Discovery of Patterns in Sleep Data

This research project aims to develop machine learning techniques for automated discovery of meaningful patterns in human sleep data. Our work to date has yielded an association mining approach for exploratory analysis of sleep data (Laxminarayan et al, 2005), including tight bounds on the false discovery rate (Laxminarayan et al, 2006), construction of a terabyte-scale database of anonymized polysomnographic recordings and health histories, and discovery of naturally occurring subgroups of sleep studies, or "sleep types", based on the stage composition of sleep (Khasawneh et al, 2010 and 2011). Planned work includes modeling of the dynamics of sleep. An initial step in this direction is presented in (Usher et al, 2012).


Publications (asterisks denote student co-authors)


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