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.