Non-linear properties of <i>R–R</i> distributions as a measure of heart rate variability

Abstract

We analyze the dynamic quality of the R–R interbeat intervals of electrocardiographic signals from healthy people and from patients with premature ventricular contractions (PVCs) by applying different measure algorithms to standardised public domain data sets of heart rate variability. Our aim is to assess the utility of these algorithms for the above mentioned purposes. Long and short time series, 24 and 0.50 h respectively, of interbeat intervals of healthy and PVC subjects were compared with the aim of developing a fast method to investigate their temporal organization. Two different methods were used: power spectral analysis and the integral correlation method. Power spectral analysis has proven to be a powerful tool for detecting long-range correlations. If it is applied in a short time series, power spectra of healthy and PVC subjects show a similar behavior, which disqualifies power spectral analysis as a fast method to distinguish healthy from PVC subjects. The integral correlation method allows us to study the fractal properties of interbeat intervals of electrocardiographic signals. The cardiac activity of healthy and PVC people stems from dynamics of chaotic nature characterized by correlation dimensions df equal to 3:40 ± 0:50 and 5:00 ± 0:80 for healthy and PVC subjects respectively. The methodology presented in this article bridges the gap between theoretical and experimental studies of non-linear phenomena. From our results we conclude that the minimum number of coupled differential equations to describe cardiac activity must be six and seven for healthy and PVC individuals respectively. From the present analysis we conclude that the correlation integral method is particularly suitable, in comparison with the power spectral analysis, for the early detection of arrhythmias on short time (0.5 h) series.