Entropy measures, entropy estimators, and their performance in quantifying complex dynamics: Effects of artifacts, nonstationarity, and long-range correlations.
Entropy measures, entropy estimators, and their performance in quantifying complex dynamics: Effects of artifacts, nonstationarity, and long-range correlations.
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DOI：
10.1103/physreve.95.062114
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发表时间：
2017-06
期刊：
影响因子：
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通讯作者：
Ivanov PC
中科院分区：
文献类型：
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作者：
Xiong W;Faes L;Ivanov PC
熵测度被广泛地应用于各种领域来量化动力系统的复杂性。然而，熵方法的实际应用是具有挑战性的，由于各种熵的措施和估计和现实世界的时间序列的复杂性，包括非平稳性和长程相关性（LRC）。我们进行了系统的研究的性能，偏差和限制的三个基本措施（熵，条件熵，信息存储）和三个传统上使用的估计（线性，核，最近邻）。我们调查的熵的估计和过程的特定参数的措施的依赖性，我们显示了三种类型的非平稳性的影响，由于人工制品（趋势，尖峰，局部方差变化）在随机自回归过程的模拟。我们还分析了LRC对熵测度的理论值和估计值的影响。最后，我们将熵方法应用于不同生理状态和临床条件下受试者的心率变异性数据。我们发现，熵的措施只能区分特定类型的心脏动力学的变化，适当的预处理是至关重要的正确估计和解释。通过论证熵方法的局限性，并阐明如何减轻偏倚和提供正确的结果解释，这项工作可以为熵方法的应用和现有研究的评价提供全面的参考。
Entropy measures are widely applied to quantify the complexity of dynamical systems in diverse fields. However, the practical application of entropy methods is challenging, due to the variety of entropy measures and estimators and the complexity of real-world time series, including nonstationarities and long-range correlations (LRC). We conduct a systematic study on the performance, bias, and limitations of three basic measures (entropy, conditional entropy, information storage) and three traditionally used estimators (linear, kernel, nearest neighbor). We investigate the dependence of entropy measures on estimator- and process-specific parameters, and we show the effects of three types of nonstationarities due to artifacts (trends, spikes, local variance change) in simulations of stochastic autoregressive processes. We also analyze the impact of LRC on the theoretical and estimated values of entropy measures. Finally, we apply entropy methods on heart rate variability data from subjects in different physiological states and clinical conditions. We find that entropy measures can only differentiate changes of specific types in cardiac dynamics and that appropriate preprocessing is vital for correct estimation and interpretation. Demonstrating the limitations of entropy methods and shedding light on how to mitigate bias and provide correct interpretations of results, this work can serve as a comprehensive reference for the application of entropy methods and the evaluation of existing studies.