Detecting chaotic behavior in HRV signals in a human cardiac transplant recipient

An investigation of the chaotic behavior in heart rate variability (HRV) signals of a normal person and a heart transplant recipient is presented. A statistical approach of chaos identification in time series is described and applied to HRV signals. This method compares the short-term predictability for a given time series to an ensemble of random data which has the same Fourier spectrum as the original time series. The forecasting error is computed as a statistic for performing statistical hypothesis testing. The results suggest that the HRV signal of the transplant recipient recorded three months after the transplanting shows the same dynamical behavior as that of the HRV signal for a normal person