HILBERT HUANG TRANSFORM ANALYSIS OF COMPLEX BIOMEDICAL SIGNALS

复杂生物医学信号的希尔伯特黄变换分析

• 批准号: 7366516
• 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/7366516
• 关键词: HILBERT; HUANG; TRANSFORM; ANALYSIS; COMPLEX

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.

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。傅立叶光谱分析一直是检查全局振幅频率分布的标准方法。但是，在两个关键限制下，傅立叶变换是有效的：1）系统必须是线性的； 2）数据必须严格定期或静止。可用的“现实世界”数据通常具有有限的持续时间，非平稳性，并且来自经常是本质上非线性的系统。在这些条件下，傅立叶分析的使用有限。但是，由于缺乏合适的替代方案，仍使用傅立叶分析来处理此类数据。傅立叶分析的宽松应用以及线性和平稳性的假设可能会导致误导性结论。最近开发的Hilbert-Huang Transform（HHT）为数据分析提供了强大的通用算法。它具有能够处理简短，非组织和非线性数据集的优点。我们正在应用这种方法来研究疾病和衰老对心率的神经自主控制的影响。