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ECG Analysis Using Consensus Clustering

dc.contributor.authorLourenço, André Ribeiro
dc.contributor.authorCarreiras, Carlos
dc.contributor.authorBulò, Samuel Rocha
dc.contributor.authorFred, Ana Luísa Nobre
dc.date.accessioned2015-08-18T10:23:39Z
dc.date.available2015-08-18T10:23:39Z
dc.date.issued2014
dc.description.abstractBiosignals analysis has become widespread, upstaging their typical use in clinical settings. Electrocardiography (ECG) plays a central role in patient monitoring as a diagnosis tool in today's medicine and as an emerging biometric trait. In this paper we adopt a consensus clustering approach for the unsupervised analysis of an ECG-based biometric records. This type of analysis highlights natural groups within the population under investigation, which can be correlated with ground truth information in order to gain more insights about the data. Preliminary results are promising, for meaningful clusters are extracted from the population under analysis. © 2014 EURASIP.por
dc.identifier.citationLOURENÇO, André Ribeiro; [et al] – ECG analysis using consensus clustering. In European Signal Processing Conference. EUSIPCO - European Signal Processing Conference, 2014. ISSN: 2219-5491. Art. nr. 6952141, p. 511-515.
dc.identifier.isbn978-099286261-9
dc.identifier.issn2219-5491
dc.identifier.urihttp://hdl.handle.net/10400.21/4796
dc.language.isoengpor
dc.peerreviewedyespor
dc.publisherEuropean Signal Processing Conference, EUSIPCOpor
dc.relation.ispartofseries6952141
dc.subjectConsensus Clusteringpor
dc.subjectECG Analysispor
dc.subjectECG-Based Biometricspor
dc.subjectEvidence Accumulationpor
dc.titleECG Analysis Using Consensus Clusteringpor
dc.typeconference object
dspace.entity.typePublication
oaire.citation.endPage515por
oaire.citation.startPage511por
oaire.citation.titleEuropean Signal Processing Conferencepor
rcaap.rightsclosedAccesspor
rcaap.typeconferenceObjectpor

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