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

• Published in: Journal of the American Statistical Association Vol. 104; no. 486; pp. 541 - 555
• Main Authors: Crainiceanu, Ciprian M., Caffo, Brian S., Di, Chong-Zhi, Punjabi, Naresh M.
• Format: Journal Article
• Language: English
• Published: Alexandria, VA Taylor & Francis 01.06.2009; American Statistical Association; Taylor & Francis Ltd
• Subjects: Applications; Applications and Case Studies; Cross-sectional analysis; Data smoothing; Datasets; Error; Error analysis; Estimation; Estimation methods; Estimators; Exact sciences and technology; General topics; Hierarchical smoothing; Hierarchy; Human behaviour; Longitudinal studies; Mathematical minima; Mathematics; Measurement; Measurement errors; Medical sciences; Medical tests; Modeling; Nonparametric inference; Nonparametric models; Numerical analysis; Numerical analysis. Scientific computation; Penalized splines; Predisposing factors; Probability and statistics; Regression analysis; Sciences and techniques of general use; Signal noise; Signal processing; Sleep; Sleep, measurement error; Statistical methods; Statistical variance; Statistics; Biometrics; Smoothing methods; Error analysis; Error estimation; Signal estimation; Hierarchical smoothing; Non parametric estimation; Statistical estimation; Penalized splines; Implementation; Statistical method; Statistical regression; Medical science; Numerical analysis; Sleep; Smoothing; Cross sectional study; Signal processing; Application; Measurement error
• ISSN: 0162-1459; 1537-274X
• DOI: 10.1198/jasa.2009.0020

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.