Physiological time-series analysis using approximate entropy and sample entropy.
Physiological time-series analysis using approximate entropy and sample entropy.
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DOI：
10.1152/ajpheart.2000.278.6.h2039
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发表时间：
2000-06
期刊：
影响因子：
--
通讯作者：
Joshua S. Richman;J. R. Moorman
中科院分区：
文献类型：
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作者：
Joshua S. Richman;J. R. Moorman
熵与动力系统相关，是信息产生的速率。然而，由时间序列表示的系统熵的估计方法不太适合分析心血管和其他生物学研究中遇到的短且嘈杂的数据集。 Pincus 引入了近似熵（ApEn），这是一组与熵密切相关的系统复杂性度量，很容易应用于临床心血管和其他时间序列。然而，ApEn 统计数据导致结果不一致。我们开发了一种新的相关复杂性度量，即样本熵 (SampEn)，并通过使用它们来分析具有已知概率特征的随机数集来比较 ApEn 和 SampEn。我们还评估了 cross-ApEn 和 cross-SampEn，它们使用心血管数据集来测量两个不同时间序列的相似性。在广泛的条件下，SampEn 比 ApEn 更符合理论。 SampEn 统计数据准确性的提高应使其可用于实验临床心血管和其他生物时间序列的研究。
Entropy, as it relates to dynamical systems, is the rate of information production. Methods for estimation of the entropy of a system represented by a time series are not, however, well suited to analysis of the short and noisy data sets encountered in cardiovascular and other biological studies. Pincus introduced approximate entropy (ApEn), a set of measures of system complexity closely related to entropy, which is easily applied to clinical cardiovascular and other time series. ApEn statistics, however, lead to inconsistent results. We have developed a new and related complexity measure, sample entropy (SampEn), and have compared ApEn and SampEn by using them to analyze sets of random numbers with known probabilistic character. We have also evaluated cross-ApEn and cross-SampEn, which use cardiovascular data sets to measure the similarity of two distinct time series. SampEn agreed with theory much more closely than ApEn over a broad range of conditions. The improved accuracy of SampEn statistics should make them useful in the study of experimental clinical cardiovascular and other biological time series.