Nonlinear additive autoregressive model-based analysis of short-term heart rate variability.
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.
Wessel
Niels
N
Institute of Physics, University of Potsdam, Am Neuen Palais 10, 14415, Potsdam, Germany. wessel@agnld.uni-potsdam.de
Malberg
Hagen
H
Bauernschmitt
Robert
R
Schirdewan
Alexander
A
Kurths
Jürgen
J
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