Pflugradt, MaikMann, SteffenFeller, ViktorOrglmeister, Reinhold2017-11-282017-11-2820121862-278Xhttps://depositonce.tu-berlin.de/handle/11303/7200http://dx.doi.org/10.14279/depositonce-6475Dieser 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.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.en610 Medizin und GesundheitArticle0013-5585