The study introduces a novel nonparametric smoothing method to address measurement error in EEG signal extraction for large-scale sleep studies like the SHHS.
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May refine EEG phenotyping in large sleep cohorts; leaves open validation for clinical risk stratification.
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.
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Crainiceanu et al. (2009) studied this question.
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