用联合熵分析短时心率变异信号的非线性动力学复杂性

用联合熵方法来分析短时心率变异性信号,该方法可以有效地从短时心跳间期信号中提取出动力学信息,从而反映心率变异性的强弱,为临床应用提供了方便。我们首先介绍联合熵基本理论,用低维的混沌序列进行了检验,证明该方法有效。然后将其应用于人体短时的心跳间期时间序列,该方法可以揭示复杂生理信号所具有的动力学特征以及衰老和疾病所伴随的非线性动力学复杂性的丢失和降低的个体自适应能力。最后,用联合熵方法来考察短时心率变异性信号的非随机性程度,该方法可以有效的揭示心室对心室纤维性颤动响应的非随机模式。

Nonlinear Dynamical Complexity Analysis of Short-term Heartbeat Series Using Joint Entropy

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.