Abstract
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.
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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)
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)
Himanen, S.L., Hasan, J.: Limitations of Rechtscaffen and Kales. Sleep Med. Rev. 4(2), 149–167 (2000)
Hasan, J.: Past and future of computer-assisted sleep analysis and drowsiness assessment. J. Clin. Neurophysiol. 13(4), 295–313 (1996)
Penzel, T., Conradt, R.: Computer based sleep recording and analysis. Sleep Med. Rev. 4(2), 131–148 (2000)
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)
Kemp, B.: A proposal for computer-based sleep/wake analysis. J. Sleep Res. 2, 179–185 (1993)
Hjorth, B.: EEG analysis based on time domain properties. Electroencephalogr. Clin. Neurophysiol. 29, 306–310 (1970)
Rezek, I., Roberts, S.: Stochastic complexity measures for physiological signal analysis. IEEE Trans. Biomed. Eng. 45(9), 1186–1191 (1998)
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)
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)
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)
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)
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)
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)
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)
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)
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)
Peng, C.K., Buldyrev, S.V., Goldberger, A.L., Havlin, S., Sciortino, F., Simons, M.: Fractal landscape analysis of DNA walks. Physica A (1992)
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)
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)
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)
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)
Alpaydın, E.: Introduction to machine learning, 2nd edn. MIT Press, Cambridge (2008)
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Farag, A.F., El-Metwally, S.M., Morsy, A.A.A. (2013). Automated Sleep Staging Using Detrended Fluctuation Analysis of Sleep EEG. In: Balas, V., Fodor, J., Várkonyi-Kóczy, A., Dombi, J., Jain, L. (eds) Soft Computing Applications. Advances in Intelligent Systems and Computing, vol 195. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33941-7_44
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DOI: https://doi.org/10.1007/978-3-642-33941-7_44
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