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Article
Nonparametric Signal Extraction and Measurement Error in the Analysis of Electroencephalographic Activity During Sleep
Journal of the American Statistical Association (2009)
  • Ciprian M Crainiceanu, Johns Hopkins University
  • Brian S Caffo, Johns Hopkins University
  • Chong-Zhi Di, Fred Hutchinson Cancer Research Center
  • Naresh M Punjabi, Johns Hopkins University
Abstract

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.

Keywords
  • Hierarchical smoothing; Penalized splines; Sleep,
  • measurement error.
Publication Date
2009
Publisher Statement
The full paper is available at the following link: http://pubs.amstat.org/doi/abs/10.1198/jasa.2009.0020?prevSearch=authorsfield%253A%2528Di%252C%2BChong%255C-Zhi%2529&searchHistoryKey=
Citation Information
Ciprian M Crainiceanu, Brian S Caffo, Chong-Zhi Di and Naresh M Punjabi. "Nonparametric Signal Extraction and Measurement Error in the Analysis of Electroencephalographic Activity During Sleep" Journal of the American Statistical Association Vol. 104 Iss. 486 (2009)
Available at: http://works.bepress.com/di/6/