A Statistical Framework for the Spectral Analysis of Electrophysiology

描述（由申请人提供）：大量研究使用多导睡眠描记术连续且无创地记录睡眠期间的电生理信号，目的是使用这些数据阐明睡眠影响健康和功能的生物学途径。多导睡眠信号，如脑电图和心率变异性测量复杂和动态的过程，其频域特性提供了有价值的和可解释的信息。缺乏易于处理的统计模型和方法来量化非平稳时间序列集合的频域特性与其他研究变量（如临床结果和实验条件）之间的关联，限制了通过分析多体描记数据可以准确解决的问题的范围。该研究项目的目标是开发一个基于随机半参数进化传递函数的框架，用于睡眠研究期间收集的电生理时间序列的频谱分析。这个框架的三个具体方面将进行探讨：（1）混合效应模型的时变谱模拟，这将允许半参数分析的进化功率谱和其他变量之间的关联，同时考虑相关信号之间的依赖关系。(2)区分非平稳时间序列的总体的程序，例如区分对治疗有积极反应的参与者和对治疗无积极反应的参与者。(3)一种时频典型相关分析，用于获得高维时间序列与由大量相关变量测量的过程之间关联的低维度量。对于框架的每一个方面，估计将开发，理论和经验属性将被建立，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：老年人睡眠的临床实验研究和绝经过渡期妇女睡眠的多文化流行病学研究。

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.