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Nonparametric Signal Extraction and Measurement Error in the Analysis of Electroencephalographic Activity During Sleep

Ciprian M. Crainiceanu, Brian S. Caffo, Chong-Zhi Di and Naresh M. Punjabi
Journal of the American Statistical Association
Vol. 104, No. 486 (June 2009), pp. 541-555
Stable URL: http://www.jstor.org/stable/40592204
Page Count: 15
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Nonparametric Signal Extraction and Measurement Error in the Analysis of Electroencephalographic Activity During Sleep
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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 biometrie 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.

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