Similarity Grouping of Human Sleep Recordings Using EEG and ECG

  • Amro Khasawneh
  • Sergio A. Alvarez
  • Carolina Ruiz
  • Shivin Misra
  • Majaz Moonis
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 273)


Characterizing variations in sleep stage composition is important in the scientific study of sleep. We use clustering, a form of unsupervised machine learning, to seek naturally occurring types within a collection of records that describe the sleep stage composition of 244 all-night human sleep studies. The results uncover a hierarchy of sleep composition types differentiated primarily by sleep efficiency or total sleep time and by the relative proportion of slow-wave sleep. The potential significance of these sleep type clusters for sleep medicine is suggested by associations between sleep type and health-related variables such as body-mass index, smoking frequency, and heart disease. EEG and ECG features, including spectral power distribution and measures of heart-rate variability, differ significantly among sleep types. The EEG signal provides sufficient information for an approximate reconstruction of the sleep type clusters, while ECG alone is found to be insufficient.


Sleep architecture Hypnogram Clustering Machine learning 


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  1. 1.
    Aserinsky, E., Kleitman, N.: Regularly occurring periods of eye motility, and concomitant phenomena, during sleep. Science 118(3062), 273–274 (1953)CrossRefGoogle Scholar
  2. 2.
    Benjamini, Y., Hochberg, Y.: Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society, Series B 57(1), 289–300 (1995)MathSciNetzbMATHGoogle Scholar
  3. 3.
    Bonnet, M.H., Johnson, L.C.: Relationship of arousal threshold to sleep stage distribution and subjective estimates of depth and quality of sleep. Sleep 1(2), 161–168 (1978)Google Scholar
  4. 4.
    Buysse, D., Reynolds III, C., Monk, T., Berman, S., Kupfer, D.: The Pittsburgh sleep quality index: A new instrument for psychiatric practice and research. Psychiatry Research 28(2), 193–213 (1989)CrossRefGoogle Scholar
  5. 5.
    Dekker, J.M., Crow, R.S., Folsom, A.R., Hannan, P.J., Liao, D., Swenne, C.A., Schouten, E.G.: Low heart rate variability in a 2-minute rhythm strip predicts risk of coronary heart disease and mortality from several causes: The ARIC study. Circulation 102(11), 1239–1244 (2000)CrossRefGoogle Scholar
  6. 6.
    Dement, W., Kleitman, N.: The relation of eye movements during sleep to dream activity: An objective method for the study of dreaming. Journal of Experimental Psychology 53, 339–346 (1957)CrossRefGoogle Scholar
  7. 7.
    Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, Series B 39(1), 1–38 (1977)MathSciNetzbMATHGoogle Scholar
  8. 8.
    Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: an update. SIGKDD Explor. Newsl. 11(1), 10–18 (2009)CrossRefGoogle Scholar
  9. 9.
    Iber, C., Ancoli-Israel, S., Chesson, A.L., Quan, S.F.: The AASM Manual for the Scoring of Sleep and Associated Events: Rules, Terminology, and Technical Specifications. American Academy of Sleep Medicine, Westchester, Illinois, USA (2007)Google Scholar
  10. 10.
    Kamen, P.W., Krum, H., Tonkin, A.M.: Poincaré plot of heart rate variability allows quantitative display of parasympathetic nervous activity in humans. Clin. Sci. (Lond.) 91(2), 201–208 (1996)Google Scholar
  11. 11.
    Karlen, W., Mattiussi, C., Floreano, D.: Sleep and wake classification with ECG and respiratory effort signals. IEEE Transactions on Biomedical Circuits and Systems 3(2), 71–78 (2009)CrossRefGoogle Scholar
  12. 12.
    Keklund, G., Akerstedt, T.: Objective components of individual differences in subjective sleep quality. J. Sleep Res. 6(4), 217–220 (1997)CrossRefGoogle Scholar
  13. 13.
    Khasawneh, A., Alvarez, S.A., Ruiz, C., Misra, S., Moonis, M.: Discovery of sleep composition types using expectation-maximization. In: Proc. 23rd IEEE International Symposium on Computer-Based Medical Systems (CBMS 2010), Perth, Australia (October 2010)Google Scholar
  14. 14.
    Khasawneh, A., Alvarez, S.A., Ruiz, C., Misra, S., Moonis, M.: EEG and ECG characteristics of human sleep composition types. In: Traver, V., Fred, A., Filipe, J., Gamboa, H. (eds.) Proc. Fourth International Conference on Health Informatics (HEALTHINF 2011), in Conjunction with the Fourth International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2011), pp. 97–106. SciTePress (January 2011)Google Scholar
  15. 15.
    Kim, J.A., Park, Y.-G., Cho, K.-H., Hong, M.-H., Han, H.-C., Choi, Y.-S., Yoon, D.: Heart rate variability and obesity indices: Emphasis on the response to noise and standing. J. Am. Board Fam. Med. 18(2), 97–103 (2005)CrossRefGoogle Scholar
  16. 16.
    Kryger, M.H., Roth, T., Dement, W.C.: Principles and Practice of Sleep Medicine, 4th edn. Elsevier Saunders, Philadelphia (2005)Google Scholar
  17. 17.
    Lewicke, A.T., Sazonov, E.S., Corwin, M.J., Schuckers, S.A.C.: Reliable determination of sleep versus wake from heart rate variability using neural networks. In: Proceedings of the IEEE International Joint Conference on Neural Networks, IJCNN 2005, vol. 4 (2005)Google Scholar
  18. 18.
    Limoges, E., Mottron, L., Bolduc, C., Berthiaume, C., Godbout, R.: Atypical sleep architecture and the autism phenotype. Brain 128(5), 1049–1061 (2005)CrossRefGoogle Scholar
  19. 19.
    Loomis, A.L., Harvey, E.N., Hobart, G.A.: Cerebral states during sleep, as studied by human brain potentials. Journal of Experimental Psychology 21(2), 127–144 (1937)CrossRefGoogle Scholar
  20. 20.
    Malik, M., et al.: Heart rate variability: standards of measurement, physiological interpretation and clinical use. task force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. Circulation 93(5), 1043–1065 (1996)CrossRefGoogle Scholar
  21. 21.
    Manning, C.D., Raghavan, P., Schutze, H.: Introduction to Information Retrieval. Cambridge University Press (2008); Web publication at informationretrieval.orgGoogle Scholar
  22. 22.
    Mietus, J.E., Peng, C.-K., Henry, I., Goldsmith, R.L., Goldberger, A.L.: The pNNx files: re-examining a widely used heart rate variability measure. Heart 88(4), 378–380 (2002)CrossRefGoogle Scholar
  23. 23.
    Moody, G.B.: Spectral analysis of heart rate without resampling. Computers in Cardiology 20, 715–718 (1993)Google Scholar
  24. 24.
    Moser, D., Anderer, P., Gruber, G., Parapatics, S., Loretz, E., Boeck, M., Kloesch, G., Heller, E., Schmidt, A., Danker-Hopfe, H., Saletu, B., Zeitlhofer, J., Dorffner, G.: Sleep classification according to AASM and Rechtschaffen & Kales: Effects on sleep scoring parameters. Sleep 32(2), 139–149 (2009)Google Scholar
  25. 25.
    Neal, R., Hinton, G.E.: A view of the EM algorithm that justifies incremental, sparse, and other variants. In: Learning in Graphical Models, pp. 355–368. Kluwer Academic Publishers (1998)Google Scholar
  26. 26.
    World Health Organization. Obesity: preventing and managing the global epidemic. Report of a WHO Consultation. WHO Technical Report Series 894. World Health Organization (2000)Google Scholar
  27. 27.
    Propper, R.E., Christman, S.D., Olejarz, S.: Home-recorded sleep architecture as a function of handedness II: Consistent right- versus consistent left-handers. J. Nerv. Ment. Dis. 195(8), 689–692 (2007)CrossRefGoogle Scholar
  28. 28.
    Rao, M.N., Blackwell, T., Redline, S., Stefanick, M.L., Ancoli-Israel, S., Stone, K.L.: Association between sleep architecture and measures of body composition. Sleep 32(4), 483–490 (2009)Google Scholar
  29. 29.
    Rechtschaffen, A., Kales, A. (eds.): A Manual of Standardized Terminology, Techniques, and Scoring System for Sleep Stages of Human Subjects. US Department of Health, Education, and Welfare Public Health Service – NIH/NIND (1968)Google Scholar
  30. 30.
    Benca, R.M., Obermeyer, W.H., Thisted, R.A., Gillin, J.C.: Sleep and psychiatric disorders. a meta-analysis. Arch. Gen. Psychiatry 49(8), 651–668 (1992)CrossRefGoogle Scholar
  31. 31.
    Silber, M.H., Anconi-Israel, S., Bonnet, M.H., Chokroverty, S., Grigg-Damberger, M.M., Hirshkowitz, M., Kapen, S., Keenan, S.A., Kryger, M.H., Penzel, T., Pressman, M.R., Iber, C.: The visual scoring of sleep in adults. Journal of Clinical Sleep Medicine 3(2), 121–131 (2007)Google Scholar
  32. 32.
    Smith, S.S., Dingwall, K., Jorgensen, G., Douglas, J.: Associations between the use of common medications and sleep architecture in patients with untreated obstructive sleep apnea. Journal of Clinical Sleep Medicine 2(2), 156–162 (2006)Google Scholar
  33. 33.
    Strehl, A.: Relationship-based Clustering and Cluster Ensembles for High-dimensional Data Mining. PhD thesis, The University of Texas at Austin (May 2002)Google Scholar
  34. 34.
    Sulekha, S., Thennarasu, K., Vedamurthachar, A., Raju, T.R., Kutty, B.M.: Evaluation of sleep architecture in practitioners of Sudarshan Kriya yoga and Vipassana meditation. Sleep and Biological Rhythms 4(3), 207–214 (2006)CrossRefGoogle Scholar
  35. 35.
    Vanoli, E., Adamson, P.B., Ba-Lin, Pinna, G.D., Lazzara, R., Orr, W.C.: Heart rate variability during specific sleep stages: A comparison of healthy subjects with patients after myocardial infarction. Circulation 91(7), 1918–1922 (1995)CrossRefGoogle Scholar
  36. 36.
    Zhang, L., Samet, J., Caffo, B., Punjabi, N.M.: Cigarette smoking and nocturnal sleep architecture. Am. J. Epidemiol. 164(6), 529–537 (2006)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Amro Khasawneh
    • 1
  • Sergio A. Alvarez
    • 2
  • Carolina Ruiz
    • 1
  • Shivin Misra
    • 1
  • Majaz Moonis
    • 3
  1. 1.Department of Computer ScienceWorcester Polytechnic InstituteWorcesterU.S.A.
  2. 2.Department of Computer ScienceBoston CollegeChestnut HillU.S.A.
  3. 3.Department of NeurologyUniversity of Massachusetts Medical SchoolWorcesterU.S.A.

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