Online learning algorithms for principal component analysis applied on single-lead ECGs
| dc.contributor.author | Pflugradt, Maik | |
| dc.contributor.author | Mann, Steffen | |
| dc.contributor.author | Feller, Viktor | |
| dc.contributor.author | Lu, Yirong | |
| dc.contributor.author | Orglmeister, Reinhold | |
| dc.date.accessioned | 2017-11-28T08:49:23Z | |
| dc.date.available | 2017-11-28T08:49:23Z | |
| dc.date.issued | 2013 | |
| dc.description | Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG geförderten) Allianz- bzw. Nationallizenz frei zugänglich. | de |
| dc.description | This 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.abstract | This 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.eissn | 0013-5585 | |
| dc.identifier.issn | 1862-278X | |
| dc.identifier.uri | https://depositonce.tu-berlin.de/handle/11303/7195 | |
| dc.identifier.uri | http://dx.doi.org/10.14279/depositonce-6470 | |
| dc.language.iso | en | |
| dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | |
| dc.subject.ddc | 610 Medizin und Gesundheit | |
| dc.subject.other | blind source separation | en |
| dc.subject.other | body sensor network | en |
| dc.subject.other | ECG processing | en |
| dc.subject.other | home monitoring | en |
| dc.subject.other | movement artifacts | en |
| dc.subject.other | neural netwoks | en |
| dc.subject.other | online PCA | en |
| dc.subject.other | single-channel ICA | en |
| dc.subject.other | SCICA | en |
| dc.title | Online learning algorithms for principal component analysis applied on single-lead ECGs | en |
| dc.type | Article | |
| dc.type.version | publishedVersion | |
| dcterms.bibliographicCitation.doi | 10.1515/bmt-2012-0026 | |
| dcterms.bibliographicCitation.issue | 2 | |
| dcterms.bibliographicCitation.journaltitle | Biomedical engineering = Biomedizinische Technik | |
| dcterms.bibliographicCitation.originalpublishername | De Gruyter | |
| dcterms.bibliographicCitation.originalpublisherplace | Berlin [u.a.] | |
| dcterms.bibliographicCitation.pageend | 130 | |
| dcterms.bibliographicCitation.pagestart | 121 | |
| dcterms.bibliographicCitation.volume | 58 | |
| tub.accessrights.dnb | domain | |
| tub.affiliation | Fak. 4 Elektrotechnik und Informatik::Inst. Energie- und Automatisierungstechnik::FG Elektronik und medizinische Signalverarbeitung | de |
| tub.affiliation.faculty | Fak. 4 Elektrotechnik und Informatik | de |
| tub.affiliation.group | FG Elektronik und medizinische Signalverarbeitung | de |
| tub.affiliation.institute | Inst. Energie- und Automatisierungstechnik | de |
| tub.publisher.universityorinstitution | Technische Universität Berlin |
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