An EOG-based Sleep Monitoring System and Its Application on On-line Sleep-stage Sensitive Light Control

Chih-En Kuo, Sheng-Fu Liang, Yi-Chieh Li, Fu-Yin Cherng, Wen-Chieh Lin, Peng-Yu Chen, Yen-Chen Liu, Fu-Zen Shaw


Human beings spend approximately one third of their lives sleeping. Conventionally, to evaluate a subjects sleep quality, all-night polysomnogram (PSG) readings are taken and scored by a well-trained expert. The development of an automatic sleep-staging system that does not rely upon mounting a bulky PSG or EEG recorder on the head will enable physiological computing systems (PhyCS) to progress toward easy sleep and comfortable monitoring. In this paper, an electrooculogram (EOG)-based sleep scoring system is proposed. Compared to PSG or EEG recordings, EOG has the advantage of easy placement, and can be operated by the user individually. The proposed method was found to be more than 83% accurate when compared with the manual scorings applied to sixteen subjects. In addition to sleep-quality evaluation, the proposed system encompasses adaptive brightness control of light according to online monitoring of the users sleep stages. The experiments show that the EOG-based sleep scoring system is a practicable solution for home-use sleep monitoring due to the advantages of comfortable recording and accurate sleep staging.


  1. Aliakseyeu, D., Du, J., Zwartkruis-Pelgrim, E., and Subramanian, S. (2011). Exploring interaction strategies in the context of sleep. In Human-Computer Interaction-INTERACT 2011, pages 19-36. Springer.
  2. Bauer, J., Consolvo, S., Greenstein, B., Schooler, J., Wu, E., Watson, N. F., Kientz, J., and Bauer, J. S. (2012). Shuteye: encouraging awareness of healthy sleep recommendations with a mobile, peripheral display. In Proceedings of the 2012 ACM annual conference on Human Factors in Computing Systems, pages 1401- 1410. ACM.
  3. Berthomier, C., Drouot, X., Herman-Stoïca, M., Berthomier, P., Prado, J., Bokar-Thire, D., Benoit, O., Mattout, J., and d'Ortho, M.-P. (2007). Automatic analysis of single-channel sleep eeg: validation in healthy individuals. Sleep, 30(11):1587.
  4. Bulling, A., Roggen, D., and Tröster, G. (2009). Wearable EOG goggles: eye-based interaction in everyday environments. ACM.
  5. Chandra, H., Oakley, I., and Silva, H. (2012). Designing to support prescribed home exercises: understanding the needs of physiotherapy patients. In Proceedings of the 7th Nordic Conference on Human-Computer Interaction: Making Sense Through Design, pages 607-616. ACM.
  6. Choe, E. K., Consolvo, S., Watson, N. F., and Kientz, J. A. (2011). Opportunities for computing technologies to support healthy sleep behaviors. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pages 3053-3062. ACM.
  7. Costa, M., Goldberger, A. L., and Peng, C.-K. (2005). Multiscale entropy analysis of biological signals. Physical Review E, 71(2):021906.
  8. Costa, M., Peng, C.-K., L Goldberger, A., and Hausdorff, J. M. (2003). Multiscale entropy analysis of human gait dynamics. Physica A: Statistical Mechanics and its applications, 330(1).
  9. Esteller, R., Echauz, J., Tcheng, T., Litt, B., and Pless, B. (2001). Line length: an efficient feature for seizure onset detection. In Engineering in Medicine and Biology Society, 2001. Proceedings of the 23rd Annual International Conference of the IEEE, volume 2, pages 1707-1710. IEEE.
  10. Fontana Gasio, P., Kräuchi, K., Cajochen, C., Someren, E. v., Amrhein, I., Pache, M., Savaskan, E., and Wirz-Justice, A. (2003). Dawn-dusk simulation light therapy of disturbed circadian rest-activity cycles in demented elderly. Experimental gerontology, 38(1):207-216.
  11. Fromm, E., Horlebein, C., Meergans, A., Niesner, M., and Randler, C. (2011). Evaluation of a dawn simulator in children and adolescents. Biological Rhythm Research, 42(5):417-425.
  12. Giménez, M. C., Hessels, M., van de Werken, M., de Vries, B., Beersma, D. G., and Gordijn, M. C. (2010). Effects of artificial dawn on subjective ratings of sleep inertia and dim light melatonin onset. Chronobiology International, 27(6):1219-1241.
  13. Guo, L., Rivero, D., Dorado, J., Rabunal, J. R., and Pazos, A. (2010). Automatic epileptic seizure detection in eegs based on line length feature and artificial neural networks. Journal of neuroscience methods, 191(1):101-109.
  14. Iber, C. (2007). The aasm manual for the scoring of sleep and associated events: rules, terminology and technical specifications.
  15. Kang, X., Jia, X., Geocadin, R. G., Thakor, N. V., and Maybhate, A. (2009). Multiscale entropy analysis of eeg for assessment of post-cardiac arrest neurological recovery under hypothermia in rats. Biomedical Engineering, IEEE Transactions on, 56(4):1023-1031.
  16. Kay, M., Choe, E. K., Shepherd, J., Greenstein, B., Watson, N., Consolvo, S., and Kientz, J. A. (2012). Lullaby: a capture & access system for understanding the sleep environment. In Proceedings of the 2012 ACM Conference on Ubiquitous Computing, pages 226-234. ACM.
  17. Kuo, B.-C. and Landgrebe, D. A. (2004). Nonparametric weighted feature extraction for classification. Geoscience and Remote Sensing, IEEE Transactions on, 42(5):1096-1105.
  18. Kupfer, D. J. and Reynolds, C. F. (1997). Management of insomnia. New England Journal of Medicine, 336(5):341-346.
  19. Lawson, S., Jamison-Powell, S., Garbett, A., Linehan, C., Kucharczyk, E., Verbaan, S., Rowland, D. A., and Morgan, K. (2013). Validating a mobile phone application for the everyday, unobtrusive, objective measurement of sleep. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pages 2497-2506. ACM.
  20. Liang, S.-F., Kuo, C.-E., Hu, Y.-H., Pan, Y.-H., and Wang, Y.-H. (2012). Automatic stage scoring of singleLin, C.-T., Ken-Li, L., Li-Wei, K., Sheng-Fu, L., Bor-Chen, K., et al. (2008). Nonparametric single-trial eeg feature extraction and classification of driver's cognitive responses. EURASIP Journal on Advances in Signal Processing, 2008.
  21. Manabe, H. and Fukumoto, M. (2006). Full-time wearable headphone-type gaze detector. In CHI'06 extended abstracts on Human factors in computing systems, pages 1073-1078. ACM.
  22. Norman, R. G., Pal, I., Stewart, C., Walsleben, J. A., and Rapoport, D. M. (2000). Interobserver agreement among sleep scorers from different centers in a large dataset. Sleep, 23(7):901-908.
  23. Norris, P. R., Anderson, S. M., Jenkins, J. M., Williams, A. E., and Morris Jr, J. A. (2008). Heart rate multiscale entropy at three hours predicts hospital mortality in 3,154 trauma patients. Shock, 30(1):17-22.
  24. Olbrich, E., Achermann, P., and Meier, P. (2003). Dynamics of human sleep eeg. Neurocomputing, 52:857-862.
  25. Pardey, J., Roberts, S., and Tarassenko, L. (1996). A review of parametric modelling techniques for eeg analysis. Medical engineering & physics, 18(1):2-11.
  26. Pincus, S. (1995). Approximate entropy (apen) as a complexity measure. Chaos: An Interdisciplinary Journal of Nonlinear Science, 5(1):110-117.
  27. Rechtschaffen, A. and Kales, A. (1968). A manual of standardized terminology, techniques and scoring system for sleep stages of human subjects.
  28. Richman, J. S. and Moorman, J. R. (2000). Physiological time-series analysis using approximate entropy and sample entropy. American Journal of PhysiologyHeart and Circulatory Physiology, 278(6):H2039- H2049.
  29. Rosenberg, R. S., Van Hout, S., et al. (2013). The american academy of sleep medicine inter-scorer reliability program: sleep stage scoring. Journal of clinical sleep medicine: JCSM: official publication of the American Academy of Sleep Medicine, 9(1):81-87.
  30. Schaltenbrand, N., Lengelle, R., Toussaint, M., Luthringer, R., Carelli, G., Jacqmin, A., Lainey, E., Muzet, A., Macher, J.-P., et al. (1996). Sleep stage scoring using the neural network model: comparison between visual and automatic analysis in normal subjects and patients. Sleep, 19(1):26.
  31. Silva, H., Palma, S., and Gamboa, H. (2011). Aal+: Continuous institutional and home care through wireless biosignal monitoring systems. In Handbook of Digital Homecare, pages 115-142. Springer.
  32. Stepanski, E. J. and Wyatt, J. K. (2003). Use of sleep hygiene in the treatment of insomnia. Sleep medicine reviews, 7(3):215-225.
  33. Takahashi, T., Cho, R. Y., Murata, T., Mizuno, T., Kikuchi, M., Mizukami, K., Kosaka, H., Takahashi, K., and Wada, Y. (2009). Age-related variation in eeg complexity to photic stimulation: A multiscale entropy analysis. Clinical Neurophysiology, 120(3):476-483.
  34. Thakor, N. V. and Tong, S. (2004). Advances in quantitative electroencephalogram analysis methods. Annu. Rev. Biomed. Eng., 6:453-495.
  35. Virkkala, J., Hasan, J., Värri, A., Himanen, S.-L., and Müller, K. (2007). Automatic sleep stage classification using two-channel electrooculography. Journal of neuroscience methods, 166(1):109-115.

Paper Citation

in Harvard Style

Kuo C., Liang S., Li Y., Cherng F., Lin W., Chen P., Liu Y. and Shaw F. (2014). An EOG-based Sleep Monitoring System and Its Application on On-line Sleep-stage Sensitive Light Control . In Proceedings of the International Conference on Physiological Computing Systems - Volume 1: PhyCS, ISBN 978-989-758-006-2, pages 20-30. DOI: 10.5220/0004725600200030

in Bibtex Style

author={Chih-En Kuo and Sheng-Fu Liang and Yi-Chieh Li and Fu-Yin Cherng and Wen-Chieh Lin and Peng-Yu Chen and Yen-Chen Liu and Fu-Zen Shaw},
title={An EOG-based Sleep Monitoring System and Its Application on On-line Sleep-stage Sensitive Light Control},
booktitle={Proceedings of the International Conference on Physiological Computing Systems - Volume 1: PhyCS,},

in EndNote Style

JO - Proceedings of the International Conference on Physiological Computing Systems - Volume 1: PhyCS,
TI - An EOG-based Sleep Monitoring System and Its Application on On-line Sleep-stage Sensitive Light Control
SN - 978-989-758-006-2
AU - Kuo C.
AU - Liang S.
AU - Li Y.
AU - Cherng F.
AU - Lin W.
AU - Chen P.
AU - Liu Y.
AU - Shaw F.
PY - 2014
SP - 20
EP - 30
DO - 10.5220/0004725600200030