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Main Title: On-line learning algorithms for extracting respiratory activity from single lead ECGs based on principal component analysis
Author(s): Pflugradt, Maik
Mann, Steffen
Feller, Viktor
Orglmeister, Reinhold
Type: Article
Language Code: en
Abstract: In 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.
Issue Date: 2012
Date Available: 28-Nov-2017
DDC Class: 610 Medizin und Gesundheit
Journal Title: Biomedical engineering = Biomedizinische Technik
Publisher: De Gruyter
Publisher Place: Berlin [u.a.]
Volume: 57
Issue: suppl. 1
Publisher DOI: 10.1515/bmt-2012-4149
Page Start: 352
Page End: 354
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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