NONLINEAR FORECASTING AND THE DYNAMICS OF CARDIAC-RHYTHM

Since the initial development of the electrocardiogram, cardiologists have made dramatic advances in the description and understanding of ca rdiac arrhythmias. Despite these successes, the analysis of cardiac rh ythm has remained largely descriptive. Recently, the principles of non linear dynamics, or chaos theory, have been applied to the quantitativ e analysis of cardiac rhythm in a variety of diverse situations. In ch aos theory, three types of signals can be defined: periodic signals, w hich repeat themselves over some finite rime interval, chaotic signals , which, while deterministic, demonstrate complex behavior and do not repeat themselves, and random signals, which are unpredictable and non deterministic. The technique of nonlinear forecasting defines trajecto ries in a suitably defined phase space and uses the future evolution o f trajectories that are close to each other over short distances to ma ke predictions for times further into the future. The ability to relia bly predict the future evolution of the trajectories derived from any signal is an important characteristic of the underlying dynamics of th e signal and can therefore used to determine those dynamics. The found ation of nonlinear forecasting is reviewed, and an algorithm is descri bed that can be used to determine the underlying dynamics of a signal and has been applied to the analysis of R-R interval data.