HILBERT HUANG TRANSFORM ANALYSIS OF COMPLEX BIOMEDICAL SIGNALS

这个子项目是利用由NIH/NCRR资助的中心拨款提供的资源的许多研究子项目之一。子项目和调查员(PI)可能从另一个NIH来源获得了主要资金，因此可能会出现在其他CRISE条目中。列出的机构是针对中心的，而不一定是针对调查员的机构。傅里叶频谱分析已经成为检查全球幅频分布的标准方法。然而，傅里叶变换在两个关键限制下是有效的：1)系统必须是线性的；2)数据必须是严格的周期或平稳的。可用的“真实世界”数据通常是有限持续时间的、非平稳的，并且来自经常本质上是非线性的系统。在这种情况下，傅立叶分析的用处有限。然而，由于缺乏合适的替代方法，仍然使用傅立叶分析来处理这类数据。对傅立叶分析的松散应用以及对线性和平稳性的假设可能会导致误导结论。最近发展起来的希尔伯特-黄变换(HHT)为数据分析提供了一种强大的通用算法。它的优点是能够处理短的、非平稳的和非线性的数据集。我们正在应用这种方法来研究疾病和衰老对自主神经控制心率的影响。

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. Fourier spectral analysis has been the standard method for examining global amplitude-frequency distributions. However, Fourier Transform is valid under two crucial restrictions: 1) the system must be linear; and 2) the data must be strictly periodic or stationary. The available "real-world" data are usually of finite duration, nonstationary, and from systems that are frequently intrinsically nonlinear. Under these conditions, Fourier analysis is of limited use. For lack of suitable alternatives, however, Fourier analysis is still employed to process such data. The loose application of Fourier analysis and the assumption of linearity and stationarity may lead to misleading conclusions. The recently developed Hilbert-Huang Transform (HHT) provides a powerful generic algorithm for data analysis. It has the advantages of being able to handle short, nonstationary, and nonlinear data sets. We are applying this method to investigate the effects of disease and aging to neuroautonomic control of heart rate.