Methodological features of the analysis of the fractal dimension of the heart rate

Objectives. The aim of the present work is to determine the fractal dimension parameter calculated for a sequence of R–R intervals in order to identify the boundaries of its change for healthy and sick patients, as well as the possibility of its use as an additional factor in the detection of cardiac pathology.Methods. In order to determine the fractal dimension parameter, the Hurst-, Barrow-, minimum coverage area-, and Higuchi methods are used. For assessing the stationarity of a number of electrocardiography (ECG) intervals, a standard method is used to compare arithmetic averages and variances from samples of the total data array of ECG intervals. To identify differences in fractal dimensions of healthy and sick patients, this parameter was ranked. Using the Kolmogorov–Smirnov two-sample criterion, the difference between the distribution laws in the samples for healthy and sick patients is shown.Results. Among the considered methods for calculating the fractal dimension, the Higuchi method demonstrates the smallest data spread between healthy patients. By ranking the calculated fractional dimension values, it was possible to identify the difference between this parameter for healthy and sick patients. The difference in the distribution of fractal dimension of healthy and sick patients is shown to be statistically significant for the coverage and Higuchi methods. At the same time, when using the traditional Hurst method, there is no reason to reject the null hypothesis that two groups of patients belong to the same general population.Conclusions. Based on the obtained data, the difference between the fractal dimension indicators of the duration of R–R intervals of healthy and sick patients is shown to be statistically significant when using the Higuchi method. The fractal dimensions of healthy and sick patients can be effectively distinguished by ranking samples. The results of the research substantiate prospects for further studies aimed at using fractal characteristics of the heart rhythm to identify abnormalities of the latter, which can serve as an additional factor in determining heart pathologies.