Affiliation:
1. Medizinische Klinik der Universität Würzburg, 97080 Würzburg; and
2. Physiologisches Institut der Universität Würzburg, 97070 Würzburg, Germany
Abstract
We present a systematic approach for detecting nonlinear components in heart rate variability (HRV). The analysis is based on twenty-three 48-h Holter recordings in healthy persons during sinus rhythm. Although many segments of 1,024 R-R intervals are stationary, only few stationary segments of 8,192–32,768 R-R intervals can be found using a test of Isliker and Kurths ( Int. J. Bifurcation Chaos 3:1573–1579, 1993.). By comparing the correlation integrals from these segments and corresponding surrogate data sets, we reject the null hypothesis that these time series are realization of linear processes. On the basis of a test statistic exploring the differences of consecutive R-R intervals, we reject the hypothesis that the R-R intervals represent a static transformation of a linear process using optimized surrogate data. Furthermore, time irreversibility of the heartbeat data is demonstrated. We interpret these results as a strong evidence for nonlinear components in HRV. Thus R-R intervals from healthy persons contain more information than can be extracted by linear analysis in the time and frequency domain.
Publisher
American Physiological Society
Subject
Physiology (medical),Cardiology and Cardiovascular Medicine,Physiology
Cited by
76 articles.
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