ABSTRACT
In this paper is reported a method using joint entropy to analyze the nonlinear dynamical complexity of short-term heart rate variability(HRV) signal. This method can effectively pick up dynamical information from the short-term heartbeat time series, reflect the dynamical complexity of heart rate variability, and so improve the quality of being covenient in clinical application. At first, the joint entropy method is demonstrated by applying it to the low-dimensional nonlinear deterministic systems such as logistic map and henon map. Then, the proposition is applied to the short-term heartbeat time series. The result shows that the method could robustly discriminate the patterns generated from healthy and pathologic states, as well as aging. Furthermore, the authors point out that decreased nonlinear dynamical complexity in the heartbeat time series with physiological aging and pathologic states is probably due to self-adjusting ability depression with aging and disease. At last, using the joint entropy method,the authors uncover nonrandom patterns in the ventricular response to atrial fibrillation.