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

@article{Crainiceanu2009NonparametricSE,
  title={Nonparametric Signal Extraction and Measurement Error in the Analysis of Electroencephalographic Activity During Sleep.},
  author={Ciprian M. Crainiceanu and B. S. Caffo and Chong-Zhi Di and Naresh M. Punjabi},
  journal={Journal of the American Statistical Association},
  year={2009},
  volume={104 486},
  pages={541-555}
}
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… CONTINUE READING