Study of features based on nonlinear dynamical modeling in ECG arrhythmia detection and classification

Abstract

We present a study of the nonlinear dynamics of electrocardiogram (ECG) signals for arrhythmia characterization. The correlation dimension and largest Lyapunov exponent are used to model the chaotic nature of five different classes of ECG signals. The model parameters are evaluated for a large number of real ECG signals within each class and the results are reported. The presented algorithms allow automatic calculation of the features. The statistical analysis of the calculated features indicates that they differ significantly between normal heart rhythm and the different arrhythmia types and, hence, can be rather useful in ECG arrhythmia detection. On the other hand, the results indicate that the discrimination between different arrhythmia types is difficult using such features. The results of this work are supported by statistical analysis that provides a clear outline for the potential uses and limitations of these features.

Effect of reflexological stimulation on heart rate variability

“…Fell et al [10] and Radhakrishna Rao et al [34] have tried the non-linear analysis of ECG and HRV signals, respectively. Several methods have been proposed: correlation dimension (CD) [35] and Poincare plot geometry [31].…”

Non-Linear Feature Extraction for Heart Rate Variability: An Overview

“…The non-linear analysis and chaos theory has been applied to many medical application [3].There are many methods are used from non-linear dynamics which will read the behavior of ECG signals such as Phase Space Reconstruction, Correlation Dimensions , Lyapunov Exponents, Recurrence Quantification Analysis etc. This paper gives a brief survey about the non-linear methods which are used in characterization of HRV patterns Section 2 is about the Literature survey of non-linear dynamics and Section 3 is for Conclusion.…”

Informative Nature and Nonlinearity of Lagged Poincaré Plots Indices in Analysis of Heart Rate Variability

“…There are publications examining gender differences in the nonlinear structure of HRV [20][21][22] and its variations throughout the times of day and night [22]. Certain publications deal with diagnosis and classification of arrhythmias based on nonlinear parameters [23,24], even up to the point of prognosis of various cardiovascular diseases like ventricular tachycardia and congestive heart failure [25]. With that, the additional clinical validation of existing novel methods is needed to define the clinical predictive value of nonlinear parameters as well as their robustness in relation to reproducibility and widespread clinical use [26].…”