Advertisement

Journal of Medical Systems

, Volume 34, Issue 4, pp 717–725 | Cite as

Estimation of Sleep Stages by an Artificial Neural Network Employing EEG, EMG and EOG

  • M. Emin Tagluk
  • Necmettin Sezgin
  • Mehmet Akin
Original Paper

Abstract

Analysis and classification of sleep stages is essential in sleep research. In this particular study, an alternative system which estimates sleep stages of human being through a multi-layer neural network (NN) that simultaneously employs EEG, EMG and EOG. The data were recorded through polisomnography device for 7 h for each subject. These collective variant data were first grouped by an expert physician and the software of polisomnography, and then used for training and testing the proposed Artificial Neural Network (ANN). A good scoring was attained through the trained ANN, so it may be put into use in clinics where lacks of specialist physicians.

Keywords

EEG EMG EOG Sleep stages ANN 

References

  1. 1.
    Penzel, T., and Conradt, R., Computer based sleep recording and analysis. Sleep Med. Rev. 4:131–148, 2000. doi: 10.1053/smrv.1999.0087.CrossRefGoogle Scholar
  2. 2.
    Carskadon, M. A., and Rechtschaffen, A., Monitoring and staging human sleep. In: Kryger, M. H., Roth, T., and Dement, W. C. (Eds.), Principles and Practice of Sleep Medicine, 4th edition. Saunders, Philadelphia, 2005.Google Scholar
  3. 3.
    Jones, B. E., Basic mechanisms of sleep–wake states. In: Kyger, M. H., Roth, T., and Dement, W. C. (Eds.), Principles and Practice of Sleep MedicineSaunders, Philadelphia, pp. 145–162, 1984.Google Scholar
  4. 4.
    Principe, J. C., Gala, S. K., and Chang, T. G., Sleep staging automation based on the theory of evidence. IEEE Trans. Biomed. Eng. 36 (5)503–509, 1989. doi: 10.1109/10.24251.CrossRefGoogle Scholar
  5. 5.
    Hellyar, M. T., Ifeachor, E. C., Mapps, D. J., Allen, E. M., and Hudson, N. R., Expert system approach to electroencephalogram signal processing. Knowl.-Based. Syst. 8 (4)164–173, 1995. doi: 10.1016/0950-7051(95)96213-B.CrossRefGoogle Scholar
  6. 6.
    Shimada, T. and Shiina, T., Detection of characteristic waves of sleep EEG by neural network analysis. In Proc. IEEE Int. Conf. Biomed. Eng., 1995, 823–824.Google Scholar
  7. 7.
    Shimada, T., Shiina, T., and Saito, Y., Sleep stage diagnosis system with neural network analysis. Proceedings of the 20th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Vol. 20, No. 4, 1998.Google Scholar
  8. 8.
    Guler, I., and Ubeyli, E. D., Multiclass support vector machines for EEG signals classification. IEEE Trans. Inf. Technol. Biomed. 11 (2)117–126, 2007. doi: 10.1109/TITB.2006.879600.CrossRefGoogle Scholar
  9. 9.
    Guler, I., and Ubeyli, E. D., Adaptive neuro-fuzzy inference system for classification of EEG signals using wavelet coefficients. J. Neurosci. Methods. 148:113–121, 2005. doi: 10.1016/j.jneumeth.2005.04.013.CrossRefGoogle Scholar
  10. 10.
    Akin, M., Kurt, M. B., Sezgin, N., and Bayram, M., Estimating vigilance level by using EEG and EMG signals. Neural Comput. Appl. 17 (3)227–236, 2008. doi: 10.1007/s00521-007-0117-7.CrossRefGoogle Scholar
  11. 11.
    Halici, U., Lecture notes on Neural Networks. The dynamic of brain multidisipliner, Diyarbakir, 1999.Google Scholar
  12. 12.
    Kiymik, M. K., Akin, M., and Subasi, A., Automatic recognition of alertness level by using wavelet transform and artificial neural network. J. Neurosci. Methods. 139 (2)231–240, 2004. doi: 10.1016/j.jneumeth.2004.04.027.CrossRefGoogle Scholar
  13. 13.
    Akin, M., Bayram, M., Eroglu, O., and Sezgin, N., Determining of dose level analysing EEG signals by using wavelet transform and neural networks. Int. J. Comput. Intell. IJCI Proceedings of International XII Turkish Symposium on Artificial Intelligence and Neural Networks, TAINN 2003, ISSN 1304–2386, Volume:1, Number:1, pages:302–305, July 2003.Google Scholar
  14. 14.
    Gelir, E. and Ardiç, S., Insan uyku evrelerinin standart terminoloji. Yontem ve Skorlama El Kitabi, 2000.Google Scholar
  15. 15.
    Horner R., PSL 472/1472 sleep physiology. Last updated 20.12.2001.Google Scholar
  16. 16.
    Hazarika N., Classification of EEG signals using the wavelet transform. Signal Processing [H. W. Wilson—AST], Vol. 59, ISS: 1, pg: 61., May 1997.Google Scholar
  17. 17.
    Yazgan, E. and Korurek, M., Tıp Elektroniği. Istanbul Technical University, publication no:1574, ISBN 975- 561-073-1, 1996.Google Scholar
  18. 18.
    Khahill, M., and Duchene, J., Detection and classification of multiple events in piecewise stationary signals. J. Signal Process. S0165-684 (98)00236–00239, 1999.Google Scholar
  19. 19.
    Jung, T. P., Makeig, S., Stensmo, M., and Sejnowski, T. J., Estimating alertness from the EEG power spectrum. IEEE Trans. Biomed. Eng. 44:60–69, 1997. doi: 10.1109/10.553713.CrossRefGoogle Scholar
  20. 20.
    Peters, B. O., Pfurtscheller, G., and Flyvbjerg, H., Automatic differentiation of multichannel EEG signals. IEEE Trans. Biomed. Eng. 48:111–116, 2001. doi: 10.1109/10.900270.CrossRefGoogle Scholar
  21. 21.
    Gevins, A., and Smith, M. E., Detecting transient cognitive impairment with EEG pattern recognition methods. Aviat. Space Environ. Med. 70 (10)1018–1024, 1999.Google Scholar
  22. 22.
    Basheer, I. A., and Hajmeer, M., Artificial neural networks: fundamentals, computing, design, and application. J. Microbiol. Methods. 43:3–31, 2000. doi: 10.1016/S0167-7012(00)00201-3.CrossRefGoogle Scholar
  23. 23.
    Haykin, S., Neural networks: a comprehensive foundation. Macmillan, New York, 1994.MATHGoogle Scholar
  24. 24.
    Guler, N. F., and Ubeyli, E. D., Wavelet-based neural network analysis of ophthalmic artery Doppler signals. Comput. Biol. Med. 34 (7)601–613, 2004. doi: 10.1016/j.compbiomed.2003.09.001.CrossRefGoogle Scholar
  25. 25.
    Eyfe, C., Artificial Neural Network. The University of Paisley, Edition 1.1, 1996.Google Scholar
  26. 26.
    Fausett, L., Fundamentals of Neural Networks architectures, algorithms, and applications. Prentice-Hall, Englewood Cliffs, 1994.MATHGoogle Scholar
  27. 27.
    Rechtschaffen, A., and Kales, A., A manual of standardized terminology, techniques and scoring system for sleep stage of human subjects. Public Health Service U. S. Government Printing Office, Washington, D.C., 1968.Google Scholar
  28. 28.
    Watanabe, T., and Watanabe, K., Noncontact method for sleep stage estimation. IEEE Trans. Biomed. Eng. 51 (10)1735–1748, 2004. doi: 10.1109/TBME.2004.828037.CrossRefGoogle Scholar
  29. 29.
    Himanen, S. L., and Hasan, J., Limitations of Rechtschaffen and Kales. Sleep Med. Rev. 4 (2)149–167, 2000. doi: 10.1053/smrv.1999.0086.CrossRefGoogle Scholar
  30. 30.
    Shimohira, M., et al., Video analysis of gross movements during sleep. Psychiatry Clin. Neurosci. 52 (2)176–177, 1998. doi: 10.1111/j.1440-1819.1998.tb01015.x.CrossRefGoogle Scholar
  31. 31.
    Salmi, T., and Leinonen, L., Automatic analysis of sleep records with static charge sensitive bed. Electroencephalogr. Clin. Neurophysiol. 64:84–87, 1986. doi: 10.1016/0013-4694(86)90047-7.CrossRefGoogle Scholar
  32. 32.
    Doi, S., Nagai, I., and Sakuma, T., A decision method for sleeping-states from body movement using neural network. Trans. Inst. Elect. Eng. Jpn. 114-C (11)1160–1165, 1994.Google Scholar
  33. 33.
    Hanaoka, M., Kobayashi, M., and Yamazaki, H., Automated sleep stage scoring by decision tree learning, 23rd Annual EMBS International Conference, 1751–1754, 2001.Google Scholar
  34. 34.
    Kurt, M. B., Sezgin, N., Akin, M., Kirbas, G., and Bayram, M., The ANN-based computing of drowsy level. Expert Syst. Appl. 36 (2)2534–2542, 2009. doi: 10.1016/j.eswa.2008.01.085.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • M. Emin Tagluk
    • 1
  • Necmettin Sezgin
    • 2
  • Mehmet Akin
    • 3
  1. 1.Department of Electrical and Electronics EngineeringUniversity of InonuMalatyaTurkey
  2. 2.Department of Electrical and Electronics EngineeringUniversity of BatmanBatmanTurkey
  3. 3.Department of Electrical and Electronics EngineeringUniversity of DicleDiyarbakirTurkey

Personalised recommendations