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dc.contributor.authorAlfaras, Miqueles_ES
dc.contributor.authorSoriano, Miguel C.es_ES
dc.contributor.authorOrtín González, Silviaes_ES
dc.date.accessioned2019-08-29T10:39:51Z-
dc.date.available2019-08-29T10:39:51Z-
dc.date.issued2019-07-18-
dc.identifier.citationFrontiers in Physics 7: 103 (2019)es_ES
dc.identifier.urihttp://hdl.handle.net/10261/189424-
dc.description.abstractWe present a fully automatic and fast ECG arrhythmia classifier based on a simple brain-inspired machine learning approach known as Echo State Networks. Our classifier has a low-demanding feature processing that only requires a single ECG lead. Its training and validation follows an inter-patient procedure. Our approach is compatible with an online classification that aligns well with recent advances in health-monitoring wireless devices and wearables. The use of a combination of ensembles allows us to exploit parallelism to train the classifier with remarkable speeds. The heartbeat classifier is evaluated over two ECG databases, the MIT-BIH AR and the AHA. In the MIT-BIH AR database, our classification approach provides a sensitivity of 92.7% and positive predictive value of 86.1% for the ventricular ectopic beats, using the single lead II, and a sensitivity of 95.7% and positive predictive value of 75.1% when using the lead V1'. These results are comparable with the state of the art in fully automatic ECG classifiers and even outperform other ECG classifiers that follow more complex feature-selection approaches.es_ES
dc.description.sponsorshipThis work was partially funded by the Spanish Ministerio de Economía y Competitividad (MINECO) and Fondo Europeo de Desarrollo Regional (FEDER) and the European Social Fund through project TEC2016-80063-C3-3-R (MINECO/AEI/FEDER/UE). MA was supported by the Beca de colaboración 012/2016 UIB fellowship on Information processing in neural and photonic systems. MS was supported by the Spanish Ministerio de Economía, Industria y Competitividad through a Ramón y Cajal Fellowship (RYC-2015-18140). SO was supported by the Conselleria d'Innovació, Recerca i Turisme del Govern de les Illes Balears and the European Social Fund.es_ES
dc.language.isoenges_ES
dc.publisherFrontiers Mediaes_ES
dc.relationinfo:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/TEC2016-80063-C3-3-Res_ES
dc.relationinfo:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/RYC-2015-18140es_ES
dc.relation.isversionofPublisher's versiones_ES
dc.rightsopenAccesses_ES
dc.subjectEcho State Networkses_ES
dc.subjectReservoir computinges_ES
dc.subjectArrhythmia classificationes_ES
dc.subjectGPUes_ES
dc.subjectECGes_ES
dc.titleA Fast Machine Learning Model for ECG-Based Heartbeat Classification and Arrhythmia Detectiones_ES
dc.typeartículoes_ES
dc.identifier.doi10.3389/fphy.2019.00103-
dc.description.peerreviewedPeer reviewedes_ES
dc.relation.publisherversionhttps://doi.org/10.3389/fphy.2019.00103es_ES
dc.identifier.e-issn2296-424X-
dc.rights.licensehttp://creativecommons.org/licenses/by/4.0/es_ES
dc.contributor.funderMinisterio de Economía y Competitividad (España)es_ES
dc.contributor.funderEuropean Commissiones_ES
dc.contributor.funderUniversidad de Las Islas Baleareses_ES
dc.contributor.funderGovern de les Illes Balearses_ES
dc.relation.csices_ES
oprm.item.hasRevisionno ko 0 false*
dc.identifier.funderhttp://dx.doi.org/10.13039/501100008975es_ES
dc.identifier.funderhttp://dx.doi.org/10.13039/501100000780es_ES
dc.identifier.funderhttp://dx.doi.org/10.13039/501100003329es_ES
dc.type.coarhttp://purl.org/coar/resource_type/c_6501es_ES
item.openairetypeartículo-
item.cerifentitytypePublications-
item.languageiso639-1en-
item.grantfulltextopen-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
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