Online learning algorithms for principal component analysis applied on single-lead ECGs

Simple item page
dc.contributor.authorPflugradt, Maik
dc.contributor.authorMann, Steffen
dc.contributor.authorFeller, Viktor
dc.contributor.authorLu, Yirong
dc.contributor.authorOrglmeister, Reinhold
dc.date.accessioned2017-11-28T08:49:23Z
dc.date.available2017-11-28T08:49:23Z
dc.date.issued2013
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.abstractThis article evaluates several adaptive approaches to solve the principal component analysis (PCA) problem applied on single-lead ECGs. Recent studies have shown that the principal components can indicate morphologically or environmentally induced changes in the ECG signal and can be used to extract other vital information such as respiratory activity. Special interest is focused on the convergence behavior of the selected gradient algorithms, which is a major criterion for the usability of the gained results. As the right choice of learning rates is very data dependant and subject to movement artifacts, a new measurement system was designed, which uses acceleration data to improve the performance of the online algorithms. As the results of PCA seem very promising, we propose to apply a single-channel independent component analysis (SCICA) as a second step, which is investigated in this paper as well.en
dc.identifier.eissn0013-5585
dc.identifier.issn1862-278X
dc.identifier.urihttps://depositonce.tu-berlin.de/handle/11303/7195
dc.identifier.urihttp://dx.doi.org/10.14279/depositonce-6470
dc.language.isoen
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/
dc.subject.ddc610 Medizin und Gesundheit
dc.subject.otherblind source separationen
dc.subject.otherbody sensor networken
dc.subject.otherECG processingen
dc.subject.otherhome monitoringen
dc.subject.othermovement artifactsen
dc.subject.otherneural netwoksen
dc.subject.otheronline PCAen
dc.subject.othersingle-channel ICAen
dc.subject.otherSCICAen
dc.titleOnline learning algorithms for principal component analysis applied on single-lead ECGsen
dc.typeArticle
dc.type.versionpublishedVersion
dcterms.bibliographicCitation.doi10.1515/bmt-2012-0026
dcterms.bibliographicCitation.issue2
dcterms.bibliographicCitation.journaltitleBiomedical engineering = Biomedizinische Technik
dcterms.bibliographicCitation.originalpublishernameDe Gruyter
dcterms.bibliographicCitation.originalpublisherplaceBerlin [u.a.]
dcterms.bibliographicCitation.pageend130
dcterms.bibliographicCitation.pagestart121
dcterms.bibliographicCitation.volume58
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
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
bmt-2012-0026.pdf
Size:
1.49 MB
Format:
Adobe Portable Document Format
Description:
Download
Collections
Publications