©2010 International Journal of Computer Applications (0975 – 8887) Volume 1 – No. 12 71 HRV Analysis of Arrhythmias Using Linear – Nonlinear Parameters Asha V. Thalange Assistant Professor, Walchand Institute of Technology, Solapur, Maharashtra, India. Rohini R. Mergu Assistant Professor, Walchand Institute of Technology, Solapur, Maharashtra, India. 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. General Terms Signal Processing, Biomedical signal processing, ECG Arrhythmia Classification. Keywords Coefficient of variance, Heart Rate Time Series (HRTS), Heart Rate Variability (HRV), Poincare plot, Power Spectral Density (PSD), Standard deviation. 1. INTRODUCTION Heart disease is a broad term that includes several more specific heart conditions which are Coronary Heart Disease, Heart Attack, Acute Coronary Syndrome, Aortic Aneurysm and Dissection, Ischemia, Arrhythmias, Cardiomyopathy, Congenital Heart Disease, Peripheral Arterial Disease (PAD). ECG analysis plays an important role in identifying the disorder. Some of the ECG feature extraction methods implemented in previous research includes Discrete Wavelet Transform, Karhunen-Loeve Transform, Hermitian Basis and other methods ([1]-[3]).In recent years special attention has been given to the analysis of the heart rate variability (HRV) and its relation to the other physiological signals. Methods for quantifying HRV are categorized as: time domain, spectral or frequency domain, geometric and nonlinear ([4],[ 5]). In time domain analysis, the intervals between adjacent normal R waves are measured over the period of recordings. A variety of statistical variables can be calculated from the intervals [5]. In frequency domain method ([6],[7]) either fast Fourier transformation or autoregression techniques can be used to quantify cyclic fluctuations of RR intervals. Two peaks are seen in RR interval power spectra a low frequency peak between 0.04 Hz – 0.15 Hz, and high frequency peak between 0.15 Hz – 0.40 Hz. The magnitude of power in LF and HF regions provide quantitative index of the sympatho-vagal dynamic balance in control of the heart rate. One nonlinear method used is the Poincare plot [8]. It is a graphical representation of the correlation between consecutive RR intervals. It is a graph of each RR interval plotted against the next interval as shown in figure 1. Poincare plot analysis is an emerging quantitative-visual technique whereby the shape of the plot is categorized into functional classes that indicate the degree of heart failure in a subject. Two basic descriptors of the plot are SD1and SD2. The line of identity is the 45 0 imaginary diagonal line on the Poincare plot. SD1 and SD2 are dispersions as shown in figure 1.