MULTISCALE ENTROPY ANALYSIS OF COMPLEX PHYSIOLOGIC SIGNALS

复杂生理信号的多尺度熵分析

• 批准号: 7366529
• 负责人: CHUNG-KANG PENG
• 金额: $ 0.81万
• 依托单位: BETH ISRAEL DEACONESS MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-03-01 至 2007-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7366529
• 关键词: MULTISCALE; ENTROPY; ANALYSIS; COMPLEX; PHYSIOLOGIC

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.

该子项目是利用NIH/NCRR资助的中心赠款提供的资源的许多研究子项目之一。子项目和研究者（PI）可能从另一个NIH来源获得主要资金，因此可以在其他CRISP条目中表示。所列机构为中心，不一定是研究者所在机构。人们对量化生理时间序列（如心率）的复杂性有相当大的兴趣。然而，传统的算法表明与随机输出相关联的某些病理过程的复杂性高于表现出长程相关性的健康动态。这种矛盾可能是由于这样的事实，即传统的算法不能占多个时间尺度固有的健康的生理动力学。介绍了一种计算复杂时间序列多尺度熵的方法。我们发现，MSE鲁棒分离健康和病理组，并始终产生更高的值为模拟的远程相关噪声比不相关的噪声。