Nonlinear Dynamics Methods for Analysis of ECG Signals

Abstract

Nonlinear dynamics methods are increasingly used in the process of information analysis of electrocardiogram (ECG) signals due to the fact that they enable the monitoring of heart rate dynamics. These methods complement the traditional analysis performed by applying the linear methods (Time- and Frequency-Domain). This article presents the results of the study of the nonlinear dynamic characteristics of the time intervals between heart intervals (RR time series), through the application of the following methods of the nonlinear dynamics: reconstructed phase space analysis, largest Lyapunov exponent and Poincare plot. Two groups of people were studied: healthy and unhealthy subjects (patients with heart failure). The performed statistical analysis of the calculated characteristics describing the nonlinear dynamics of the RR time series show that it differ significantly between the two groups studied. Therefore, the application of these methods may be helpful in the diagnosis of cardiovascular disease. The introduction of nonlinear dynamics methods as well as linear ECG signal analysis methods requires information technology professionals to actively collaborate with cardiologists to integrate these new methods into clinical practice to support physician activity in diagnosis and early detection of cardiovascular diseases. The analysis of the investigated signals was performed by applying a web-based application using a serverless architecture, which is experimental and has no commercial purpose.