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,
including tight bounds on the false discovery rate.
Planned work includes the construction of a terabyte-scale
database of anonymized polysomnographic recordings and
health histories and the development of techniques for
multiscale analysis of sleep data.
Publications (asterisks denote student co-authors)
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Laxminarayan*, P., Alvarez, S.A., Ruiz, C., Moonis, M.,
"Mining Statistically Significant Associations for Exploratory Analysis
of Human Sleep Data",
IEEE Transactions on Information Technology in Biomedicine,
vol 10, no 3 (July 2006), 440-450
-
Laxminarayan*, P., Ruiz, C., Alvarez, S.A., Moonis, M.,
"Mining Associations over Human Sleep Time Series",
Proc. 18th IEEE International Symposium on Computer-Based
Medical Systems (A. Tsymbal and P. Cunningham, eds.),
IEEE Computer Society Press, Dublin, Ireland, June 2005, 323-328
Project Personnel
Faculty
- Sergio A. Alvarez, Ph.D. (Boston College)
- Carolina Ruiz, Ph.D. (Worcester Polytechnic Institute)
- Majaz Moonis, M.D. (U. of Massachusetts Medical School and Day Kimball Hospital)
Current Students
Alumni
- Parameshvyas Laxminarayan, M.S. (currently a Ph.D. student at Boston University)