Heart Variability Analysis by using Non-Linear Techniques and their Comparison

electrocardiogram (ECG) provides information about individual cardiac health. Aside from directly analyzing the ECG signals, researchers and doctors also extract other indirect measurements from the ECG signals and one of the most popular measurements is heart rate variability (HRV). Heart Rate Variability (HRV) measurements analyze how the RR intervals of an ECG signal, which show the variation between consecutive heartbeats, change over time. Heart rate (HR) is a non-stationary signal and its variation may contain indicators of current disease, or warnings about impending cardiac diseases. Hence, HR variation analysis (instantaneous HR against time axis) has become a popular noninvasive tool for assessing the activities of the autonomic nervous system. Computer based analytical tools for in-depth study of data over daylong intervals can be very useful in diagnostics (2). Therefore, in this paper two non linear techniques Poincare and Recurrence Quantification Analysis are implemented by using Matlab for HRV analysis. Three parameters SD1, SD2 and % REC are taken into consideration for doing the comparison between both the techniques.

[1]  Jürgen Kurths,et al.  Recurrence plots for the analysis of complex systems , 2009 .

[2]  A Voss,et al.  Ability of heart rate variability as screening tool for heart diseases in men , 2009, 2009 36th Annual Computers in Cardiology Conference (CinC).

[3]  M. T. Aparicio,et al.  Detecting Determinism Using Recurrence Quantification Analysis: Three Test Procedures , 2005 .

[4]  G. Breithardt,et al.  Heart rate variability: standards of measurement, physiological interpretation and clinical use. Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. , 1996 .

[5]  A. Malliani,et al.  Heart rate variability. Standards of measurement, physiological interpretation, and clinical use , 1996 .

[6]  J. Zbilut,et al.  Recurrence quantification analysis as a tool for nonlinear exploration of nonstationary cardiac signals. , 2002, Medical engineering & physics.

[7]  Lerma Claudia,et al.  Poincaré plot indexes of heart rate variability capture dynamic adaptations after haemodialysis in chronic renal failure patients , 2003 .

[8]  Claudia Lerma,et al.  Poincaré plot indexes of heart rate variability capture dynamic adaptations after haemodialysis in chronic renal failure patients. , 2003, Clinical physiology and functional imaging.

[9]  A M Nasrabadi,et al.  Utilizing occurrence sequence of Heart Rate's phase space points in order to discriminate heart Arrhythmia , 2010, 2010 17th Iranian Conference of Biomedical Engineering (ICBME).

[10]  U. Rajendra Acharya,et al.  Heart rate variability: a review , 2006, Medical and Biological Engineering and Computing.