Modeling and Discovery of Patterns in Human Sleep
This research project aims to develop machine learning
techniques for modeling and automated discovery of meaningful patterns
in human sleep data.
In recent work in this direction, we developed a deep convolutional neural
network approach to sleep stage classification with interpretation of the
emergent internal features (Paisarnsrisomsuk et al, 2020, 2018;
Sokolovsky et al, 2019, 2018).
In prior work, we demonstrated
discovery of naturally occurring subgroups of sleep
studies, or "sleep types", based on the stage composition of sleep
(Khasawneh et al, 2010 and 2011).
Our earlier work 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), and
construction of a terabyte-scale database of anonymized
polysomnographic recordings and health histories.
Sleep Dynamics
Our work has also included modeling of the dynamics of sleep.
An initial step in this direction is presented in (Usher et al, 2012).
A major challenge to dynamical modeling of sleep is the scarcity
of dynamical stage-transition events within a single all-night sleep
recording for a given person.
The paper (Alvarez and Ruiz, 2013) presents a general E-M framework
for dynamical modeling of time series data (sleep in particular) that
contain limited dynamical information. Subsequent work (Wang et al, 2014)
applied this general approach to sleep using partially observable Markov
models as the class of dynamical models.
Selected publications (asterisks denote student co-authors)
-
S. Paisarnsrisomsuk*, C. Ruiz, and S. A. Alvarez .
Improved deep learning classification of human sleep stages,
IEEE Computer-Based Medical Systems (CBMS 2020),
Mayo Clinic, Rochester, MN, July 28-30, 2020
-
M. Sokolovsky*, F. Guerrero*, S. Paisarnsrisomsuk*, C. Ruiz, and S. A. Alvarez.
Deep learning for automated feature discovery and classification of sleep stages, IEEE / ACM Transactions on Computational Biology and Bioinformatics,
online early access, Apr. 2019
doi:10.1109/TCBB.2019.2912955
Near-final version available here.
-
S. Paisarnsrisomsuk*, M. Sokolovsky*, F. Guerrero*, C. Ruiz, and S. A. Alvarez.
Deep Sleep: convolutional neural networks for predictive modeling of human
sleep time-signals, KDD 2018 Deep Learning Day, 2018 ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2018), London, UK,
Aug. 2018
-
M. Sokolovsky*, F. Guerrero*, S. Paisarnsrisomsuk*, C. Ruiz, and S. A. Alvarez.
Human expert-level automated sleep stage prediction and feature discovery by
deep convolutional neural networks, full paper, BIOKDD 2018, in
conjunction with the 2018 ACM SIGKDD International Conference on
Knowledge Discovery and Data Mining (KDD 2018), London, UK, Aug. 2018
-
C. Wang*, S. A. Alvarez, C. Ruiz, and M. Moonis.
Semi-Markov modeling-clustering of human sleep with efficient
initialization and stopping, full paper,
Proc. Seventh International Conference on Bio-inspired Systems
and Signal Processing (BIOSIGNALS 2014), in conjunction with the Seventh
International Joint Conference on Biomedical Engineering Systems and
Technologies (BIOSTEC 2014), Angers, France, Mar. 3-6, 2014
-
S. A. Alvarez and C. Ruiz.
"Collective Probabilistic Dynamical Modeling of Sleep Stage Transitions",
short paper, Proc. Sixth International Conference on Bio-inspired Systems
and Signal Processing (BIOSIGNALS 2013), in conjunction with the Sixth
International Joint Conference on Biomedical Engineering Systems and
Technologies (BIOSTEC 2013), Barcelona, Catalunya, Spain, Feb. 11-14, 2013
-
A. Khasawneh*, S. A. Alvarez, C. Ruiz, S. Misra*, and M. Moonis.
"EEG and ECG Characteristics of Human Sleep Composition Types",
full paper (acceptance rate: 48/538 = 8.9%), Best Paper Award,
Proc. HEALTHINF 2011 (Fourth International Conference on Health
Informatics),
in conjunction with BIOSTEC 2011, Rome, Italy, Jan. 26-29, 2011, 97-106
-
A. Khasawneh*, S. A. Alvarez, C. Ruiz, S. Misra*, and M. Moonis.
"Discovery of Sleep Composition Types using Expectation-Maximization",
Proc. 23rd IEEE International Symposium on Computer-Based Medical Systems
(CBMS 2010), Perth, Australia, Oct. 12-15, 2010
-
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
Former group members
- Mike Sokolovsky, M.S.
- Francisco Guerrero, M.S.
- Chiying Wang, Ph.D.
- Amro Khasawneh
- Shivin Misra, M.S.
- Parameshvyas Laxminarayan, M.S.