Abstract

The purpose of the study was to investigate the short- and long-term variations in the non-linear dynamics of heart rate variability, and to determine the relationships between conventional time and frequency domain methods and the newer non-linear methods of characterizing heart rate variability. Twelve healthy subjects were investigated by 3-h ambulatory ECG recordings repeated on 3 separate days. Correlation dimension, non-linear predictability, mean heart rate, and heart rate variability in the time and frequency domains were measured and compared with the results from corresponding surrogate time series. A small significant amount of non-linear dynamics exists in heart rate variability. Correlation dimensions and non-linear predictability are relatively specific parameters for each individual examined. The correlation dimension is inversely correlated to the heart rate and describes mainly linear correlations. Non-linear predictability is correlated with heart rate variability measured as the standard deviation of the R-R intervals and the respiratory activity expressed as power of the high-frequency band. The dynamics of heart rate variability changes suddenly even during resting, supine conditions. The abrupt changes are highly reproducible within the individual subjects. The study confirms that the correlation dimension of the R-R intervals is mostly due to linear correlations in the R-R intervals. A small but significant part is due to non-linear correlations between the R-R intervals. The different measures of heart rate variability (correlation dimension, average prediction error, and the standard deviation of the R-R intervals) characterize different properties of the signal, and are therefore not redundant measures. Heart rate variability cannot be described as a single chaotic system. Instead heart rate variability consists of intertwined periods with different non-linear dynamics. It is hypothesized that the heart rate is governed by a system with multiple "strange" attractors.

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