EEG AND ECG CHARACTERISTICS OF HUMAN SLEEP COMPOSITION TYPES

Amro Khasawneh, Sergio A. Alvarez, Carolina Ruiz, Shivin Misra, Majaz Moonis

Abstract

Unsupervised clustering of staged human polysomnographic recordings reveals a hierarchy of sleep composition types described primarily by sleep efficiency and slow wave sleep content. Associations are found between these sleep clusters and health-related variables including BMI, smoking habits, and heart disease, showing that sleep types correspond to objective and medically relevant groupings. The present work describes the sleep type hierarchy, and studies the EEG and ECG correlates of sleep composition type. It is found that measures of EEG variation such as δ, θ, and α spectral content, as well as average heart rate, and measures of heart rate variability, including the standard deviation of the sequence of RR intervals, and Hjörth activity and mobility of the ECG signal, differ significantly among sleep composition type clusters. EEG analysis is shown to allow approximate reconstruction of sleep type without the need for ECG data, while ECG alone is found to be insufficient for accurate sleep type classification.

References

  1. Benjamini, Y. and Hochberg, Y. (1995). 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.
  2. Bonnet, M. and Johnson, L. (1978). Relationship of arousal threshold to sleep stage distribution and subjective estimates of depth and quality of sleep. Sleep, 1(2):161- 168.
  3. Buysse, D., Reynoldsiii, C., Monk, T., Berman, S., and Kupfer, D. (1989). The Pittsburgh sleep quality index: A new instrument for psychiatric practice and research. Psychiatry Research, 28(2):193-213.
  4. Dekker, J. M., Crow, R. S., Folsom, A. R., Hannan, P. J., Liao, D., Swenne, C. A., and Schouten, E. G. (2000). 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.
  5. Dempster, A. P., Laird, N. M., and Rubin, D. B. (1977). Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, Series B, 39(1):1-38.
  6. Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., and Witten, I. H. (2009). The WEKA data mining software: an update. SIGKDD Explor. Newsl., 11(1):10-18.
  7. 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. American Academy of Sleep Medicine, Westchester, Illinois, USA.
  8. Kamen, P., Krum, H., and Tonkin, A. (1996). Poincaré plot of heart rate variability allows quantitative display of parasympathetic nervous activity in humans. Clin Sci (Lond), 91(2):201-8.
  9. Karlen, W., Mattiussi, C., and Floreano, D. (2009). Sleep and wake classification with ecg and respiratory effort signals. IEEE Transactions on Biomedical Circuits and Systems, 3(2):71-78.
  10. Keklund, G. and Akerstedt, T. (1997). Objective components of individual differences in subjective sleep quality. J Sleep Res., 6(4):217-220.
  11. Khasawneh, A., Alvarez, S. A., Ruiz, C., Misra, S., and Moonis, M. (2010). Discovery of sleep composition types using expectation-maximization. In Proc. 23rd IEEE International Symposium on Computer-Based Medical Systems (CBMS 2010), Perth, Australia.
  12. Kryger, M., Roth, T., and Dement, W. (2005). Principles and Practice of Sleep Medicine. Elsevier Saunders, Philadelphia, PA, USA, 4th edition.
  13. Lewicke, A., Sazonov, E., Corwin, M., and Schuckers, S. (2005). Reliable determination of sleep versus wake from heart rate variability using neural networks. In Neural Networks, 2005. IJCNN 7805. Proceedings. 2005 IEEE International Joint Conference on, volume 4.
  14. Limoges, E., Mottron, L., Bolduc, C., Berthiaume, C., and Godbout, R. (2005). Atypical sleep architecture and the autism phenotype. Brain, 128(5):1049-1061.
  15. Loomis, A., Harvey, E., and Hobart, G. (1937). Cerebral states during sleep, as studied by human brain potentials. Journal of Experimental Psychology, 21(2):127- 144.
  16. M Malik et al (1996). 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.
  17. Manning, C. D., Raghavan, P., and Schutze, H. (2008). Introduction to Information Retrieval. Cambridge University Press. Web publication at informationretrieval.org.
  18. Mietus, J., Peng, C.-K., I, I. H., Goldsmith, R., and Goldberger, A. (2002). The pNNx files: re-examining a widely used heart rate variability measure. Heart, 88(4):378-380.
  19. Moody, G. (1993). Spectral analysis of heart rate without resampling. Computers in Cardiology, 20:715-718.
  20. 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., and Dorffner, G. (2009). Sleep classification according to AASM and Rechtschaffen & Kales: Effects on sleep scoring parameters. Sleep, 32(2):139-149.
  21. Neal, R. and Hinton, G. E. (1998). A view of the EM algorithm that justifies incremental, sparse, and other variants. In Learning in Graphical Models, pages 355- 368. Kluwer Academic Publishers.
  22. Propper, R., Christman, S., and Olejarz, S. (2007). Homerecorded sleep architecture as a function of handedness II: Consistent right- versus consistent lefthanders. J Nerv Ment Dis., 195(8):689-692.
  23. Rao, M., Blackwell, T., Redline, S., Stefanick, M., AncoliIsrael, S., and Stone, K. (2009). Association between sleep architecture and measures of body composition. Sleep, 32(4):483-90.
  24. Rechtschaffen, A. and Kales, A., editors (1968). 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.
  25. Silber, M., Anconi-Israel, S., Bonnet, M., Chokroverty, S., Grigg-Damberger, M., Hirshkowitz, M., Kapen, S., Keenan, S., Kryger, M., Penzel, T., Pressman, M., and Iber, C. (2007). The visual scoring of sleep in adults. Journal of Clinical Sleep Medicine, 3(2):121-131.
  26. Smith, S., Dingwall, K., Jorgensen, G., and Douglas, J. (2006). 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.
  27. Strehl, A. (2002). Relationship-based Clustering and Cluster Ensembles for High-dimensional Data Mining. PhD thesis, The University of Texas at Austin.
  28. Sulekha, S., Thennarasu, K., Vedamurthachar, A., Raju, T., and Kutty, B. (2006). Evaluation of sleep architecture in practitioners of Sudarshan Kriya yoga and Vipassana meditation. Sleep and Biological Rhythms, 4(3):207-214.
  29. Vanoli, E., Adamson, P. B., Ba-Lin, Pinna, G. D., Lazzara, R., and Orr, W. C. (1995). Heart rate variability during specific sleep stages: A comparison of healthy subjects with patients after myocardial infarction. Circulation, 91(7):1918-1922.
  30. Zhang, L., Samet, J., Caffo, B., and Punjabi, N. (2006). Cigarette smoking and nocturnal sleep architecture. Am J Epidemiol., 164(6):529-537.
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Paper Citation


in Harvard Style

Khasawneh A., A. Alvarez S., Ruiz C., Misra S. and Moonis M. (2011). EEG AND ECG CHARACTERISTICS OF HUMAN SLEEP COMPOSITION TYPES . In Proceedings of the International Conference on Health Informatics - Volume 1: HEALTHINF, (BIOSTEC 2011) ISBN 978-989-8425-34-8, pages 97-106. DOI: 10.5220/0003173900970106


in Bibtex Style

@conference{healthinf11,
author={Amro Khasawneh and Sergio A. Alvarez and Carolina Ruiz and Shivin Misra and Majaz Moonis},
title={EEG AND ECG CHARACTERISTICS OF HUMAN SLEEP COMPOSITION TYPES},
booktitle={Proceedings of the International Conference on Health Informatics - Volume 1: HEALTHINF, (BIOSTEC 2011)},
year={2011},
pages={97-106},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003173900970106},
isbn={978-989-8425-34-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Health Informatics - Volume 1: HEALTHINF, (BIOSTEC 2011)
TI - EEG AND ECG CHARACTERISTICS OF HUMAN SLEEP COMPOSITION TYPES
SN - 978-989-8425-34-8
AU - Khasawneh A.
AU - A. Alvarez S.
AU - Ruiz C.
AU - Misra S.
AU - Moonis M.
PY - 2011
SP - 97
EP - 106
DO - 10.5220/0003173900970106