International Journal of Advance Research In Science And Engineering http://www.ijarse.com IJARSE, Vol. No.3, Issue No.1, January 2014 ISSN-2319-8354(E) 49 | Page www.ijarse.com HEART RATE VARIABILITY ANALYSIS: A REVIEW 1 D Mahesh Kumar, 2 S C Prasannakumar, 3 B G Sudarshan, 4 D Jayadevappa, 1 Asst. Professor, 4 Professor, Dept. of Inst. Tech., JSSATE, VTU, Karnataka, (India) 2 Professor, Dept. of Inst. Tech., RVCE, VTU, Karnataka, (India) 3 Associate professor/medical officer, RVCE, VTU, Karnataka, (India) ABSTRACT Biological signals are nonlinear and non periodic in nature. The signals show self-similarity in various scales of time. Heart rate variability is a noisy time series having self-similar and self affine characteristics. Its analysis is commonly used in accessing the autonomic nervous system of the human body and in diagnosing of cardiac status and other parameters related to ANS in both normal and pathological conditions. The last two decades have shown that there is significant relationship between the Autonomic nervous system (ANS) and cardiovascular mortality, including sudden cardiac death. Easy and non-invasive way to measure has popularized its use. Fractal dimension is one of the robust methods in characterizing complex time series. Non linear time series methods are meant for extracting invariant of the dynamics of system that is a unique characteristic of the system. Unlike Euclidean dimension, fractal dimension is a fractal number that is characteristic of the statistical property and dynamics of the EKG. This paper reviews various methods used for the analysis of HRV and comparing HRV parameters in variouspathological conditions. Keywords: Heart Rate Variability (HRV), Fractal Dimension, Hurst Component, Autonomic Nervous System (ANS), FFT and Spectral Analysis. I INTRODUCTION HRV is a ECG signal useful for understanding the status of the Autonomic Nervous System (ANS). HRV reflects the heart‟s ability to adapt to the changing circumstances by detecting and fast responding to the unpredictable stimuli. HRV analysis has the ability to assess overall cardiac health and the state of the ANS responsible for regulating cardiac activity. A key advantage of HRV analysis is the ability to detect the early signs of development of pathological processes or the presence of a functional disorder which may not be revealed by the procedures of ordinary methods. Nirmal D.Thakur et al [1] proposed conventional predictors, which diminished HRV predicts both death and arrhythmic events with greater sensitivity and specificity. Fractal analysis is an emerging tool in cardiovascular diagnostics. It is based on recent discovery that the heart rate time series exhibits statistical properties associated with the dynamics of the heart beat. From past 20 years it is