Izvestiya of Saratov University.
ISSN 1817-3020 (Print)
ISSN 2542-193X (Online)

временные ряды

The method for diagnostics of the phase synchronization of the vegetative control of blood circulation in real time

Background and Objectives: The development of methods for the analysis of non-stationary signals of biological nature makes it possible to solve a number of fundamental and applied problems. The use of these methods is promising for the diagnosis and prevention of diseases of the cardiovascular system. The creation of the device makes it possible to detect diseases at an early stage. However, this requires the development of methods for analyzing non-stationary signals of biological nature in real time.


Разработан метод диагностики фазовой синхронизации, ориентированный на анализ в реальном времени нестационарных сигналов биологической природы. Проведено сопоставление статистических свойств предложенного подхода с известным методом диагностики синхронизации, зарекомендовавшим себя при анализе экспериментальных данных. Сопоставление производиться на примере анализа искусственных данных, воспроизводящих статистические свойства экспериментальных временных реализаций контуров вегетативного контроля кровообращения.

Comparison of Methods for Phase Synchronization Diagnostics from Test Data Modeling Nonstationary Signals of Biological Nature

Three methods of phase synchronization diagnostics from time series are compared by the analysis of test data. These data reproduce the statistics of experimental temporal realizations recorded from the system of human cardiovascular system autonomic control.

Contemporary problems in modeling from time series

Mathematical modeling from discrete sequences of experimental data (time series) is an actively developing field in mathematical statistics and nonlinear dynamics. It started from approximation of a set of data points on a plane with a smooth curve, while currently such empiric models take the form of sophisticated differential and difference equations and are capable of describing even nonlinear oscillatory and wave phenomena. Practical applications of the empiric models are various ranging from future forecasts to technical and medical diagnostics.