Azami Hamed, Escudero Javier
School of Engineering, Institute for Digital Communications, University of Edinburgh, Edinburgh EH9 3FB, UK.
Entropy (Basel). 2018 Mar 20;20(3):210. doi: 10.3390/e20030210.
Dispersion entropy (DispEn) is a recently introduced entropy metric to quantify the uncertainty of time series. It is fast and, so far, it has demonstrated very good performance in the characterisation of time series. It includes a mapping step, but the effect of different mappings has not been studied yet. Here, we investigate the effect of linear and nonlinear mapping approaches in DispEn. We also inspect the sensitivity of different parameters of DispEn to noise. Moreover, we develop fluctuation-based DispEn (FDispEn) as a measure to deal with only the fluctuations of time series. Furthermore, the original and fluctuation-based forbidden dispersion patterns are introduced to discriminate deterministic from stochastic time series. Finally, we compare the performance of DispEn, FDispEn, permutation entropy, sample entropy, and Lempel-Ziv complexity on two physiological datasets. The results show that DispEn is the most consistent technique to distinguish various dynamics of the biomedical signals. Due to their advantages over existing entropy methods, DispEn and FDispEn are expected to be broadly used for the characterization of a wide variety of real-world time series. The MATLAB codes used in this paper are freely available at http://dx.doi.org/10.7488/ds/2326.
离散熵（DispEn）是一种最近引入的熵度量，用于量化时间序列的不确定性。它计算速度快，并且到目前为止，在时间序列特征描述方面已表现出非常好的性能。它包括一个映射步骤，但不同映射的效果尚未得到研究。在此，我们研究线性和非线性映射方法在离散熵中的作用。我们还考察了离散熵不同参数对噪声的敏感性。此外，我们开发了基于波动的离散熵（FDispEn）作为一种仅处理时间序列波动的度量。此外，引入了原始的和基于波动的禁止离散模式，以区分确定性和随机时间序列。最后，我们在两个生理数据集上比较了离散熵、基于波动的离散熵、排列熵、样本熵和莱姆尔 - 齐夫复杂度的性能。结果表明，离散熵是区分生物医学信号各种动态的最一致的技术。由于相对于现有熵方法具有优势，离散熵和基于波动的离散熵有望广泛用于各种实际时间序列的特征描述。本文使用的MATLAB代码可在http://dx.doi.org/10.7488/ds/2326免费获取。
