Based on permutation entropy (PE), which has been presented as a measure to characterize the complexity of nonlinear time series, phase permutation entropy (PPE) is proposed in this paper. Experiments are implemented using artificial and actual data to show the performance of PPE algorithm. The achieved results demonstrate that PPE can amplify the detection effect of dynamical changes compared with PE whether using the logistic map or actual signals. Increasing embedding dimension can improve the capability of detecting dynamical changes using PPE method. Furthermore, PPE is not sensitive to data length when embedding dimension is less than or equal to 5 and it is more susceptible to noise than PE. The results from actual signals show that PPE can be used as an effective analytical tool in the field of biomedical and engineering signals processing.

基于置换熵（PE），作为表征非线性时间序列复杂性的一种措施，本文提出了相位置换熵（PPE）。利用人工和实际数据进行了实验，以展示PPE算法的性能。获得的结果表明，无论使用逻辑图还是实际信号，与PE相比，PPE都能放大动态变化的检测效果。嵌入尺寸的增加可以提高使用PPE方法检测动态变化的能力。此外，当嵌入尺寸小于或等于5时，PPE对数据长度不敏感，并且比PE对噪声更敏感。实际信号的结果表明，PPE可以用作生物医学和工程信号处理领域的有效分析工具。