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.

该副本是利用众多研究子项目之一 由NIH/NCRR资助的中心赠款提供的资源。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。列出的机构是 对于中心，这不一定是调查员的机构。 时间频和时间分析已被广泛用于生物医学信号处理。 从历史上看，傅立叶光谱分析已用于此目的，但仅在极限条件下具有至关重要的限制。 本文介绍了新的信号处理工具的使用，即经验模式分解（EMD）和Hilbert-Huang Transform（HHT）。 这是分析非固定和非线性信号的替代方法，并且基于以下假设：任何信号都由不同的简单内在模式振荡组成。 这里考虑的应用程序是使用EMD和HHT对神经元信号进行分析，用于识别和分解中枢神经系统中的节奏。 神经系统的节奏与重要的行为和认知状态有关，包括注意力，记忆，对象识别，感觉运动整合，感知和语言处理。 从几只大鼠的大脑皮层收集实验数据；一群人在utero中暴露了香烟烟，而另一组则没有。 这些记录是针对听觉刺激产生的事件相关电位。 算法的验证是通过将其应用于人为构造的信号来完成的。 初步分析表明，来自未暴露和暴露大鼠的信号确实显示出频率含量的差异。 可以从HHT提取瞬时频率信息，从而提供有关振荡/变化的信息。 也可以使用内在模式函数分析神经元振荡的时间结构。 在新的数据集以及与其他算法的比较上，正在沿着这些路线进行进一步的工作。