Improved Permutation Entropy for Measuring Complexity of Time Series under Noisy Condition
Improved Permutation Entropy for Measuring Complexity of Time Series under Noisy Condition
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改进的排列熵用于测量噪声条件下时间序列的复杂性
DOI：
10.1155/2019/1403829
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发表时间：
2019-01-01
期刊：
影响因子：
2.3
通讯作者：
Yu, Jing
中科院分区：
文献类型：
--
作者：
Chen, Zhe;Li, Yaan;Yu, Jing
测量观测时间序列的复杂性对于理解所研究系统的特征起着重要作用。排列熵（PE）是复杂性分析的强大工具，但它有一些局限性。例如，丢弃幅度信息；等式（即分析信号中的相等值）没有得到正确处理；噪声条件下的性能还有待提高。本文提出了改进的排列熵（IPE）。所提出的方法结合了之前 PE 修改的一些优点。其有效性通过合成和实验分析得到验证。与 PE 相比，IPE 能够检测尖峰特征并正确区分心率变异性 (HRV) 信号。此外，它在噪声条件下表现更好。船舶分类实验结果表明，IPE在0dB下的识别率比PE高28.66%。因此，IPE 可以作为 PE 的替代品来分析噪声条件下的时间序列。
Measuring complexity of observed time series plays an important role for understanding the characteristics of the system under study. Permutation entropy (PE) is a powerful tool for complexity analysis, but it has some limitations. For example, the amplitude information is discarded; the equalities (i.e., equal values in the analysed signal) are not properly dealt with; and the performance under noisy condition remains to be improved. In this paper, the improved permutation entropy (IPE) is proposed. The presented method combines some advantages of previous modifications of PE. Its effectiveness is validated through both synthetic and experimental analyses. Compared with PE, IPE is capable of detecting spiky features and correctly differentiating heart rate variability (HRV) signals. Moreover, it performs better under noisy condition. Ship classification experiment results demonstrate that IPE achieves 28.66% higher recognition rate than PE at 0dB. Hence, IPE could be used as an alternative of PE for analysing time series under noisy condition.