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。子弹和调查员（PI）可能已经从其他NIH来源获得了主要资金，因此可以在其他清晰的条目中代表。列出的机构适用于该中心，这不一定是调查员的机构。量化生理时间序列的复杂性，例如心率，人们引起了极大的兴趣。但是，传统算法表明，与随机输出相关的某些病理过程的复杂性比显示长期相关性的健康动力学更高。这种悖论可能是由于传统算法无法解释健康生理动力学固有的多个时间尺度的事实。我们引入了一种计算复杂时间序列的多尺度熵（MSE）的方法。我们发现，MSE可靠地分离健康和病理组，并始终如一地产生模拟的远程相关噪声的值，而不是与无关的噪声相比。