A Statistical Framework for the Spectral Analysis of Electrophysiology

电生理学频谱分析的统计框架

• 批准号: 8825702
• 负责人: Robert T Krafty
• 金额: $ 28.34万
• 依托单位: TEMPLE UNIV OF THE COMMONWEALTH
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-08-10 至 2017-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8825702
• 关键词: Accounting; Address; Affect; Algorithms; Asses; Autonomic nervous system; Behavior Therapy; Biological; Classification; Clinical; Collection; Complex; Computer software; Data; Data Analyses; Data Set; Dependency; Discriminant Analysis; Discrimination; Elderly; Electroencephalography; Electrophysiology (science); Epidemiologic Studies; Evaluation; Fourier Transform; Frequencies; Goals; Health; Measures; Menopause; Methods; Modeling; Monte Carlo Method; Outcome; Participant; Pathway interactions; Patient Self-Report; Physiological; Polysomnography; Population; Procedures; Process; Process Measure; Property; Relative (related person); Research Design; Research Project Grants; Sampling; Series; Signal Transduction; Simulate; Sleep; Sleep Stages; Sleeplessness; Statistical Methods; Statistical Models; Techniques; Testing; Theoretical model; Time; Validation; Weight; Woman; analog; base; clinical effect; computer program; diaries; heart rate variability; public health relevance; research study; response; theories; treatment effect; two-dimensional

DESCRIPTION (provided by applicant): A large number of studies use polysomnography to continuously and non-invasively record electrophysiological signals during sleep with the goal of using these data to elucidate the biological pathways through which sleep affects health and functioning. Polysomnographic signals such as electroencenphograms and heart rate variability measure complex and dynamic processes whose frequency domain properties provided valuable and interpretable information. A dearth of tractable statistical models and methods for quantifying associations between frequency domain properties of collections of nonstationary time series with other study variables, such as clinical outcomes and experimental conditions, has limited the scope of questions that can be accurately addressed by analyzing polysomographic data. The goal of this research project is to develop a framework based on stochastic semiparametric evolutionary transfer functions for the spectral analysis of electrophysiological time series collected during sleep studies. Three specific aspects of this framework will be explored: (1) A time-varying spectral analogue of mixed effects models that will allow for the semiparametric analysis of the association between evolutionary power spectra and other variables while accounting for dependencies among correlated signals. (2) A procedure for discriminating between populations of nonstationary time series, such as between participants that respond positively to a treatment from those who do not. (3) A time-frequency canonical correlation analysis for obtaining low-dimensional measures of association between high-dimensional time series and processes measured by large collections of correlated variables. For each aspect of the framework, estimators will be developed, theoretical and empirical properties will be established, and efficient algorithms and computer programs will be created. These new methods will be used to analyze data from two existing studies: a clinical experimental study of sleep in older adults and a multi-cultural epidemiological study of sleep in women during the menopausal transition.

描述（由申请人提供）：大量研究使用多种研究在睡眠期间连续和非侵入性记录电生理信号，目的是使用这些数据来阐明睡眠会影响健康和功能的生物学途径。多磁图和心率变异性等多磁图诸如频域特性的复杂和动态过程的过程提供了有价值且可解释的信息。缺乏量化非组织时间序列集合的频域性能与其他研究变量（例如临床结果和实验条件）之间关联的可易待统计模型和方法，限制了可以通过分析综合学数据来准确解决的问题的范围。该研究项目的目的是基于在睡眠研究期间收集的电生理时间序列的光谱分析基于随机半参数转移函数开发框架。将探讨该框架的三个特定方面：（1）混合效应模型的时变光谱模拟，该模型将允许对进化功率谱和其他变量之间关联的半磁头分析，同时考虑相关信号之间的依赖关系。 （2）一种歧视非组织时间序列人群的程序，例如，参与者对不这样做的治疗做出积极反应。 （3）用于获得高维时间序列和通过大量相关变量测量的高维度序列之间的相关性低维度的时间频率相关分析。对于框架的每个方面，将开发估计器，将建立理论和经验属性，并将创建有效的算法和计算机程序。这些新方法将用于分析两项现有研究的数据：对老年人睡眠的临床实验研究，以及对绝经期间女性睡眠的多元文化流行病学研究。