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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. R-R intervals stationarity nonlinear dynamics surrogate data linear models Footnotes Address for reprint requests: M. Meesmann, Medizinische Klinik der Universität Würzburg, Josef-Schneider-Str.2, 97080 Würzburg, Germany. This study was supported by the Bundesministerium für Bildung und Forschung, Germany, with additional support by St. Jude Medical GmbH Ventritex, Leverkusen, within the project “Nichtlineare EKG-Analysen zur Risikostratifizierung und Therapiebeurteilung von Herzpatienten.” Copyright © 1998 the American Physiological Society
AJP - Heart and Circulatory Physiology – The American Physiological Society
Published: Nov 1, 1998
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