Multiscale entropy analysis of complex physiologic time series
Multiscale entropy analysis of complex physiologic time series
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DOI：
10.1103/physrevlett.89.068102
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发表时间：
2002-08-05
影响因子：
8.6
通讯作者：
Peng, CK
中科院分区：
文献类型：
--
作者：
Costa, M;Goldberger, AL;Peng, CK
人们对量化生理时间序列（如心率）的复杂性非常感兴趣。然而，传统算法表明，与随机输出相关的某些病理过程比具有长期相关性的健康动态具有更高的复杂性。这一悖论可能是由于传统算法未能考虑到健康生理动力学中固有的多个时间尺度。介绍了一种计算复杂时间序列多尺度熵（MSE）的方法。我们发现MSE强有力地将健康组和病理组分开，并且与不相关噪声相比，模拟远程相关噪声始终产生更高的值。
There has been considerable interest in quantifying the complexity of physiologic time series, such as heart rate. However, traditional algorithms indicate higher complexity for certain pathologic processes associated with random outputs than for healthy dynamics exhibiting long-range correlations. This paradox may be due to the fact that conventional algorithms fail to account for the multiple time scales inherent in healthy physiologic dynamics. We introduce a method to calculate multiscale entropy (MSE) for complex time series. We find that MSE robustly separates healthy and pathologic groups and consistently yields higher values for simulated long-range correlated noise compared to uncorrelated noise.