On-line learning algorithms for extracting respiratory activity from single lead ECGs based on principal component analysis

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dc.contributor.authorPflugradt, Maik
dc.contributor.authorMann, Steffen
dc.contributor.authorFeller, Viktor
dc.contributor.authorOrglmeister, Reinhold
dc.date.accessioned2017-11-28T08:49:35Z
dc.date.available2017-11-28T08:49:35Z
dc.date.issued2012
dc.descriptionDieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG geförderten) Allianz- bzw. Nationallizenz frei zugänglich.de
dc.descriptionThis publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.en
dc.description.abstractIn this paper we present several statistic gradient algorithms from literature to solve the Principal Component Analysis (PCA) problem. We used a linear artificial neural network forming the basis of the implemented algorithms which is a neat way for on-line computation of the PCA expansion. As convergence is a key-aspect of these algorithms and is cru-cial for the usefulness in particular applications, we compared the different learning rules with respect to their suitability in ECG signal processing. Recent studies have shown, that a surrogate respiratory signal can be derived from single-lead ECGs by applying PCA. Since the traditionally applied closed-form computations of PCA are numerically demanding, it seems promising to resort to an adaptive approach when dealing with changing environments like the ECG.en
dc.identifier.eissn0013-5585
dc.identifier.issn1862-278X
dc.identifier.urihttps://depositonce.tu-berlin.de/handle/11303/7200
dc.identifier.urihttp://dx.doi.org/10.14279/depositonce-6475
dc.language.isoen
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/
dc.subject.ddc610 Medizin und Gesundheit
dc.titleOn-line learning algorithms for extracting respiratory activity from single lead ECGs based on principal component analysisen
dc.typeArticle
dc.type.versionpublishedVersion
dcterms.bibliographicCitation.doi10.1515/bmt-2012-4149
dcterms.bibliographicCitation.issuesuppl. 1
dcterms.bibliographicCitation.journaltitleBiomedical engineering = Biomedizinische Technik
dcterms.bibliographicCitation.originalpublishernameDe Gruyter
dcterms.bibliographicCitation.originalpublisherplaceBerlin [u.a.]
dcterms.bibliographicCitation.pageend354
dcterms.bibliographicCitation.pagestart352
dcterms.bibliographicCitation.volume57
tub.accessrights.dnbdomain
tub.affiliationFak. 4 Elektrotechnik und Informatik::Inst. Energie- und Automatisierungstechnik::FG Elektronik und medizinische Signalverarbeitungde
tub.affiliation.facultyFak. 4 Elektrotechnik und Informatikde
tub.affiliation.groupFG Elektronik und medizinische Signalverarbeitungde
tub.affiliation.instituteInst. Energie- und Automatisierungstechnikde
tub.publisher.universityorinstitutionTechnische Universität Berlin
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