In this paper, we propose dispersion Lempel–Ziv complexity and combine it with dispersion entropy to construct complexity-entropy plane. This measure aims to identify time series with different properties. These two quantities take advantage of nonlinear symbolic representation and quantify the complexity of series. In addition, they are relatively stable for different parameters and robust against white noise with different levels. The complexity-entropy plane is able to identify nonlinear chaotic maps and distinguish them from stochastic process. Also, the multiscale features of signals are detected, suggesting the underlying dynamics. This technique is effective to track the dynamical changes of heart rate time series and to characterize different pathologic states. It can also prove that aging and disease correspond to the loss of complexity.

在本文中，我们提出了色散Lempel-Ziv复杂度，并将其与色散熵结合以构造复杂度熵平面。该措施旨在确定具有不同属性的时间序列。这两个量利用了非线性符号表示并量化了序列的复杂性。此外，它们对于不同的参数相对稳定，并且对不同级别的白噪声具有鲁棒性。复杂度熵平面能够识别非线性混沌图，并将其与随机过程区分开。同样，信号的多尺度特征被检测到，暗示了潜在的动态。该技术可有效跟踪心率时间序列的动态变化并表征不同的病理状态。它还可以证明衰老和疾病与复杂性的丧失相对应。