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Benchmark of deep learning algorithms for the automatic screening in electrocardiograms transmitted by implantable cardiac devices

hal.structure.identifierInstitut de Mathématiques de Bordeaux [IMB]
hal.structure.identifierModélisation et calculs pour l'électrophysiologie cardiaque [CARMEN]
hal.structure.identifierInstitut de rythmologie et modélisation cardiaque [Pessac] [IHU Liryc]
dc.contributor.authorGASSA, Narimane
hal.structure.identifierInstitut de rythmologie et modélisation cardiaque [Pessac] [IHU Liryc]
dc.contributor.authorSACRISTAN, Benjamin
hal.structure.identifierModélisation et calculs pour l'électrophysiologie cardiaque [CARMEN]
hal.structure.identifierInstitut de Mathématiques de Bordeaux [IMB]
dc.contributor.authorZEMZEMI, Nejib
hal.structure.identifierDepartment of Mathematics and Statistics [Montréal]
dc.contributor.authorLABORDE, Maxime
hal.structure.identifierUniversitat Pompeu Fabra [Barcelona] [UPF]
dc.contributor.authorOLIVER, Juan
hal.structure.identifierUniversitat Pompeu Fabra [Barcelona] [UPF]
dc.contributor.authorPERABLA, Clara
hal.structure.identifierUniversitat Pompeu Fabra [Barcelona] [UPF]
dc.contributor.authorJIMENEZ-PEREZ, Guillermo
hal.structure.identifierUniversitat Pompeu Fabra [Barcelona] [UPF]
dc.contributor.authorCAMARA, Oscar
hal.structure.identifierCentre de recherche Cardio-Thoracique de Bordeaux [Bordeaux] [CRCTB]
dc.contributor.authorPLOUX, Sylvain
hal.structure.identifierMaastricht University Medical Centre [MUMC]
dc.contributor.authorSTRIK, Marc
hal.structure.identifierInstitut de rythmologie et modélisation cardiaque [Pessac] [IHU Liryc]
dc.contributor.authorBORDACHAR, Pierre
hal.structure.identifierIHU-LIRYC
dc.contributor.authorDUBOIS, Remi
dc.date.accessioned2024-04-04T02:43:21Z
dc.date.available2024-04-04T02:43:21Z
dc.date.conference2021-09-12
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/191326
dc.description.abstractEnThe objective of this work was to benchmark different deep learning architectures for noise detection against cardiac arrhythmia episodes recorded by pacemakers and implantable cardioverter-defibrillators (PM/ICDs) and transmitted for remote monitoring. Up to now, most signal processing from ICD data has been based on classical hand-crafted algorithms, not AI or DL-based ones. The database consist of PM/ICD data from 805 patients representing a total of 10471 recordings from three different channels: the right ventricular (RV), the right atria (RA), and the shock channel. Four deep learning approaches were trained and optimized to classify PM/ICDs' records as actual ventricular signal vs noise episodes. We evaluated the performance of the different models using the F 2 score. Results show that the use of 2D representations of 1D signals led to better performances than the direct use of 1D signals, suggesting that the detection of noise takes advantage of a spectral decomposition of the signal, which remains to be confirmed in other contexts. This study proposes deep learning approaches for the analysis of remote monitoring recordings from PM/ICDs. The detection of noise allows efficient management of this large daily flow of data.
dc.language.isoen
dc.title.enBenchmark of deep learning algorithms for the automatic screening in electrocardiograms transmitted by implantable cardiac devices
dc.typeCommunication dans un congrès
dc.subject.halStatistiques [stat]/Machine Learning [stat.ML]
dc.subject.halInformatique [cs]/Traitement du signal et de l'image
bordeaux.hal.laboratoriesInstitut de Mathématiques de Bordeaux (IMB) - UMR 5251*
bordeaux.institutionUniversité de Bordeaux
bordeaux.institutionBordeaux INP
bordeaux.institutionCNRS
bordeaux.conference.titleCINC 2021 - Computing In Cardiology
bordeaux.countryCZ
bordeaux.conference.cityBrno
bordeaux.peerReviewedoui
hal.identifierhal-03485146
hal.version1
hal.invitednon
hal.proceedingsoui
hal.conference.end2021-09-15
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-03485146v1
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.au=GASSA,%20Narimane&SACRISTAN,%20Benjamin&ZEMZEMI,%20Nejib&LABORDE,%20Maxime&OLIVER,%20Juan&rft.genre=unknown


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