Preprocessing RR interval time series for heart rate variability analysis and estimates of standard deviation of RR intervals

Heart rate variability is concerned with the analysis of the fluctuations in the interval between heart beats known as RR intervals. The long time RR time series obtained suffer from non-stationarity and the presence of ectopic beats, which prevents extraction of useful statistical information. The paper describes a wavelet-based technique for trend removal and a nonlinear filter to remove ectopic beats. This attempts to correct the limitations observed in a recent advanced heart rate toolkit [J. Niskanen, M.P. Tarvainen, P.O. Ranta-aho P.A. Karjalainen, Software for advanced HRV analysis, Comput. Meth. Prog. Biomed.,76 (2004) 73–81] when preprocessing. The results are encouraging. The preprocessed data are then used to obtain the standard deviation of RR interval time series (SDRR) of 15 healthy patients and 15 patients suffering from congestive heart failure. The results demonstrate the importance of preprocessing. The analysis show that the SDRR values of congestive heart failure patients are depressed compared to the healthy group.