Publication:
Hidden Markov Models for Activity Detection in Atrial Fibrillation Electrograms

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dc.affiliation.dptoUC3M. Departamento de Teoría de la Señal y Comunicacioneses
dc.affiliation.grupoinvUC3M. Grupo de Investigación: Tratamiento de la Señal y Aprendizaje (GTSA)es
dc.contributor.authorRíos Muñoz, Gonzalo Ricardo
dc.contributor.authorMoreno Pino, Fernando
dc.contributor.authorSoto, Nina
dc.contributor.authorMartínez Olmos, Pablo
dc.contributor.authorArtés Rodríguez, Antonio
dc.contributor.authorFernández Avilés, Francisco
dc.contributor.authorArenal, Ángel
dc.contributor.funderComunidad de Madrides
dc.contributor.funderMinisterio de Ciencia, Innovación y Universidades (España)es
dc.date.accessioned2022-05-18T09:49:30Z
dc.date.available2022-05-18T09:49:30Z
dc.date.issued2020-09-13
dc.descriptionProceeding of 2020 Computing in Cardiology (CinC 2020), 13-16 September 2020, Rimini, Italyen
dc.description.abstractActivity detection in atrial fibrillation (AF) electrograms (EGMs) is a key concept to understand the mechanisms of this frequent arrhythmia and design new strategies for its treatment. We present a new method that employs Hidden Markov Models (HMMs) to identify activity presence in bipolar EGMs. The method is fully unsupervised and hence it does not require labeled training data. The HMM activity detection method was validated and compared to the non-linear energy operator (NLEO) method for a set of manually annotated EGMs. The HMM performed better than the NLEO and exhibited more robustness in the presence of low voltage fragmented EGMs.en
dc.description.sponsorshipThis study was supported by grants PI18/01895 from the Instituto de Salud Carlos III, and RD16/0011/0029 Red de Terapia Celular from the Instituto de Salud Carlos III, the projects RTI2018-099655-B-I00; TEC2017-92552-EXP; PID2019-108539RB-C22, Y2018/TCS-4705, and the support of NVIDIA Corporation with the donation of the Titan V GPU used during this research.en
dc.description.statusPublicadoes
dc.format.extent4
dc.identifier.bibliographicCitationComputing in Cardiology (CinC 2020), 13-16 September 2020, Rimini, Italy. V. 47. IEEE, 2020.en
dc.identifier.doihttps://doi.org/10.22489/CinC.2020.098
dc.identifier.isbn978-1-7281-7382-5
dc.identifier.issn2325-887X
dc.identifier.publicationfirstpage1
dc.identifier.publicationlastpage4
dc.identifier.publicationtitleComputing in Cardiology (CinC 2020), 13-16 September 2020, Rimini, Italyen
dc.identifier.publicationvolume47
dc.identifier.urihttps://hdl.handle.net/10016/34837
dc.identifier.uxxiCC/0000033345
dc.language.isoengen
dc.publisherIEEEen
dc.relation.eventdate13-16 September 2020en
dc.relation.eventplaceRimini, Italia
dc.relation.eventtitle2020 Computing in Cardiology Conference (CinC 2020)es
dc.relation.projectIDGobierno de España. RTI2018-099655-B-I00es
dc.relation.projectIDGobierno de España. TEC2017-92552-EXPes
dc.relation.projectIDGobierno de España. PID2019-108539RB-C22es
dc.relation.projectIDComunidad de Madrid. Y2018/TCS-4705es
dc.rights© 2020, IEEEen
dc.rights.accessRightsopen accessen
dc.subject.ecienciaBiología y Biomedicinaes
dc.subject.ecienciaTelecomunicacioneses
dc.subject.otherTrainingen
dc.subject.otherLow voltageen
dc.subject.otherNonlinear distortionen
dc.subject.otherHidden Markov modelsen
dc.subject.otherAtrial fibrillationen
dc.subject.otherTraining dataen
dc.subject.otherRobustnessen
dc.titleHidden Markov Models for Activity Detection in Atrial Fibrillation Electrogramsen
dc.typeconference paper*
dc.type.hasVersionAM*
dspace.entity.typePublication
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