title: Nonlinear additive autoregressive model-based analysis of short-term heart rate variability.
abstract: In this contribution we test the hypothesis that nonlinear additive autoregressive model-based data analysis improves the diagnostic ability based on short-term heart rate variability. For this purpose, a nonlinear regression approach, namely, the maximal correlation method is applied to the data of 37 patients with dilated cardiomyopathy as well as of 37 age- and sex-matched healthy subjects. We find that this approach is a powerful tool in discriminating both groups and promising for further model-based analyses.
authors.affiliation: Institute of Physics, University of Potsdam, Am Neuen Palais 10, 14415, Potsdam, Germany. wessel@agnld.uni-potsdam.de
authors.forename: Niels
authors.initials: N
authors.lastname: Wessel
authors.forename: Hagen
authors.initials: H
authors.lastname: Malberg
authors.forename: Robert
authors.initials: R
authors.lastname: Bauernschmitt
authors.forename: Alexander
authors.initials: A
authors.lastname: Schirdewan
authors.forename: Jürgen
authors.initials: J
authors.lastname: Kurths
mesh_terms: D000465:Algorithms; D002311:Cardiomyopathy, Dilated / Q000175:diagnosis*; D016022:Case-Control Studies; D003627:Data Interpretation, Statistical; D015716:Electrocardiography, Ambulatory; D005260:Female; D006339:Heart Rate / Q000502:physiology; D006801:Humans; D008297:Male; D008875:Middle Aged; D017711:Nonlinear Dynamics; D013997:Time Factors