USE OF EMPIRICAL MODE DECOMPOSITION AND HILBERT-HUANG TRANSFORM IN THE ANALYSIS

经验模态分解和Hilbert-Huang变换在分析中的应用

• 批准号: 7610017
• 负责人: SHIVAN HARAN
• 金额: $ 1.81万
• 依托单位: UNIV OF ARKANSAS FOR MED SCIS
• 依托单位国家: 美国
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-05-01 至 2008-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7610017
• 关键词: Algorithms; Attention; Behavioral; Cerebral cortex; Cognitive; Computer Retrieval of Information on Scientific Projects Database; Condition; Data; Data Set; Event-Related Potentials; Frequencies; Funding; Grant; Institution; Link; Memory; Motor; Nervous system structure; Neuraxis; Neurons; Paper; Perception; Purpose; Rattus; Research; Research Personnel; Resources; Sensory; Signal Transduction; Source; Structure; Time; United States National Institutes of Health; Validation; Work; auditory stimulus; base; cigarette smoking; computerized data processing; in utero; language processing; object recognition; response; tool

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. Time-frequency and temporal analyses have been widely used in biomedical signal processing. Historically, Fourier spectral analysis has been used for this purpose, but is valid only under extremely general conditions with some crucial restrictions. This paper presents the use of a new signal processing tools, namely the Empirical Mode Decomposition (EMD) and the Hilbert-Huang transform (HHT). This is an alternative approach to the analysis of non stationary and non linear signals, and is based on the assumption that any signal consists of different simple intrinsic mode oscillations. The application considered here is the analysis of neuronal signals using EMD and HHT, to be used to identify and decompose the rhythms in the central nervous system. Rhythms of the nervous system have been linked to important behavioral and cognitive states, including attention, memory, object recognition, sensory motor integration, perception, and language processing. Experimental data were collected from the cerebral cortex of several rats; one group had been exposed to the cigarette smoke in-utero, while the other group had not. The recordings were of event-related potentials produced in response to auditory stimulus. Validation of the algorithm was done by applying it to artificially constructed signals. Preliminary analyses indicate that the signals from unexposed and exposed rats do show differences in the frequency content. Instantaneous frequency information may be extracted from the HHT, providing information on the oscillations/changes. Temporal structure of the neuronal oscillations may also be analyzed using the intrinsic mode functions. Further work is being pursued along these lines on new sets of data, as well as comparison with other algorithms.

这个子项目是许多研究子项目中利用 资源由NIH/NCRR资助的中心拨款提供。子项目和 调查员(PI)可能从NIH的另一个来源获得了主要资金， 并因此可以在其他清晰的条目中表示。列出的机构是 该中心不一定是调查人员的机构。 时频分析和时间分析在生物医学信号处理中有着广泛的应用。在历史上，傅里叶频谱分析曾被用于这一目的，但只有在具有一些关键限制的极端一般条件下才有效。本文介绍了一种新的信号处理工具，即经验模式分解(EMD)和希尔伯特-黄变换(HHT)。这是分析非平稳和非线性信号的另一种方法，它基于任何信号都由不同的简单固有模式振荡组成的假设。这里考虑的应用是使用EMD和HHT来分析神经元信号，用于识别和分解中枢神经系统的节律。神经系统的节律与重要的行为和认知状态有关，包括注意力、记忆、物体识别、感觉运动整合、感知和语言处理。实验数据来自几只大鼠的大脑皮层；一组在宫内接触过香烟烟雾，另一组没有。这些记录是对听觉刺激做出反应而产生的事件相关电位。通过将其应用于人工构造的信号来验证算法的有效性。初步分析表明，未暴露和暴露的大鼠的信号在频率成分上确实存在差异。可以从HHT中提取瞬时频率信息，以提供关于振荡/变化的信息。也可以使用本征模式函数来分析神经元振荡的时间结构。目前正在沿着这些思路进一步研究新的数据集，并与其他算法进行比较。