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，功率谱描述了对变异性的贡献 以不同频率振荡的波形的时间序列。追求低维度的从业者 对总体功率谱的总结测量通常将频率划分为频带， 在这些范围内创建崩溃的权力衡量标准。然而，标准频段有 很大程度上是通过对时间序列数据的主观检查而开发的，可能无法提供足够的信息 给定感兴趣群体的功率谱的汇总测量。该提案旨在 建立一个用于复制时间序列的自适应频带估计和分析的新框架， 从而弥合了单个时间序列的光谱信息分析和 对群体内的光谱信息进行分析。与努力相关的四个具体目标 是： (1) 开发一种用于复制、平稳信号的频带估计方法，该方法最适合 保留群体内重复的变异性，(2) 开发局部频带 用于复制、非平稳信号的估计方法，最能保留时间和复制变化 群体内的行为，（3）开发重复的频带估计方法， 最好地保留个体之间的特征和相互关系的多元信号 (4) 开发一套跨多个软件平台的用户友好的分析工具。 将进行蒙特卡罗模拟研究来探索所提出的经验特性 方法并将其性能与使用传统频段进行比较。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、瞳孔变化 扩张和 MRI，来自三项现有研究，旨在解决各种生物学和临床问题。这 预计对实际调查的影响将是巨大的，为从业人员提供合理的依据 用于分析从广泛的科学和生物医学研究中收集的数据的最佳工具。 相关性（参见说明）： 该提案将设计实用的统计程序来识别频段摘要 生物医学时间序列数据的测量，可以最佳地表征群体的振荡模式 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分析各种数据的最佳工具 生物医学研究。