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Determination of Sleep Stage Separation Ability of Features Extracted from EEG Signals Using Principle Component Analysis

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Abstract

In this study, a method was proposed in order to determine how well features extracted from the EEG signals for the purpose of sleep stage classification separate the sleep stages. The proposed method is based on the principle component analysis known also as the Karhunen–Loéve transform. Features frequently used in the sleep stage classification studies were divided into three main groups: (i) time-domain features, (ii) frequency-domain features, and (iii) hybrid features. That how well features in each group separate the sleep stages was determined by performing extensive simulations and it was seen that the results obtained are in agreement with those available in the literature. Considering the fact that sleep stage classification algorithms consist of two steps, namely feature extraction and classification, it will be possible to tell a priori whether the classification step will provide successful results or not without carrying out its realization thanks to the proposed method.

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References

  1. Sinha, R. K., Artificial neural network and wavelet based automated detection of sleep spindles, REM sleep and wake states. J. Med. Syst. 32:291–299, 2008. doi:10.1007/s10916-008-9134-z.

    Article  Google Scholar 

  2. Kim, M. S., Cho, Y. C., Berdakh, A., and Seo, H. D., Analysis of brain function and classification of sleep stage EEG using Daubechies wavelet. Sens. Mater. 20:1001–014, 2008.

    Google Scholar 

  3. Virkkala, J., Hasan, J., Värri, A., Himanen, S. L., and Müller, K., Automatic sleep stage classification using two-channel electro-oculography. J. Neurosci. Methods. 166:109–115, 2007. doi:10.1016/j.jneumeth.2007.06.016.

    Article  Google Scholar 

  4. Doroshenkov, L., Konyshev, V., and Selishchev, S., Classification of human sleep stages based on EEG processing using hidden Markov models. Biomed. Eng. (N.Y.). 41:125–28, 2007. doi:10.1007/s10527-007-0006-5.

    Google Scholar 

  5. Held, C. M., Heiss, J. E., Estévez, P. A., Perez, C. A., Garrido, M., Algarin, C., and Peirano, P., Extracting rules from polysomnographic recordings for infant sleep stage classification. IEEE Trans. Biomed. Eng. 53:101954–1962, 2006. doi:10.1109/TBME.2006.881798.

    Article  Google Scholar 

  6. Hanaoka, M., Kobayashi, M., and Yamazaki, H., Automatic sleep stage scoring based on waveform recognition method and decision-tree learning. Syst. Comput. Jpn. 33:111–13, 2002. doi:10.1002/scj.10248.

    Article  Google Scholar 

  7. Caffarel, J., Gibson, G. J., Harrison, J. P., Griffiths, C. J., and Drinnan, M. J., Comparison of manual sleep staging with automated neural network-based analysis in clinical practice. Med. Biol. Eng. Comput. 44:105–110, 2006. doi:10.1007/s11517-005-0002-4.

    Article  Google Scholar 

  8. Duda, R. O., Hart, P. E., and Stork, D. G., Pattern classification. Wiley, New York, 2001.

    MATH  Google Scholar 

  9. Rechtschaffen, and Kales, A., (ed.), A manual of standardized terminology, techniques and scoring system for sleep stages of human subjects, Brain Information Service/Brain Research Institute, 1968.

  10. Gonzales, R. C., and Woods, R. E., Digital image processing, 3rd edition. Pearson Prentice Hall, Englewood Cliffs, NJ, 2008.

    Google Scholar 

  11. Szeto, H. H., Spectral edge frequency as a simple quantitative measure of the maturation of electrocortical activity. Pediatr. Res. 27:3289–292, 1990. doi:10.1203/00006450-199003000-00018.

    Article  MathSciNet  Google Scholar 

  12. Kay, S. M., Modern spectral estimation: Theory and application. Prentice Hall, Englewood Cliffs, NJ, 1988 January.

    MATH  Google Scholar 

  13. International Database PhysioNet Sleep Recordings: http://www.physionet.org

  14. Agarwal, R., and Gotman, J., Computer-assisted sleep staging. IEEE Trans. Biomed. Eng. 48:121412–1423, 2001. doi:10.1109/10.966600.

    Article  Google Scholar 

  15. Van Hese, P., Philips, W., De Koninck, J., Van de Walle, R., and Lemahieu, I., Automatic detection of sleep stages using the EEG. Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, vol 2, pp. 944–1947, 2001.

  16. Kerkeni, N., Alexandre, F., Bedoui, M. H., Bougrain, L., and Dogui, M., Automatic classification of sleep stages on a EEG signal by artificial neural network, 5th WSEAS International Conference on SIGNAL, SPEECH and IMAGE PROCESSING—WSEAS SSIP’05, Corfu, Greece, 2005.

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Authors and Affiliations

  1. Department of Electrical and Electronics Engineering, Sakarya University, 54187, Adapazari, Turkey

    Cabir Vural & Murat Yildiz

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  1. Cabir Vural
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  2. Murat Yildiz
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Correspondence to Cabir Vural.

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Vural, C., Yildiz, M. Determination of Sleep Stage Separation Ability of Features Extracted from EEG Signals Using Principle Component Analysis. J Med Syst 34, 83–89 (2010). https://doi.org/10.1007/s10916-008-9218-9

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  • DOI: https://doi.org/10.1007/s10916-008-9218-9

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