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A Spatial Adaptation of the Time Delay Neural Network for Solving ECGI Inverse Problem

hal.structure.identifierModélisation et calculs pour l'électrophysiologie cardiaque [CARMEN]
dc.contributor.authorKAROUI, Amel
hal.structure.identifierInstitut de Mathématiques de Bordeaux [IMB]
dc.contributor.authorBENDAHMANE, Mostafa
hal.structure.identifierModélisation et calculs pour l'électrophysiologie cardiaque [CARMEN]
dc.contributor.authorZEMZEMI, Nejib
dc.contributor.editorYves Coudière
dc.contributor.editorValéry Ozenne
dc.contributor.editorEdward Vigmond
dc.contributor.editorNejib Zemzemi
dc.date.accessioned2024-04-04T03:00:46Z
dc.date.available2024-04-04T03:00:46Z
dc.date.issued2019-05-30
dc.date.conference2019-06-06
dc.identifier.isbn978-3-030-21949-9
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/192845
dc.description.abstractEnThe ECGI inverse problem is still a common area of research. Since the results in the state of the art are not yet satisfactory, exploring new methods for the resolution of the inverse problem of electrocardiography is the main goal of this paper. To this purpose, we suggest to use temporal and spatial constraints to solve the inverse problem using neural networks methods. First, we use a time-delay neural network initialized with the spatial adjacency operator of the heart surface mesh. Then, we suggest a new approach to reconstruct the heart surface potential from the body surface potential using a spatial adaptation of time delay neural network. It consists on taking into account temporal and spatial dependence between potential measures. This allows to exploit the local and dynamic potential propagation properties. We test these approaches on simulated data. Results show that the new approach outperforms the classic time-delay neural network and has considerableimprovements with respect to the state-of-the-art methods.
dc.description.sponsorshipL'Institut de Rythmologie et modélisation Cardiaque - ANR-10-IAHU-0004
dc.description.sponsorshipPlateforme FDSOI pour le node 11nm - ANR-10-EQPX-0030
dc.language.isoen
dc.publisherSpringer
dc.source.titleLecture Notes in Computer Science
dc.subject.enTime-delay neural network
dc.subject.enSpatial adaptation
dc.subject.enAdjacency matrix
dc.subject.enInverse problem
dc.subject.enElectrocardiography
dc.title.enA Spatial Adaptation of the Time Delay Neural Network for Solving ECGI Inverse Problem
dc.typeCommunication dans un congrès
dc.identifier.doi10.1007/978-3-030-21949-9_11
dc.subject.halSciences du Vivant [q-bio]/Médecine humaine et pathologie/Cardiologie et système cardiovasculaire
dc.subject.halInformatique [cs]/Modélisation et simulation
dc.subject.halScience non linéaire [physics]
dc.subject.halMathématiques [math]/Statistiques [math.ST]
dc.subject.halStatistiques [stat]/Machine Learning [stat.ML]
bordeaux.page94-102
bordeaux.volumeLNCS-11504
bordeaux.hal.laboratoriesInstitut de Mathématiques de Bordeaux (IMB) - UMR 5251*
bordeaux.institutionUniversité de Bordeaux
bordeaux.institutionBordeaux INP
bordeaux.institutionCNRS
bordeaux.conference.titleFIMH 2019 - 10th International Conference on Functional Imaging and Modeling of the Heart
bordeaux.countryFR
bordeaux.title.proceedingLecture Notes in Computer Science
bordeaux.conference.cityBordeaux
bordeaux.peerReviewedoui
hal.identifierhal-02154094
hal.version1
hal.invitednon
hal.proceedingsoui
hal.conference.end2019-06-06
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-02154094v1
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.btitle=Lecture%20Notes%20in%20Computer%20Science&rft.date=2019-05-30&rft.volume=LNCS-11504&rft.spage=94-102&rft.epage=94-102&rft.au=KAROUI,%20Amel&BENDAHMANE,%20Mostafa&ZEMZEMI,%20Nejib&rft.isbn=978-3-030-21949-9&rft.genre=unknown


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