Adaptive Frequency Band Estimation and Analysis

自适应频带估计和分析

• 批准号: 10250557
• 负责人: Scott A Bruce
• 金额: --
• 依托单位: GEORGE MASON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2021-09-02
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10250557
• 关键词: Address; Behavior; Biological; Characteristics; Clinical; Computer software; Data; Data Analyses; Dimensions; Frequencies; Individual; Instruction; Investigation; Magnetic Resonance Imaging; Measures; Methods; Monte Carlo Method; Mydriasis; Pattern; Performance; Population; Procedures; Property; Research Personnel; Series; Signal Transduction; Time; Time Series Analysis; analytical tool; design; heart rate variability; interest; preservation; tool; user-friendly

The frequency-domain properties of many biomedical time series contain valuable information. These properties are characterized through its power s pectrum, which describes the contribution to the variability of a time series from waveforms oscillating at different frequencies. Practitioners seeking low dimensional summarymeasures of the power spectrum from a population often partition frequencies into bands and create collapsed measures of power within these bands. However, standard frequency bands have largely been developed through subjective inspection of time series data and may not provide adequate summary measures of the power spectrum for a given population of interest. This proposal seeks to establish a new framework for adaptive frequency band estimation and analysis for replicated time series, thus bridging an important gap between the analysis of spectral information from a single time series and the analysis of spectral information within a population. The four specific aims associated with the effort are: (1) to develop a frequency band estimation method for replicated, stationary signals that best preserves variability across replicates within a population, (2) to develop a local frequency band estimation method for replicated, nonstationary signals that best preserves time and replicate-varying behavior within a population, (3) to develop a frequency band estimation method for replicated, multivariate signals that best preserves the characteristics and interrelationships between individual components and (4) to develop a suite of user-friendly analytical tools across multiple software platforms. Monte Carlo simulation studies will be conducted to explore the empirical prope rties of the proposed methods and to compare their performances to the use of traditional frequency bands. The investigators will use these new methods to analyze a range ofbiological signals, including heart rate variability, pupil dilation, and MRI, from three existing studies to address a variety of biological and clinical questions. The impact in practical investigations is expected to be substantial, equipping practitioners with justified optimal tools for analyzing data collected from a broad spectrum of scientific and biomedical studies. RELEVANCE (See instructions): This proposal will design practical statistical procedures for identifying frequency band summary measures of biomedical time series data that optimally characterize oscillatory patterns for a population of interest. The investigators will use these new methods to analyze biological signals from three existing studies and provide practitioners with optimal tools for analyzing data from a broad spectrum of biomedical studies.

许多生物医学时间序列的频域特性包含着有价值的信息。这些 属性通过其功率谱来表征，功率谱描述了对可变性的贡献。 从不同频率的波形振荡的时间序列。寻求低维的从业者 发明内容来自群体的功率谱的测量通常将频率划分为频带 在这些乐队中创造了崩溃的力量。然而，标准频带具有 主要是通过对时间序列数据的主观检查来开发的，可能无法提供足够的 对于给定的感兴趣群体的功率谱的概括测量。这项建议旨在 建立了一个新的框架，用于对复制时间序列进行自适应频带估计和分析， 从而在分析来自单个时间序列的光谱信息和 对人口中光谱信息的分析。与努力相关的四个具体目标 （1）开发一种用于复制的平稳信号的频带估计方法， 保持种群内重复样本的变异性，（2）开发局部频带 最佳保持时间和复制变化的复制非平稳信号的估计方法 行为在人群中，（3）开发一种频带估计方法，用于复制， 最好地保留个体之间的特征和相互关系的多变量信号 组件和（4）开发一套跨多个软件平台的用户友好的分析工具。 蒙特卡罗模拟研究将进行探索的经验特性的建议 方法，并比较其性能的使用传统的频带。调查人员 将使用这些新方法来分析一系列生物信号，包括心率变异性，瞳孔， 扩张和MRI，从三个现有的研究，以解决各种生物和临床问题。的 在实际调查中的影响预计将是巨大的，装备从业人员的合理 最佳工具，用于分析从广泛的科学和生物医学研究中收集的数据。 相关性（参见说明）： 本建议将设计实用的统计程序，用于识别频带摘要 生物医学时间序列数据的措施，最佳表征振荡模式的人口 兴趣研究人员将使用这些新方法来分析来自三个现有的生物信号。 研究并为从业者提供最佳工具，用于分析来自广泛领域的数据， 生物医学研究