%0 Book Section %1 statphys23_0641 %A Schumann, A.Y. %A Kantelhardt, J.W. %A Bauer, A. %A Schmidt, G. %B Abstract Book of the XXIII IUPAP International Conference on Statistical Physics %C Genova, Italy %D 2007 %E Pietronero, Luciano %E Loreto, Vittorio %E Zapperi, Stefano %K analysis cardiovascular complex non-stationarities quasi-periodicities series statphys23 system systems time topic-10 %T Multivariate Phase Rectified Signal Averaging %U http://st23.statphys23.org/webservices/abstract/preview_pop.php?ID_PAPER=641 %X The behaviour of many natural complex systems is characterized by nonstationarities and phase shifts making a conventional analysis of periodicities not fully reliable. Recently, the method of Phase Rectified Signal Averaging (PRSA) 1 has been introduced for the extraction of non-stationary oscillations out of noisy signals with varying mean. As an example, PRSA was shown to be superior in risk classification of sudden cardiac death after initial myocardial infarction 2. The main advantage of the PRSA is its capability to analyze separately periodicities occurring around increases (or, alternatively, decreases) of the signal. We now suggest a multivariate form of PRSA to study the relationships (i.e., interactions, partial syncronization, etc.) between two or more complex signals. We compare this method with cross correlation and cross spectra techniques and also discuss the application of multivariate PRSA in a recent baroreflex regulation study. 1) A. Bauer et al., Physica A 364, 423 (2006)\\ 2) A. Bauer et al., The Lancet 367, 1674 (2006)

@incollection{statphys23_0641, abstract = {The behaviour of many natural complex systems is characterized by nonstationarities and phase shifts making a conventional analysis of periodicities not fully reliable. Recently, the method of Phase Rectified Signal Averaging (PRSA) [1] has been introduced for the extraction of non-stationary oscillations out of noisy signals with varying mean. As an example, PRSA was shown to be superior in risk classification of sudden cardiac death after initial myocardial infarction [2]. The main advantage of the PRSA is its capability to analyze separately periodicities occurring around increases (or, alternatively, decreases) of the signal. We now suggest a multivariate form of PRSA to study the relationships (i.e., interactions, partial syncronization, etc.) between two or more complex signals. We compare this method with cross correlation and cross spectra techniques and also discuss the application of multivariate PRSA in a recent baroreflex regulation study. 1) A. Bauer et al., Physica A 364, 423 (2006)\\ 2) A. Bauer et al., The Lancet 367, 1674 (2006)}, added-at = {2007-06-20T10:16:09.000+0200}, address = {Genova, Italy}, author = {Schumann, A.Y. and Kantelhardt, J.W. and Bauer, A. and Schmidt, G.}, biburl = {https://www.bibsonomy.org/bibtex/265675c976623270a0e654462016a480c/statphys23}, booktitle = {Abstract Book of the XXIII IUPAP International Conference on Statistical Physics}, editor = {Pietronero, Luciano and Loreto, Vittorio and Zapperi, Stefano}, interhash = {f428c361efaaca73cff885310df31815}, intrahash = {65675c976623270a0e654462016a480c}, keywords = {analysis cardiovascular complex non-stationarities quasi-periodicities series statphys23 system systems time topic-10}, month = {9-13 July}, timestamp = {2007-06-20T10:16:25.000+0200}, title = {Multivariate Phase Rectified Signal Averaging}, url = {http://st23.statphys23.org/webservices/abstract/preview_pop.php?ID_PAPER=641}, year = 2007 }