MULTISCALE ENTROPY ANALYSIS OF COMPLEX PHYSIOLOGIC SIGNALS

本子项目是利用由NIH/NCRR资助的中心赠款提供的资源的众多研究子项目之一。子项目和研究者（PI）可能已经从另一个NIH来源获得了主要资金，因此可以在其他CRISP条目中表示。列出的机构是中心的，不一定是研究者的机构。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强有力地分离了健康组和病理组，并且模拟的远程相关噪声的值始终高于不相关噪声的值。

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. 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 than for uncorrelated noise.