Complex, self-regulating systems such as the human heart must process inputs with a broad range of characteristics to generate physiological data and time series. Many of these physiological time series seem to be highly chaotic, represent nonstationary data, and fluctuate in an irregular and complex manner. One hypothesis is that the seemingly chaotic structure of physiological time series arises from external and intrinsic perturbations that push the system away from a homeostatic set point. An alternative hypothesis is that the fluctuations are due, at least in part, to the system's underlying dynamics. In this review, we describe new computational approaches - based on new theoretical concepts - for analyzing physiological time series. We'll show that the application of these methods could potentially lead to a novel diagnostic tool for distinguishing healthy individuals from those with congestive heart failure (CHF)

Keywords