@article {804,
	title = {Normalization and extraction of interpretable metrics from raw accelerometry data.},
	journal = {Biostatistics},
	volume = {15},
	year = {2014},
	month = {2014 Jan},
	pages = {102-16},
	abstract = {<p>We introduce an explicit set of metrics for human activity based on high-density acceleration recordings from a hip-worn tri-axial accelerometer. These metrics are based on two concepts: (i) Time Active, a measure of the length of time when activity is distinguishable from rest and (ii) AI, a measure of the relative amplitude of activity relative to rest. All measurements are normalized (have the same interpretation across subjects and days), easy to explain and implement, and reproducible across platforms and software implementations. Metrics were validated by visual inspection of results and quantitative in-lab replication studies, and by an association study with health outcomes.</p>},
	keywords = {Acceleration, Aged, Baltimore, Cohort Studies, Data Interpretation, Statistical, Female, Humans, Male, Motor Activity},
	issn = {1468-4357},
	doi = {10.1093/biostatistics/kxt029},
	author = {Bai, Jiawei and He, Bing and Shou, Haochang and Zipunnikov, Vadim and Glass, Thomas A and Crainiceanu, Ciprian M}
}
@article {810,
	title = {Combining hidden Markov models for comparing the dynamics of multiple sleep electroencephalograms.},
	journal = {Stat Med},
	volume = {32},
	year = {2013},
	month = {2013 Aug 30},
	pages = {3342-56},
	abstract = {<p>In this manuscript, we consider methods for the analysis of populations of electroencephalogram signals during sleep for the study of sleep disorders using hidden Markov models (HMMs). Notably, we propose an easily implemented method for simultaneously modeling multiple time series that involve large amounts of data. We apply these methods to study sleep-disordered breathing (SDB) in the Sleep Heart Health Study (SHHS), a landmark study of SDB and cardiovascular consequences. We use the entire, longitudinally collected, SHHS cohort to develop HMM population parameters, which we then apply to obtain subject-specific Markovian predictions. From these predictions, we create several indices of interest, such as transition frequencies between latent states. Our HMM analysis of electroencephalogram signals uncovers interesting findings regarding differences in brain activity during sleep between those with and without SDB. These findings include stability of the percent time spent in HMM latent states across matched diseased and non-diseased groups and differences in the rate of transitioning.</p>},
	keywords = {Data Interpretation, Statistical, Electroencephalography, Female, Humans, Longitudinal Studies, Male, Markov Chains, Middle Aged, Models, Statistical, Sleep Apnea Syndromes},
	issn = {1097-0258},
	doi = {10.1002/sim.5747},
	author = {Langrock, Roland and Swihart, Bruce J and Caffo, Brian S and Punjabi, Naresh M and Crainiceanu, Ciprian M}
}
