Analysis of Heart Rate Variability Using Detrended Fluctuation Algorithm and Its Comparison with Pan Tompkin Algorithm

Abstract

Signal Processing is the best real time implementation of a specific problem. . This thesis deals with assessment of the different heart rate analysis methods for distinguishing between the heart disease and normal rhythm. Many linear and nonlinear methods, such as the time domain, frequency domain and the detrended fluctuation analysis, were tested and the reached results are presented. This work utilises the above techniques for diagnosis of an ECG signal by determining its nature as well as exploring the possibility for real-time implementation of the above techniques. This presents two algorithms, one is the Detrended Fluctuation Analysis algorithm and other is Pan tompkin algorithm. These two algorithms have been implemented in Matlab. The Heartbeat signals were frequently contain either slow trends or very slow frequency oscillation, the detrending was necessary as a preprocessing step to prepare for an analysis by using non-linear method measures, while the nonlinear measure were strongly affected by detrending. The DFA is technique for diagnosis of an ECG feature extraction. It is applicable in context of the nonstationary signal. It involves removing fluctuation trends from the signal. Such trends have to be well distinguished from the intrinsic fluctuations of the system in order to find the correct scaling behavior of fluctuations. Experimental data are affected by non-stationarities. HRV analysis is performed using the methods that are based on assumption that the signal is stationary within experiment duration, which is the normally not correct for the long-duration signals. The HRV analysis by nonlinear method brings useful prognosis information which will be helpful for the assessment of the cardiac condition. So we concluded that the DFA is suitable for the longterm analysis of non-stationary time series such as HRV signals.