Chaos detection in time series: a statistical approach

A statistical approach of chaos identification in time series is described and applied to the heart rate variability (HRV) signals of a normal person and a heart transplant recipient. The method compares the short-term predictability of the given time series to an ensemble of random data set that 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 of a normal person