization
dc.subject.enElectrocardiography
dc.subject.enFinite Elemet Method
dc.subject.enGeneralized Singular Value Decomposition
dc.subject.enTikhonov Regularization
dc.subject.enL1-Norm Regularization
dc.subject.enInverse Problem
dc.subject.enRobust Generalized Cross Validation
dc.title.enEvaluation of fifteen algorithms for the resolution of the electrocardiography imaging inverse problem using ex-vivo and in-silico data
dc.typeArticle de revue
dc.subject.halInformatique [cs]/Modélisation et simulation
dc.subject.halMathématiques [math]/Optimisation et contrôle [math.OC]
dc.subject.halSciences du Vivant [q-bio]/Ingénierie biomédicale/Imagerie
bordeaux.journalFrontiers in Physiology
bordeaux.page1708
bordeaux.volume9
bordeaux.hal.laboratoriesInstitut de Mathématiques de Bordeaux (IMB) - UMR 5251*
bordeaux.institutionUniversité de Bordeaux
bordeaux.institutionBordeaux INP
bordeaux.institutionCNRS
bordeaux.peerReviewedoui
hal.identifierhal-01923763
hal.version1
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-01923763v1
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Frontiers%20in%20Physiology&rft.date=2018-11-28&rft.volume=9&rft.spage=1708&rft.epage=1708&rft.eissn=1664-042X&rft.issn=1664-042X&rft.au=KAROUI,%20Amel&BEAR,%20Laura&MIGERDITICHAN,%20Pauline&ZEMZEMI,%20Nejib&rft.genre=article


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