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Artificial Intelligence for Detection of Ventricular Oversensing Machine Learning Approaches for Noise Detection Within Non-Sustained Ventricular Tachycardia Episodes Remotely Transmitted by Pacemakers and Implantable Cardioverter Defibrillators

hal.structure.identifierCentre Hospitalier Universitaire de Bordeaux [CHU de Bordeaux]
hal.structure.identifierIHU-LIRYC
dc.contributor.authorSTRIK, Marc
hal.structure.identifierCentre Hospitalier Universitaire de Bordeaux [CHU de Bordeaux]
hal.structure.identifierIHU-LIRYC
dc.contributor.authorSACRISTAN, Benjamin
hal.structure.identifierCentre Hospitalier Universitaire de Bordeaux [CHU de Bordeaux]
hal.structure.identifierIHU-LIRYC
dc.contributor.authorBORDACHAR, Pierre
hal.structure.identifierCentre Hospitalier Universitaire de Bordeaux [CHU de Bordeaux]
hal.structure.identifierIHU-LIRYC
dc.contributor.authorDUCHATEAU, Josselin
hal.structure.identifierCHU Clermont-Ferrand
dc.contributor.authorESCHALIER, Romain
hal.structure.identifierCentre Hospitalier Universitaire de Toulouse [CHU Toulouse]
dc.contributor.authorMONDOLY, Pierre
hal.structure.identifierService de cardiologie [Centre Hospitalier de la Côte Basque, Bayonne]
dc.contributor.authorLABORDERIE, Julien
hal.structure.identifierModélisation et calculs pour l'électrophysiologie cardiaque [CARMEN]
hal.structure.identifierIHU-LIRYC
dc.contributor.authorGASSA, Narimane
hal.structure.identifierModélisation et calculs pour l'électrophysiologie cardiaque [CARMEN]
hal.structure.identifierIHU-LIRYC
dc.contributor.authorZEMZEMI, Nejib
hal.structure.identifierIHU-LIRYC
dc.contributor.authorLABORDE, Maxime
hal.structure.identifierUniversitat Pompeu Fabra [Barcelona] [UPF]
dc.contributor.authorGARRIDO, Juan
hal.structure.identifierUniversitat Pompeu Fabra [Barcelona] [UPF]
dc.contributor.authorPERABLA, Clara Matencio
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 Hospitalier Universitaire de Bordeaux [CHU de Bordeaux]
hal.structure.identifierIHU-LIRYC
dc.contributor.authorHAÏSSAGUERRE, Michel
hal.structure.identifierIHU-LIRYC
dc.contributor.authorDUBOIS, Rémi
hal.structure.identifierCentre Hospitalier Universitaire de Bordeaux [CHU de Bordeaux]
hal.structure.identifierIHU-LIRYC
dc.contributor.authorPLOUX, Sylvain
dc.date2023-07
dc.date.accessioned2024-04-04T02:33:41Z
dc.date.available2024-04-04T02:33:41Z
dc.date.issued2023-07
dc.identifier.issn1556-3871
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/190501
dc.description.abstractEnBackground:Pacemakers (PMs) and implantable cardioverter defibrillators (ICDs) increasingly automatically record and remotely transmit non-sustained ventricular tachycardia (NSVT) episodes which may reveal ventricular oversensing.Objectives:We aimed to develop and validate a machine learning algorithm which accurately classifies NSVT episodes transmitted by PMs and ICDs in order to lighten healthcare workload burden and improve patient safety.Methods:PMs or ICDs (Boston Scientific) from four French hospitals with ≥1 transmitted NSVT episode were split into three subgroups: training set, validation set, and test set. Each NSVT episode was labelled as either physiological or non-physiological. Four machine learning algorithms (2DTF-CNN, 2D-DenseNet, 2DTF-VGG, and 1D-AgResNet) were developed using a training and validation dataset. Accuracies of the classifiers were compared with an analysis of the remote monitoring team of the Bordeaux University Hospital using F2 scores (favoring sensitivity over predictive positive value) using an independent test set.Results:807 devices transmitted 10.471 NSVT recordings (82% ICD, 18% PM), of which 87 devices (10.8%) transmitted 544 NSVT recordings with non-physiological signals. The classification by the remote monitoring team resulted in an F2 score of 0,932 (sensitivity of 95%, specificity of 99%) The four machine learning algorithms showed high and comparable F2 scores (2DTF-CNN: 0,914, 2D-DenseNet: 0,906, 2DTF-VGG: 0,863, 1D-AgResNet: 0,791) and only 1D-AgResNet had significantly different labeling as compared with the remote monitoring team.Conclusion:Machine learning algorithms were accurate in detecting non-physiological signals within EGMs transmitted by pacemaker and ICDs. An artificial intelligence approach may render remote monitoring less resourceful and improve patient safety.
dc.description.sponsorshipL'Institut de Rythmologie et modélisation Cardiaque
dc.language.isoen
dc.publisherElsevier
dc.rights.urihttp://creativecommons.org/licenses/by/
dc.title.enArtificial Intelligence for Detection of Ventricular Oversensing Machine Learning Approaches for Noise Detection Within Non-Sustained Ventricular Tachycardia Episodes Remotely Transmitted by Pacemakers and Implantable Cardioverter Defibrillators
dc.typeArticle de revue
dc.typeArticle de synthèse
dc.identifier.doi10.1016/j.hrthm.2023.06.019
dc.subject.halStatistiques [stat]/Machine Learning [stat.ML]
dc.subject.halSciences du Vivant [q-bio]/Ingénierie biomédicale
dc.subject.halSciences du Vivant [q-bio]/Médecine humaine et pathologie/Cardiologie et système cardiovasculaire
dc.subject.halSciences de l'ingénieur [physics]/Traitement du signal et de l'image
dc.description.sponsorshipEuropePersonalized Therapies for Atrial Fibrillation. A Translational Approach
bordeaux.journalHeart Rhythm
bordeaux.hal.laboratoriesInstitut de Mathématiques de Bordeaux (IMB) - UMR 5251*
bordeaux.institutionUniversité de Bordeaux
bordeaux.institutionBordeaux INP
bordeaux.institutionCNRS
bordeaux.peerReviewedoui
hal.identifierhal-04155080
hal.version1
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-04155080v1
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Heart%20Rhythm&rft.date=2023-07&rft.eissn=1556-3871&rft.issn=1556-3871&rft.au=STRIK,%20Marc&SACRISTAN,%20Benjamin&BORDACHAR,%20Pierre&DUCHATEAU,%20Josselin&ESCHALIER,%20Romain&rft.genre=article&unknown


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