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

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dc.contributor.author Gassa, Narimane
dc.contributor.author Sacristan, Benjamin
dc.contributor.author Zemzemi, Nejib
dc.contributor.author Laborde, Maxime
dc.contributor.author Garrido Oliver, Juan
dc.contributor.author Matencio Perabla, Clara
dc.contributor.author Jimenez-Perez, Guillermo
dc.contributor.author Camara, Oscar
dc.contributor.author Ploux, Sylvain
dc.contributor.author Strik, Marc
dc.contributor.author Bordachar, Pierre
dc.contributor.author Dubois, Remi
dc.date.accessioned 2023-03-09T07:26:52Z
dc.date.available 2023-03-09T07:26:52Z
dc.date.issued 2021
dc.identifier.citation Gassa N, Sacristan B, Zemzemi N, Laborde M, Garrido Oliver J, Matencio Perabla C, Jimenez-Perez G, Camara O, Ploux S, Strik M, Bordachar P, Dubois R. Benchmark of deep learning algorithms for the automatic screening in electrocardiograms transmitted by implantable cardiac devices. In: 2021 Computing in Cardiology (CinC); 2021 Sep 13-15; Brno, Czech Republic. [Piscataway]: IEEE; 2021. 4 p. DOI: 10.23919/CinC53138.2021.9662651
dc.identifier.issn 2325-8861
dc.identifier.uri http://hdl.handle.net/10230/56127
dc.description Comunicació presentada a 2021 Computing in Cardiology (CinC), celebrat del 13 al 15 de setembre de 2021 a Brno, Txèquia.
dc.description.abstract The 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 F2 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.description.sponsorship This Project has received funding from the European Unions Horizon research and innovation programme under the Marie Skodowska-Curie grant agreement No. 860974 and by the French National Research Agency, grant references ANR- 10-IAHU04- LIRYC and ANR-11-EQPX0030.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof 2021 Computing in Cardiology (CinC); 2021 Sep 13-15; Brno, Czech Republic. [Piscataway]: IEEE; 2021. 4 p.
dc.rights © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. http://dx.doi.org/10.23919/CinC53138.2021.9662651
dc.title Benchmark of deep learning algorithms for the automatic screening in electrocardiograms transmitted by implantable cardiac devices
dc.type info:eu-repo/semantics/conferenceObject
dc.identifier.doi http://dx.doi.org/10.23919/CinC53138.2021.9662651
dc.subject.keyword Deep learning
dc.subject.keyword Databases
dc.subject.keyword Electric shock
dc.subject.keyword Signal processing algorithms
dc.subject.keyword Pacemakers
dc.subject.keyword Computer architecture
dc.subject.keyword Benchmark testing
dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/860974
dc.rights.accessRights info:eu-repo/semantics/openAccess
dc.type.version info:eu-repo/semantics/acceptedVersion

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