A Statistical Framework for the Spectral Analysis of High-Dimensional Physiological Time Series Signals

高维生理时间序列信号频谱分析的统计框架

• 批准号: 10345875
• 负责人: Robert T Krafty
• 金额: $ 33.12万
• 依托单位: EMORY UNIVERSITY
• 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 2023-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10345875
• 关键词: Statistical; Framework; Spectral; Analysis; Dimensional

Abstract A wide range of researchers record physiological signals over time. These signals contain dynamic information about important biological processes, a deeper understanding of which is essential for advancing preventions, diagnoses and treatments of disease. The complex nature of physiological time series signals, which are inher- ently nonstationary and where biological interest often lies in oscillatory patterns, presents challenges for their analysis. These challenges are exacerbated in modern studies, where researchers often record a large number of signals simultaneously. Simultaneous analyses of such data that take into account cross-signal relations is essential to obtaining a comprehensive understanding of complex biological pathways. Researchers' ability to fully utilize the information contained in these data is currently hindered by a dearth of formal statistical methods for the spectral analysis of high-dimensional nonstationary time series under modern study designs. The broad goal of this research is to develop a framework of scalable methods for the adaptive spectral analysis of non- stationary high-dimensional time series. The framework will introduce a novel spectral domain factor structure to overcome the high-dimensionality of the data and will be formulated in a Bayesian framework that can flexibly adapt to the dynamics of the data. Specific aims will establish three aspects within this framework: (1) estimation and inference for a high-dimensional time-varying power spectrum, (2) analysis of associations between high- dimensional time-varying power spectra and biological covariates, and (3) using high-dimensional time-varying spectra to predict future events. For each aspect, we will formulate a novel model and explore its properties, create a sampling scheme for estimation and inference using advanced Monte Carlo techniques, develop user friendly software, and compare empirical performance to that of existing approaches in simulation and validation studies. The framework will be used to analyze data from three studies: an observational study of signals col- lected across systemic physiological systems in critical care patients, a study of nocturnal high-density EEG, and a study of physiological systems involved in regulating locomotion.

抽象的 许多研究人员随着时间的推移记录生理信号。这些信号包含动态信息 关于重要的生物过程，对其进行更深入的了解对于推进预防至关重要， 疾病的诊断和治疗。生理时间序列信号的复杂性，其固有的 完全不稳定并且生物学兴趣通常在于振荡模式，这给他们带来了挑战 分析。这些挑战在现代研究中更加严重，研究人员经常记录大量数据 信号同时进行。考虑到跨信号关系的此类数据的同步分析是 对于全面了解复杂的生物途径至关重要。研究人员的能力 目前，由于缺乏正式的统计方法，无法充分利用这些数据中包含的信息 用于现代研究设计下高维非平稳时间序列的谱分析。广义的 这项研究的目标是开发一个可扩展方法的框架，用于非自适应谱分析。 平稳的高维时间序列。该框架将引入一种新颖的谱域因子结构 为了克服数据的高维性，并将在贝叶斯框架中制定，该框架可以灵活地 适应数据的动态。具体目标将在此框架内建立三个方面：（1）估计 以及高维时变功率谱的推断，（2）高维之间的关联分析 维时变功率谱和生物协变量，以及（3）使用高维时变 谱来预测未来事件。对于每个方面，我们将制定一个新颖的模型并探索其特性， 使用先进的蒙特卡罗技术创建用于估计和推理的采样方案，开发用户 友好的软件，并将经验性能与现有模拟和验证方法的性能进行比较 研究。该框架将用于分析三项研究的数据：信号观测研究 对重症监护患者的全身生理系统进行夜间高密度脑电图研究，以及 对参与调节运动的生理系统的研究。

项目成果

Cardiac vagal control in response to acute stress during pregnancy: Associations with life stress and emotional support.

• DOI: 10.1111/psyp.13808
• 发表时间: 2021-06
• 期刊: Psychophysiology
• 影响因子: 3.7
• 作者: Tung I;Krafty RT;Delcourt ML;Melhem NM;Jennings JR;Keenan K;Hipwell AE
• 通讯作者: Hipwell AE

Interpretable principal component analysis for multilevel multivariate functional data.

• DOI: 10.1093/biostatistics/kxab018
• 发表时间: 2021-09
• 期刊: Biostatistics
• 影响因子: 2.1
• 作者: Jun Zhang;G. Siegle;Tao Sun;Wendy D’Andrea;R. Krafty

Greenspace redevelopment, pressure of displacement, and sleep quality among Black adults in Southwest Atlanta.

• DOI: 10.1038/s41370-021-00313-9
• 发表时间: 2021-05
• 期刊: Journal of exposure science & environmental epidemiology
• 影响因子: 4.5
• 作者: Williams PC;Krafty R;Alexander T;Davis Z;Gregory AV;Proby R;Troxel W;Coutts C
• 通讯作者: Coutts C

Trend Analyses of Users of a Syringe Exchange Program in Philadelphia, Pennsylvania: 1999-2014.

宾夕法尼亚州费城注射器交换计划用户趋势分析：1999-2014 年。

• DOI: 10.1007/s10461-016-1393-y
• 发表时间: 2016
• 期刊: AIDS and behavior
• 影响因子: 4.4
• 作者: Maurer,LaurieA;Bass,SarahBauerle;Ye,Du;Benitez,José;Mazzella,Silvana;Krafty,Robert
• 通讯作者: Krafty,Robert

Empirical Frequency Band Analysis of Nonstationary Time Series.

• DOI: 10.1080/01621459.2019.1671199
• 发表时间: 2020
• 期刊: Journal of the American Statistical Association
• 影响因子: 3.7
• 作者: Bruce SA;Tang CY;Hall MH;Krafty RT
• 通讯作者: Krafty RT