Determining the Hurst exponent of fractal time series and its application to electrocardiographic analysis.
An alternative regression-based method for estimating the Hurst coefficient of a fractal time series is proposed. A formal mathematical description of the methodology is presented. The geometric relationship of the algorithm to the family of self-similar fractal curves is outlined. The computational structure of the algorithm is optimal for generation of real-time estimates of H. We show that the method can be applied to biologically-derived time series such as the cardiac interbeat interval and we obtain estimates of H from several diverse electrocardiographic data sets.
DePetrillo
P B
PB
Laboratory of Clinical Studies, Section of Clinical Science, Unit of Clinical and Biochemical Pharmacology, Bethesda, MD, USA. pbdp@helix.nih.gov
Speers
D
D
Ruttimann
U E
UE
D000465:Algorithms; D004562:Electrocardiography / Q000706:statistics & numerical data*; D005080:Exercise Test / Q000706:statistics & numerical data; D017709:Fractals; D006801:Humans; D008432:Mathematical Computing; D012016:Reference Values; D012044:Regression Analysis; D012815:Signal Processing, Computer-Assisted; D012984:Software