Abstract
Aims: We have developed a new approach to the study of human heart rate, which is based on the use of a vector rhythmocardiosignal, which includes as its component the classical rhythmocardiosignal in the form of a sequence of heart cycle durations in an electrocardiogram. Background: Most modern automated heart rate analysis systems are based on a statistical analysis of the rhythmocardiogram, which is an ordered set of R-R interval durations in a recorded electrocardiogram. However, this approach is not very informative, since R-R intervals reflect only the change in the duration of cardiac cycles over time and not the entire set of time intervals between single-phase values of the electrocardiosignal for all its phases. Objective: The aim of this paper is to present a mathematical model in the form of a vector of stationary and permanently connected random sequences of a rhythmocardiosignal with an increased resolution for its processing problems. It shows how the vector rhythmocardiosignal is formed and processed in diagnostic systems. The structure of probabilistic characteristics of this model is recorded for statistical analysis of heart rate in modern cardiodiagnostics systems. Methods: Based on a new mathematical model of a vector rhythmocardiosignal in the form of a vector of stationary and permanently connected random sequences, new methods for statistical estimation of spectral-correlation characteristics of heart rate with increased resolution have been developed. Results: The spectral power densities of the components of the vector rhythmocardiosignal are justified as new diagnostic features when performing rhythm analysis in modern cardiodiagnostics systems, complementing the known signs and increasing the informative value of heart rate analysis in modern cardiodiagnostics systems. Conclusion: The structure of probabilistic characteristics of the proposed mathematical model for heart rate analysis in modern cardiodiagnostics systems is studied. It is shown how the vector rhythmocardiosignal is formed, and its statistical processing is carried out on the basis of the proposed mathematical model and developed methods.
Highlights
IntroductionMost methods for processing the classical rhythmocardiosignal in the framework of the stochastic approach are based on three of its probabilistic models, namely, a random variable, a random stationary sequence, and a periodically correlated random sequence are used
As a component of this software package, a system of computer programs has been developed for the automated formation and the statistical analysis of heart rate based on a vector rhythmocardiosignal, which expanded the functionality of the existing software package and made it possible to automatically analyze the heart rate with increased information content
The mathematical model of a high-resolution rhythmocardiosignal has been verified by testing the statistical hypotheses about stationarity for the normal distribution of components of a high-resolution rhythmocardiosignal, which has been the basis for reducing the computational complexity of statistical methods for the analysis of heart rate in computer systems of medical diagnostics
Summary
Most methods for processing the classical rhythmocardiosignal in the framework of the stochastic approach are based on three of its probabilistic models, namely, a random variable, a random stationary sequence, and a periodically correlated random sequence are used These models are based on an approach to describing the heart rate as a sequence of RR intervals that uses a ritmocardiogram (classical ritmocardiogram), which imposes significant limitations on the informative value of heart rate analysis. Most modern automated heart rate analysis systems are based on a statistical analysis of the rhythmocardiogram, which is an ordered set of R-R interval durations in a recorded electrocardiogram This approach is not very informative, since R-R intervals reflect only the change in the duration of cardiac cycles over time and not the entire set of time intervals between single-phase values of the electrocardiosignal for all its phases
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