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Main Title: Online learning algorithms for principal component analysis applied on single-lead ECGs
Author(s): Pflugradt, Maik
Mann, Steffen
Feller, Viktor
Lu, Yirong
Orglmeister, Reinhold
Type: Article
Language Code: en
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.
Issue Date: 2013
Date Available: 28-Nov-2017
DDC Class: 610 Medizin und Gesundheit
Subject(s): blind source separation
body sensor network
ECG processing
home monitoring
movement artifacts
neural netwoks
online PCA
single-channel ICA
Journal Title: Biomedical engineering = Biomedizinische Technik
Publisher: De Gruyter
Publisher Place: Berlin [u.a.]
Volume: 58
Issue: 2
Publisher DOI: 10.1515/bmt-2012-0026
Page Start: 121
Page End: 130
EISSN: 0013-5585
ISSN: 1862-278X
Notes: Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG geförderten) Allianz- bzw. Nationallizenz frei zugänglich.
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.
Appears in Collections:FG Elektronik und medizinische Signalverarbeitung » Publications

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