![](bg8.png)
Systems and Signal Processing (BIOSIGNALS 2013),
pages 209–214, Barcelona, Spain. SciTePress.
Berndt, D. J. and Clifford, J. (1994). Using dynamic time
warping to find patterns in time series. In KDD Work-
shop, volume 10 (16), pages 359–370. Seattle, WA.
Bianchi, M. T., Cash, S. S., Mietus, J., Peng, C.-K.,
and Thomas, R. (2010). Obstructive sleep apnea al-
ters sleep stage transition dynamics. PLOS ONE,
5(6):e11356.
Chervin, R. D., Fetterolf, J. L., Ruzicka, D. L., Thelen, B. J.,
and Burns, J. W. (2009). Sleep stage dynamics differ
between children with and without obstructive sleep
apnea. Sleep, 32(10):13251332.
Chu-Shore, J., Westover, M. B., and Bianchi, M. T. (2010).
Power law versus exponential state transition dynam-
ics: application to sleep-wake architecture. PLOS
ONE, 5(12):e14204.
Hernandez, T. L., Ballard, R. D., Weil, K. M., Shep-
ard, T. Y., Scherzinger, A. L., Stamm, E. R., Sharp,
T. A., and Eckel, R. H. (2009). Effects of maintained
weight loss on sleep dynamics and neck morphology
in severely obese adults. Obesity, 17(1):84–91.
Hubert, L. and Arabie, P. (1985). Comparing par-
titions. Journal of Classification, 2:193–218.
10.1007/BF01908075.
Iber, C., Ancoli-Israel, S., Chesson, A., and Quan, S.
(2007). The AASM Manual for the Scoring of
Sleep and Associated Events: Rules, Terminology
and Technical Specifications. Westchester: American
Academy of Sleep Medicine.
Itakura (1975). Minimum prediction residual principle ap-
plied to speech recognition. IEEE Transactions on
Acoustics, Speech, and Signal Processing, 23(1):52–
72.
Kim, J., Lee, J. S., Robinson, P., and Jeong, D. U. (2009).
Markov analysis of sleep dynamics. Physical Review
Letters, 102(17):178104.
Kishi, A., Struzik, Z. R., Natelson, B. H., Togo, F., and
Yamamoto, Y. (2008). Dynamics of sleep stage tran-
sitions in healthy humans and patients with chronic
fatigue syndrome. American Journal of Physiology-
Regulatory, Integrative and Comparative Physiology,
294(6):R1980–R1987.
Oates, T., Firoiu, L., and Cohen, P. R. (1999). Clustering
time series with hidden Markov models and dynamic
time warping. In Proceedings of the IJCAI-99 Work-
shop on Neural, Symbolic and Reinforcement Learn-
ing Methods for Sequence Learning, pages 17–21.
Phillips, A. J., Robinson, P. A., Kedziora, D. J., and Abey-
suriya, R. G. (2010). Mammalian sleep dynamics:
how diverse features arise from a common physiolog-
ical framework. PLOS Computational Biology, 6(6).
Rand, W. M. (1971). Objective criteria for the evaluation of
clustering methods. Journal of the American Statisti-
cal Association, 66(336):846–850.
Rechtschaffen, A. and Kales, A. (1968). A manual of stan-
dardized terminology, techniques and scoring system
for sleep stages of human subjects. US Department of
Health, Education, and Welfare Public Health Service
- NIH/NIND.
Sakoe, H. and Chiba, S. (1978). Dynamic programming
algorithm optimization for spoken word recognition.
IEEE Transactions on Acoustics, Speech and Signal
Processing, 26(1):43–49.
Salvador, S. and Chan, P. (2007). Toward accurate dynamic
time warping in linear time and space. Intelligent Data
Analysis, 11(5):561–580.
Vinh, N. X., Epps, J., and Bailey, J. (2010). Informa-
tion theoretic measures for clusterings comparison:
Variants, properties, normalization and correction for
chance. The Journal of Machine Learning Research,
9999:2837–2854.
Wang, C., Alvarez, S. A., Ruiz, C., and Moonis, M.
(2013). Computational modeling of sleep stage dy-
namics using Weibull semi-Markov chains. In Proc.
Sixth International Conference on Health Informat-
ics (HEALTHINF 2013), pages 122–130, Barcelona,
Spain. SciTePress.
Yu, S.-Z. (2010). Hidden semi-Markov models. Artificial
Intelligence, 174(2):215 – 243. Special Review Issue.
Zung, W. W., Naylor, T. H., Gianturco, D. T., and Wilson,
W. P. (1965). Computer simulation of sleep EEG pat-
terns with a Markov chain model. Recent advances in
biological psychiatry, 8:335–355.
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