Skip to main content
SpringerLink
Log in

Time Frequency Analysis for Automated Sleep Stage Identification in Fullterm and Preterm Neonates

  • Original Paper
  • Published:
Journal of Medical Systems Aims and scope Submit manuscript

Abstract

This work presents a new methodology for automated sleep stage identification in neonates based on the time frequency distribution of single electroencephalogram (EEG) recording and artificial neural networks (ANN). Wigner–Ville distribution (WVD), Hilbert–Hough spectrum (HHS) and continuous wavelet transform (CWT) time frequency distributions were used to represent the EEG signal from which features were extracted using time frequency entropy. The classification of features was done using feed forward back-propagation ANN. The system was trained and tested using data taken from neonates of post-conceptual age of 40 weeks for both preterm (14 recordings) and fullterm (15 recordings). The identification of sleep stages was successfully implemented and the classification based on the WVD outperformed the approaches based on CWT and HHS. The accuracy and kappa coefficient were found to be 0.84 and 0.65 respectively for the fullterm neonates’ recordings and 0.74 and 0.50 respectively for preterm neonates’ recordings.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Scher, M., and Loparo, K., Neonatal EEG/sleep state analysis: a complex phenotype of developmental neural plasticity. Dev. Neurosci. 31:259–279, 2009.

    Article  Google Scholar 

  2. Heraghty, J., Hilliard, T., Henderson, A., and Fleming, P., The physiology of sleep in infants. Arch. Dis. Child. 93:982–985, 2008.

    Article  Google Scholar 

  3. Villa, M., Calcagnini, G., Pagani, J., Paggi, B., Massa, F., and Ronchetti, R., Effects of sleep stage and age on the short term heart rate variability during sleep in health infants and children. Chest. 117:460–466, 2000.

    Article  Google Scholar 

  4. Bertelle, V., Sevestre, A., Laou-Hap, K., Nagahapitiye, M., and Sizun, J., Sleep in the neonatal intensive care unit. J. Perinat. Neonatal. Nurs. 21 (2)140–148, 2007.

    Google Scholar 

  5. Gerla, V., Bursa, M., Lhotska, L., Paul, K., and Krajca, V., Newborn sleep stage classification using hybrid evolutionary approach. Int. J. Bioelectromagn. 9 (1)25–26, 2007.

    Google Scholar 

  6. Gerla, V., Lhotska, L., Krajca, V., and Paul, K., Multichannel analysis of the newborn EEG data, IEEE, ITAB, International Special Topics Conference on Information Technology in Biomedicine, 2006.

  7. Piryatinska, A., Terdik, G., Woyczynski, W., Loparo, K., Scher., M., and Zlotnik, A., Automated detection of neonatal EEG sleep stages. Comput. Methods Programs Biomed. 95:51–46, 2009.

    Article  Google Scholar 

  8. Penzel, T., Hirshkowitz, M., Harsh, J., Chervin, R., Butkov, N., Kryger, M., Malow, B., Vitiello, M., Silber, M., Kushida, C., and Chesson, A., Digital analysis and technical specifications. Journal of Clinical Sleep Medicine. 3 (2)109–120, 2007.

    Google Scholar 

  9. Fraiwan, L., Khaswaneh, N., and Lweesy, K., Automatic sleep stage scoring with wavelet packets based on single EEG recording. Proceedings World Academy of Science, Engineering and Technology, Paris. 54:385–488, 2009.

    Google Scholar 

  10. Tagluk, M., Sezgin, N., and Akin M., Estimation of sleep stages by an artificial neural networks employing EEG, EMG and EOG. Journal of Medical Systems 2009, in press.

  11. Fell, J., Röschke, J., Mann, K., and Schäffner, C., Discrimination of sleep stages: a comparison between spectral and nonlinear EEG measures. Electroencephalogr. Clin. Neurophysiol. 98 (5)401–410, 1996.

    Article  Google Scholar 

  12. Shimada, T., Shiina, T., and Saito, Y., Sleep stage diagnosis system with neural network analysis. Engineering in Medicine and Biology Society. 4 (29)2074–2047, 1998.

    Google Scholar 

  13. Agarwal, R., and Gotman, J., Computer-assisted sleep staging. IEEE Trans. Biomed. Eng. 48 (12)1412–1423, 2001.

    Article  Google Scholar 

  14. Pinero, P., Garcia, P., Arco, L., Alvarezc, A., Garc, M., and Bonal, R., Sleep stage classification using fuzzy sets and machine learning techniques. Neurocomputing. 58:1137–1143, 2004.

    Article  Google Scholar 

  15. Robert, C., Guilpin, C., and Limoge, A., Review of neural network applications in sleep research. J. Neurosci. Methods. 79:187–193, 1998.

    Article  Google Scholar 

  16. Orfanidis, J., Introduction to signal processing. Prentice-Hall, Englewood Cliffs, 1996.

    Google Scholar 

  17. Muthuswamy, J., and Thakor, N., Spectral analysis method for neurological signals. J. Neurosci. Methods. 83:1–14, 1998.

    Article  Google Scholar 

  18. Semmlow, J., Biosignal and biomedical image processing. Marcel Decker, New York, USA, 2004.

    Book  Google Scholar 

  19. Faust, O., Acharya, R., Krishnan, S., and Min, L., Analysis of cardiac signals using spatial filling index and time frequency domain. Biomedical Engineering Online. 3:30, 2004.

    Article  Google Scholar 

  20. Walnut, D., Wavelet analysis. Birkhauser; 2002. ISBN-0-8176-3962-4

  21. Wei-Yen, H., Chou-Ching, L., Min-Shaung, J., and Yung-Nein, S., Wavelet-based fractal features with active segment selection: application to single-trial EEG data. J. Neurosci. Methods. 163:145–160, 2007.

    Article  Google Scholar 

  22. Bang-hua, Y., Guo-zheng, Y., Ting, W., and Rong-guo, Y., Subject-based feature extraction using fuzzy wavelet packet in brain-computer interfaces. Signal Process. 87:1569–1574, 2007.

    Article  MATH  Google Scholar 

  23. Huang, N., Shen, Z., Long, S., Wu, M., Shih, H., and Zheng, Q., The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc. R. Soc. Lond. A. 454:903–995, 1998.

    Article  MathSciNet  MATH  Google Scholar 

  24. Xiaoli, L., Duan, L., Zhenhu, L., Logan, J., and Sleigh, J., Analysis of depth of anesthesia with Hilbert–Hough spectral entropy. Clin. Neurophysiol. 119:2465–2475, 2008.

    Article  Google Scholar 

  25. Djie, Y., Junsheng, C., and Yu, Y., Application of the EMD method and Hilbert spectrum for fault gear diagnosis of roller bearings. Mech. Syst. Signal Process. 19:259–270, 2005.

    Article  Google Scholar 

  26. Shigemura, S., Nishimura, T., Tsubai, M., and Yokoi, H., An investigation of EEG artifacts elimination using neural network with non-recursive 2nd order volterra filters. Conf. Proc. IEEE Eng. Med. Biol. Soc. 1:612–615, 2004.

    Google Scholar 

  27. Sreenivas, T., Unnikrishnan, V., and Narayana, D., Pruned neural networks reduction in EEG signal. Conf. Proc. IEEE Eng. Med. Biol. Soc. 13 (3)1411–1412, 1991.

    Google Scholar 

  28. Kiymik, M., 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.

    Article  Google Scholar 

  29. Sim, J., and Wrigth, C., The Kappa statistics in reliability studies: use, interpretation, and sample size requirements. Phys. Ther. 85 (3)257–268, 2005.

    Google Scholar 

  30. Cohen, J., A coefficient of agreement for nominal scales. Education and Psychological Measurements. 20:37–46, 1960.

    Article  Google Scholar 

Download references

Acknowledgment

This work was supported by the German Research Foundation-DFG (Deutsche Forschungsgemeinschaft). The authors would like to thank Dr. Alexandra Piryatinska.

Author information

Authors and Affiliations

  1. Biomedical Engineering Department, Jordan University of Science & Technology, Irbid, Jordan

    Luay Fraiwan & Khaldon Lweesy

  2. Computer Engineering Department, Jordan University of Science & Technology, Irbid, Jordan

    Natheer Khasawneh & Mohammad Fraiwan

  3. Thoracic Clinic, University of Heidelberg, Heidelberg, Germany

    Heinrich Wenz

  4. Medical informatics Department, University of Heidelberg, Heidelberg, Germany

    Hartmut Dickhaus

Authors
  1. Luay Fraiwan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Khaldon Lweesy
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Natheer Khasawneh
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mohammad Fraiwan
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Heinrich Wenz
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Hartmut Dickhaus
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Luay Fraiwan.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Fraiwan, L., Lweesy, K., Khasawneh, N. et al. Time Frequency Analysis for Automated Sleep Stage Identification in Fullterm and Preterm Neonates. J Med Syst 35, 693–702 (2011). https://doi.org/10.1007/s10916-009-9406-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10916-009-9406-2

Keywords

Search

Navigation