Automated Sleep Staging Using Detrended Fluctuation Analysis of Sleep EEG

  • Amr F. Farag
  • Shereen M. El-Metwally
  • Ahmed Abdel Aal Morsy
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 195)


An accurate sleep staging is crucial for the treatment of sleep disorders. Recently some studies demonstrated that the long range correlations of many physiological signals measured during sleep show some variations during the different sleep stages. In this study, detrended fluctuation analysis (DFA) is used to study the electroencephalogram (EEG) signal autocorrelation during different sleep stages. A classification of these stages is then made by introducing the calculated DFA power law exponents to a K-Nearest Neighbor classifier. Our study reveals that a 2-D feature space composed of the DFA power law exponents of both the filtered THETA and BETA brain waves resulted in a classification accuracy of 94.44%, 91.66% and 83.33% for the wake, non-rapid eye movement and rapid eye movement stages, respectively. We conclude that it might be possible to build an automated sleep assessment system based on DFA analysis of the sleep EEG signal.


Electroencephalogram (EEG) Detrended fluctuation analysis (DFA) sleep K-Nearest Neighbor (KNN) 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Carskadon, M.A., Dement, W.C.: Normal human sleep: An overview. In: Kryger, M.H., Roth, T., Dement, W.C. (eds.) Principles and Practice of Sleep Medicine, vol. 3. Saunders, Philadephia (2000)Google Scholar
  2. 2.
    Rechtschaffen, A., Kales, A.: A manual of standardized technology, techniques, and scoring system for sleep stages of human subjects. US Public Health Service. U.S. Govt. Printing Office, Washington, DC (1968)Google Scholar
  3. 3.
    Himanen, S.L., Hasan, J.: Limitations of Rechtscaffen and Kales. Sleep Med. Rev. 4(2), 149–167 (2000)CrossRefGoogle Scholar
  4. 4.
    Hasan, J.: Past and future of computer-assisted sleep analysis and drowsiness assessment. J. Clin. Neurophysiol. 13(4), 295–313 (1996)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Penzel, T., Conradt, R.: Computer based sleep recording and analysis. Sleep Med. Rev. 4(2), 131–148 (2000)CrossRefGoogle Scholar
  6. 6.
    Penzel, T., Stephan, K., Kubicki, S., Herrmann, W.M.: Integrated sleep analysis, with em-phasis on automatic methods. In: Degen, R., Rodin, E.A. (eds.) Epilepsy, Sleep and Sleep Deprivation, 2nd edn. Epilepsy Res. suppl. 2, pp. 177–200. Elsevier Science Publishers B.V. (1991)Google Scholar
  7. 7.
    Kemp, B.: A proposal for computer-based sleep/wake analysis. J. Sleep Res. 2, 179–185 (1993)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Hjorth, B.: EEG analysis based on time domain properties. Electroencephalogr. Clin. Neurophysiol. 29, 306–310 (1970)CrossRefGoogle Scholar
  9. 9.
    Rezek, I., Roberts, S.: Stochastic complexity measures for physiological signal analysis. IEEE Trans. Biomed. Eng. 45(9), 1186–1191 (1998)CrossRefGoogle Scholar
  10. 10.
    Jobert, M., Schulz, H., Jähnig, P., Tismer, C., Bes, F., Escola, H.: A computerized method for detecting episodes of wakefulness during sleep based on the alpha slow-wave index (ASI). Sleep 17(1), 37–46 (1994)Google Scholar
  11. 11.
    Dimpfel, W., Hofmann, H.C., Schober, F., Todorova, A.: Validation of an EEG-derived spectral frequency index (SFx) for continuous monitoring of sleep depth in humans. Eur. J. Med. Res. 3, 453–460 (1998)Google Scholar
  12. 12.
    Hammer, N., Todorova, A., Hofmann, H.C., Schober, F., Vonderheid-Guth, B., Dimpfel, W.: Description of healthy and disturbed sleep by means of the spectral frequency index (SFx)—a retrospective analysis. Eur. J. Med. Res. 6, 333–344 (2001)Google Scholar
  13. 13.
    Mourtazaev, M.S., Kemp, B., Zwinderman, A.H., Kamphuisen, H.A.C.: Age and gender affect different characteristics of slow waves in the sleep EEG. Sleep 18(7), 557–564 (1995)Google Scholar
  14. 14.
    Flexer, A., Gruber, G., Dorffner, G.: A reliable probabilistic sleep stager based on a single EEG signal. Artif. Intell. Med. 33(3), 199–207 (2005)CrossRefGoogle Scholar
  15. 15.
    Kaplan, A., Röschke, J., Darkhovsky, B., Fell, J.: Macrostructural EEG characterization based on nonparametric change point segmentation: application to sleep analysis. J. Neurosci. Meth. 106, 81–90 (2001)CrossRefGoogle Scholar
  16. 16.
    Gunes, S., Polat, K., Yosunkaya, S.: Efficient sleep stage recognition system based on EEG signal using k-means clustering based feature weighting. ELSEVIER, Expert Systems with Applications 37, 7922–7928 (2010)CrossRefGoogle Scholar
  17. 17.
    Jo, H.G., Park, J.Y., Lee, C.K., An, S.K., Yoo, S.K.: Genetic fuzzy classifier for sleep stage identification. ELSEVIER, Computers in Biology and Medicine 40, 629–634 (2010)CrossRefGoogle Scholar
  18. 18.
    Jiang, Z., Ning, Y., An, B., Li, A., Feng, H.: Detecting mental EEG properties using detrended fluctuation analysis. In: 27th Annual Conference on Engineering in Medicine and Biology, Shanghai, China (2005)Google Scholar
  19. 19.
    Peng, C.K., Buldyrev, S.V., Goldberger, A.L., Havlin, S., Sciortino, F., Simons, M.: Fractal landscape analysis of DNA walks. Physica A (1992)Google Scholar
  20. 20.
    Kantelhardt, J.W., Koscielny-Bunde, E., Rego, H.H.A., Havlin, S., Bunde, A.: Detecting long-range correlations with detrended fluctuation analysis. Physica A 295, 441–454 (2001)MATHCrossRefGoogle Scholar
  21. 21.
    Penzel, T., Kantelhardt, J.W., Grote, L., Bunde, A.: Comparison of detrended fluctuation analysis and spectral analysis for heart rate variability in sleep and sleep apnea. IEEE Trans. Biomed. Eng. 50(10), 1143–1151 (2003)CrossRefGoogle Scholar
  22. 22.
    Peng, C.K., Havlin, S., Stabley, H.E., Goldberg, A.L.: Quantification of scaling exponents and crossover exponents phenomena in non-stationary heartbeat time series. Chaos 5(1), 82–87 (1995)CrossRefGoogle Scholar
  23. 23.
    Kantelhardt, J.W., Penzel, T., Sven R., Becker, H., Havlin, S.: ArminBun Breathing during REM and non-REM sleep: correlated versus uncorrelated behavior. Physica A 319 (2003)Google Scholar
  24. 24.
    Alpaydın, E.: Introduction to machine learning, 2nd edn. MIT Press, Cambridge (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Amr F. Farag
    • 1
    • 2
  • Shereen M. El-Metwally
    • 1
  • Ahmed Abdel Aal Morsy
    • 1
  1. 1.Department of Systems and Biomedical EngineeringCairo UniversityGizaEgypt
  2. 2.Department of Biomedical EngineeringShorouk Higher institute of EngineeringEL-ShoroukEgypt

Personalised recommendations