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Application of kernel principal component analysis for single-lead-ECG-derived respiration

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dc.creatorWidjaja, Devyspa
dc.creatorVaron, Carolinaspa
dc.creatorDorado, Alexanderspa
dc.creatorSuykens, Johan A. K.spa
dc.creatorVan Huffel, Sabinespa
dc.date.accessioned2020-08-19T14:41:24Z
dc.date.available2020-08-19T14:41:24Z
dc.date.created2012-02-03spa
dc.description.abstractRecent studies show that principal component analysis (PCA) of heartbeats is a well-performing method to derive a respiratory signal from ECGs. In this study, an improved ECG-derived respiration (EDR) algorithm based on kernel PCA (kPCA) is presented. KPCA can be seen as a generalization of PCA where nonlinearities in the data are taken into account by nonlinear mapping of the data, using a kernel function, into a higher dimensional space in which PCA is carried out. The comparison of several kernels suggests that a radial basis function (RBF) kernel performs the best when deriving EDR signals. Further improvement is carried out by tuning the parameter that represents the variance of the RBF kernel. The performance of kPCA is assessed by comparing the EDR signals to a reference respiratory signal, using the correlation and the magnitude squared coherence coefficients. When comparing the coefficients of the tuned EDR signals using kPCA to EDR signals obtained using PCA and the algorithm based on the R peak amplitude, statistically significant differences are found in the correlation and coherence coefficients (both ), showing that kPCA outperforms PCA and R peak amplitude in the extraction of a respiratory signal from single-lead ECGs.eng
dc.format.mimetypeapplication/pdf
dc.identifier.doihttps://doi.org/10.1109/TBME.2012.2186448
dc.identifier.issnISSN: 0018-9294
dc.identifier.issnEISSN: 1558-2531
dc.identifier.urihttps://repository.urosario.edu.co/handle/10336/27226
dc.language.isoengspa
dc.publisherIEEEspa
dc.relation.citationEndPage1176
dc.relation.citationIssueNo. 4
dc.relation.citationStartPage1169
dc.relation.citationTitleIEEE Transactions on Biomedical Engineering
dc.relation.citationVolumeVol. 59
dc.relation.ispartofIEEE Transactions on Biomedical Engineering, ISSN: 0018-9294;EISSN: 1558-2531, Vol.59, No.4 (April 2012); pp. 1169-1176spa
dc.relation.urihttps://ieeexplore.ieee.org/document/6144719spa
dc.rights.accesRightsinfo:eu-repo/semantics/restrictedAccess
dc.rights.accesoRestringido (Acceso a grupos específicos)spa
dc.sourceIEEE Transactions on Biomedical Engineeringspa
dc.source.instnameinstname:Universidad del Rosario
dc.source.reponamereponame:Repositorio Institucional EdocUR
dc.subject.keywordKernespa
dc.subject.keywordPrincipal component analysisspa
dc.subject.keywordElectrocardiographyspa
dc.subject.keywordEigenvalues and eigenfunctionsspa
dc.subject.keywordCoherencespa
dc.subject.keywordCorrelationspa
dc.subject.keywordEntropyspa
dc.titleApplication of kernel principal component analysis for single-lead-ECG-derived respirationspa
dc.title.TranslatedTitleAplicación del análisis de componentes principales del núcleo para la respiración derivada de ECG de derivación únicaspa
dc.typearticleeng
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersion
dc.type.spaArtículospa
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