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.

Public Health Relevance

This project develops new statistical techniques to asses how physiological activity during sleep is connected to health and functioning and to better understand causes, effects, and treatments of poor sleep.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Research Project (R01)
Project #
1R01GM113243-01
Application #
8825702
Study Section
Special Emphasis Panel (ZGM1-BBCB-5 (BM))
Program Officer
Wehrle, Janna P
Project Start
2014-08-10
Project End
2017-05-31
Budget Start
2014-08-10
Budget End
2015-05-31
Support Year
1
Fiscal Year
2014
Total Cost
$283,367
Indirect Cost
$54,193
Name
Temple University
Department
Biostatistics & Other Math Sci
Type
Other Domestic Higher Education
DUNS #
057123192
City
Philadelphia
State
PA
Country
United States
Zip Code
19122