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IEEJ Transactions on Electrical and Electronic Engineering. Volume 8, Issue 3, pages 247–252, 2013.
Kiyoshi Kotani Member1,*, Hajime Ogawa Non-member1,2, Hisayoshi Ogata Non-member3,4, Takashi Numata Non-member1, Kimitaka Nakazawa Non-member3,5 and Yasuhiko Jimbo Member.
Abstract
In this paper, we propose a method, named cutting detrended fluctuation analysis (cutting DFA), for the evaluation of global and local fractal properties from data containing noisy observational errors. This method evaluates how the Hurst exponent varies by cutting out in the order of the largest deviation from the mean value. An analysis of the simulated fractal signal reveals that cutting DFA exhibits a linear transition of the Hurst exponent with respect to the cutting rate. The mean value and the slope thereof reflect the global and local fractal properties of the time series, respectively. We then analyze the long-term heart rate variability of congestive heart failure (CHF) patients and healthy subjects with observational errors. It is demonstrated that CHF patients have a higher value in the mean Hurst exponent than healthy subjects, indicating a higher global Hurst exponent. Also, it is demonstrated that healthy subjects have a statistically significant difference in slope from monofractal time series, while CHF patients do not. These results indicate that the local fractal property of healthy subjects is far from monofractal time series, which matches previous findings. Therefore, it is confirmed that cutting DFA extracts fractal properties of original heart rate variability from data containing observational errors. © 2013 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.
