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An Evaluation of Three Approaches Using Hurst Estimation to Differentiate Between Normal and Abnormal HRV

[+] Author Affiliations
Marwin Ko, Brandon Stark, Monica Barbadillo, YangQuan Chen

University of California - Merced, Merced, CA

Paper No. DETC2015-46966, pp. V009T07A036; 6 pages
  • ASME 2015 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
  • Volume 9: 2015 ASME/IEEE International Conference on Mechatronic and Embedded Systems and Applications
  • Boston, Massachusetts, USA, August 2–5, 2015
  • Conference Sponsors: Design Engineering Division, Computers and Information in Engineering Division
  • ISBN: 978-0-7918-5719-9
  • Copyright © 2015 by ASME


In this study, three different approaches using seven Hurst estimators to analyze heart rate variability (HRV) are evaluated. Herein, normal sinus rhythm and arrhythmia will be referred to as normal and abnormal HRV, respectively. The Hurst parameter is estimated using the following methods: aggregated variance, absolute value, box periodogram, difference variance, Higuchi, Peng, and rescaled range [1,2]. In this paper, the three approaches used are total time series estimation, cumulative window estimation, and sliding window estimation. These approaches were influenced by previous studies [3–5]. In all three approaches, bilateral results indicate that both normal and abnormal HRV data exhibit long range dependence (LRD), when H > 0.5 [6,7]. However, normal HRV data displayed a noticeably higher amount of LRD. In this novel study, the results display further potential research avenues using Hurst parameter estimation to analyze HRV data to differentiate between normal and abnormal HRV.

Copyright © 2015 by ASME



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