Nonparametric Signal Extraction and Measurement Error in the Analysis of Electroencephalographic Activity During Sleep.
We introduce methods for signal and associated variability estimation based on hierarchical nonparametric smoothing with application to the Sleep Heart Health Study (SHHS). SHHS is the largest electroencephalographic (EEG) collection of sleep-related data, which contains, at each visit, two quasi-continuous EEG signals for each subject. The signal features extracted from EEG data are then used in second level analyses to investigate the relation between health, behavioral, or biometric outcomes and sleep. Using subject specific signals estimated with known variability in a second level regression becomes a nonstandard measurement error problem. We propose and implement methods that take into account cross-sectional and longitudinal measurement error. The research presented here forms the basis for EEG signal processing for the SHHS.
Crainiceanu
Ciprian M
CM
Ciprian M. Crainiceanu is Assistant Professor, Department of Biostatistics, Johns Hopkins University, Baltimore, MD 21205 ( ccrainic@jhsph.edu ). Brian Caffo is Associate Professor, Department of Biostatistics, Johns Hopkins University, Baltimore, MD 21205 ( bcaffo@jhsph.edu ). Chongzhi Di is Ph.D. candidate, Department of Biostatistics, Johns Hopkins University, Baltimore, MD 21205 ( cdi@jhsph.edu ). Naresh M. Punjabi is Associate Professor, Department of Epidemiology, Johns Hopkins University, Bloomberg School of Public Health, Baltimore, MD 21205 ( npunbjabi@jhmi.edu ).
Caffo
Brian S
BS
Di
Chong-Zhi
CZ
Punjabi
Naresh M
NM