HRV Analysis of Arrhythmias Using Linear - Nonlinear Parameters

Abstract

Heart rate variability (HRV) refers to the beat-to-beat alterations in heart rate. HRV analysis is based on the concept that, fast fluctuations reflect the changes of sympathetic and vagal activity which results in variability of intervals between R waves i.e. "RR intervals". In the current work HRV was assessed by traditional linear time and frequency-domain indexes, in parallel with the non linear Poincare plot analysis.Initially R peaks are detected from the ECG and RR interval signal is obtained which is further used to get the HRV signal. Spectral analysis of this HRV signal is done to estimate the power content in different frequency bands. Two frequency bands play a vital role in the power spectrum, a low frequency and a high frequency. Simultaneously, for time domain analysis, parameters such as mean of RR interval signal, standard deviation, coefficient of variance, root mean square of standard deviation are evaluated from RR interval signal and analyzed for different arrhythmias. Poincare plot analysis is an emerging quantitative-visual technique whereby the shape of the plot is categorized into functional classes that indicate the different arrhythmia. In this each R-R interval is plotted as a function of the previous one, and the standard deviations of the instantaneous and long-term R-R interval variability are calculated.It is observed that mean of RR interval signal and coefficient of variance plays an important role and can be used in classification along with the power content in low and high frequency bands. Also position and orientation of RR intervals in Poincare plot play an important role in visual identification of arrhythmias. The method is applied to normal sinus rhythm, ST change, CU Ventricular Tachyarrhythmia, Malignant ventricular arrhythmia signals. In this work, the different linear and non linear parameters evaluated show a particular range for various cardiac arrhythmias.

Nonlinear Dynamics of Reaction Time and Time Estimation during Repetitive Test

“…Other authors have also demonstrated the close relationship between CV and dynamic indices of the Poincaré plot in the case of other physiological variables, such as blood pressure [65,66] or blood sugar levels [58]. Since there is an interest for the simultaneous consideration of the mentioned parameters, as a novelty element, we proposed the application of this comparative analysis in the case of RT and VRT variability to display nonlinear aspects of the time-interval sequential patterns.…”