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Model based on support vector machine for the estimation of the heart rate variability

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dc.creatorHernández-Ruiz C.M.spa
dc.creatorVillagrán Martínez S.A.spa
dc.creatorOrtiz Guzmán J.E.spa
dc.creatorGaona Garcia P.A.spa
dc.date.accessioned2020-05-25T23:56:48Z
dc.date.available2020-05-25T23:56:48Z
dc.date.created2018spa
dc.description.abstractThis paper shows the design, implementation and analysis of a Machine Learning (ML) model for the estimation of Heart Rate Variability (HRV). Through the integration of devices and technologies of the Internet of Things, a support tool is proposed for people in health and sports areas who need to know an individual’s HRV. The cardiac signals of the subjects were captured through pectoral bands, later they were classified by a Support Vector Machine algorithm that determined if the HRV is depressed or increased. The proposed solution has an efficiency of 90.3% and it’s the initial component for the development of an application oriented to physical training that suggests exercise routines based on the HRV of the individual. © Springer Nature Switzerland AG 2018.eng
dc.format.mimetypeapplication/pdf
dc.identifier.doihttps://doi.org/10.1007/978-3-030-01421-6_19
dc.identifier.urihttps://repository.urosario.edu.co/handle/10336/22528
dc.language.isoengspa
dc.publisherSpringer Verlagspa
dc.relation.citationEndPage194
dc.relation.citationStartPage186
dc.relation.citationTitleLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.relation.citationVolumeVol. 11140 LNCS
dc.relation.ispartofLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol.11140 LNCS,(2018); pp. 186-194spa
dc.relation.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85054845276&doi=10.1007%2f978-3-030-01421-6_19&partnerID=40&md5=f2eb5b6d02d1016fe4378bb762db85a0spa
dc.rights.accesRightsinfo:eu-repo/semantics/openAccess
dc.rights.accesoAbierto (Texto Completo)spa
dc.source.instnameinstname:Universidad del Rosariospa
dc.source.reponamereponame:Repositorio Institucional EdocURspa
dc.subject.keywordInternet of thingsspa
dc.subject.keywordNeural networksspa
dc.subject.keywordPatient monitoringspa
dc.subject.keywordSupport vector machinesspa
dc.subject.keywordApplication-orientedspa
dc.subject.keywordCardiac signalsspa
dc.subject.keywordHeart rate variabilityspa
dc.subject.keywordHeart-rate monitorsspa
dc.subject.keywordInternet of Things (IOT)spa
dc.subject.keywordModel-based OPCspa
dc.subject.keywordPhysical trainingspa
dc.subject.keywordSupport vector machine algorithmspa
dc.subject.keywordHeartspa
dc.subject.keywordHeart Rate Monitor (HRM)spa
dc.subject.keywordHeart rate variability (HRV)spa
dc.subject.keywordInternet of things (IOT)spa
dc.subject.keywordSupport vector machine (SVM)spa
dc.titleModel based on support vector machine for the estimation of the heart rate variabilityspa
dc.typeconferenceObjecteng
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersion
dc.type.spaDocumento de conferenciaspa
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