Study of HRV Dynamics and Comparison Using Wavelet Analysis and Pan Tompkins Algorithm

Abstract

Heart rate variability (HRV) provides a non-invasive means of quantifying cardiac autonomic activity. It has been shown to be a powerful predictor of arrhythmia related complications in patients surviving the acute phase of myocardial infarction. It has also increasingly been used to measure autonomic nervous system activities. This work aims to study heart rate variability during normal or abnormal functioning of the heart and whether it can be used to predict the occurrence of any abnormality. Additionally, it aims to compare results based on wavelet analysis and Pan Tompkins algorithm. Both time domain analysis and frequency domain analysis of HRV are presented. The HRV dynamics is evaluated using non-parametric (Fast Fourier Transform) method. Results of stimulations in MATLAB are presented.