Detecting chaos in HRV signals in human cardiac transplant recipients.

A statistical approach to chaos identification in time series is presented. The method is applied to numerical data generated by chaotic systems and to heart rate variability (HRV) signals of normal subjects and heart transplant recipients. 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 short-term prediction error is computed as a discriminating statistic for performing statistical hypothesis testing. The results suggest that HRV signals of the transplant recipients recorded 3 months after the transplantations show the same signature of chaos as that of the HRV signals for normal subjects.