Sleep Stages Clustering Using Time and Spectral Features of EEG Signals

An Unsupervised Approach
  • J. L. Rodríguez-SoteloEmail author
  • A. Osorio-Forero
  • A. Jiménez-Rodríguez
  • F. Restrepo-de-Mejía
  • D. H. Peluffo-Ordoñez
  • J. Serrano
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10337)


Sleep stage classification is a highly addressed issue in polysomnography; It is considered a tedious and time-consuming task if done manually by the specialist; therefore, from the engineering point of view, several methods have been proposed to perform an automatic sleep stage classification. In this paper an unsupervised approach to automatic sleep stage clustering of EEG signals is proposed which uses spectral features related to signal power, coherences, asymmetries, and Wavelet coefficients; the set of features is classified using a clustering algorithm that optimizes a cost function of minimum sum of squares. Accuracy and kappa coefficients are comparable to those of the current literature as well as individual stage classification results. Methods and results are discussed in the light of the current literature, as well as the utility of the groups of features to differentiate the states of sleep. Finally, clustering techniques are recommended for implementation in support systems for sleep stage scoring.


Wavelet Transform Sleep Stage Automatic Classification Absolute Power Gamma Rhythm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This study is part of the “classification of sleep stages polysomnographic recordings from feature selection methods and unsupervised clustering” coded 328-038 in 2013 at the Autonoma University of Manizales. The research groups involved are Neuroaprendizaje and Automática research groups. The translation process was in charge of the Translation Center of the Autónoma University of Manizales.


  1. 1.
    Aboalayon, K.A.I., Almuhammadi, W.S., Faezipour, M.: A comparison of different machine learning algorithms using single channel EEG signal for classifying human sleep stages. In: 2015 Long Island Systems, Applications and Technology, pp. 1–6 (2015).
  2. 2.
    Agarwal, R., Gotman, J.: Digital tools in polysomnography. J. Clin. Neurophys. Off. Publ. Am. Electroencephalogr. Soc. 19(2), 136–143 (2002). Google Scholar
  3. 3.
    Berry, R.B., Budhiraja, R., Gottlieb, D.J., Gozal, D., Iber, C., Kapur, V.K., Marcus, C.L., Mehra, R., Parthasarathy, S., Quan, S.F., Redline, S., Strohl, K.P., Ward, S.L.D., Tangredi, M.M.: Rules for scoring respiratory events in sleep: update of the 2007 AASM manual for the scoring of sleep and associated events. J. Clin. Sleep Med. 8(5), 597–619 (2012)Google Scholar
  4. 4.
    Brennan, R.L., Prediger, D.J.: Coefficient kappa: some uses, misuses, and alternatives. Educ. Psychol. Meas. 41(3), 687–699 (1981)CrossRefGoogle Scholar
  5. 5.
    Cantero, J.L., Atienza, M., Madsen, J.R., Stickgold, R.: Gamma EEG dynamics in neocortex and hippocampus during human wakefulness and sleep. NeuroImage 22(3), 1271–1280 (2004)CrossRefGoogle Scholar
  6. 6.
    Danker-Hopfe, H., Anderer, P., Zeitlhofer, J., Boeck, M., Dorn, H., Gruber, G., Heller, E., Loretz, E., Moser, D., Parapatics, S., Saletu, B., Schmidt, A., Dorffner, G.: Interrater reliability for sleep scoring according to the Rechtschaffen & Kales and the new AASM standard. J. Sleep Res. 18(1), 74–84 (2009)CrossRefGoogle Scholar
  7. 7.
    Delorme, A., Makeig, S.: EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J. Neurosci. Methods 134(1), 9–21 (2004)CrossRefGoogle Scholar
  8. 8.
    Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. Wiley, Hoboken (2000)zbMATHGoogle Scholar
  9. 9.
    Durka, P.J., Klekowicz, H., Blinowska, K.J., Szelenberger, W., Niemcewicz, S.: A simple system for detection of EEG artifacts in polysomnographic recordings. IEEE Trans. Biomed. Eng. 50(4), 526–528 (2003)CrossRefGoogle Scholar
  10. 10.
    Fraiwan, L., Lweesy, K., Khasawneh, N., Wenz, H., Dickhaus, H.: Automated sleep stage identification system based on time-frequency analysis of a single EEG channel and random forest classifier. Comput. Methods Programs Biomed. 108(1), 10–19 (2012)CrossRefGoogle Scholar
  11. 11.
    Gath, I., Geva, A.B.: Unsupervised optimal fuzzy clustering. IEEE Trans. Pattern Anal. Mach. Intell. 11(7), 773–780 (1989)CrossRefzbMATHGoogle Scholar
  12. 12.
    Gunes, S., Polat, K., Yosunkaya, Ş.: Efficient sleep stage recognition system based on EEG signal using k-means clustering based feature weighting. Expert Systems with Applications 37(12), 7922–7928 (2010)CrossRefGoogle Scholar
  13. 13.
    Hansen, P., Mladenović, N.: Variable neighborhood search: principles and applications. Eur. J. Oper. Res. 130(3), 449–467 (2001)MathSciNetCrossRefzbMATHGoogle Scholar
  14. 14.
    Harmon-Jones, E.: Unilateral right-hand contractions cause contralateral alpha power suppression and approach motivational affective experience. Psychophysiology 43(6), 598–603 (2006)CrossRefGoogle Scholar
  15. 15.
    Hassan, A.R., Bhuiyan, M.I.H.: Computer-aided sleep staging using complete ensemble empirical mode decomposition with adaptive noise and bootstrap aggregating. Biomed. Signal Process. Control 24, 1–10 (2016)CrossRefGoogle Scholar
  16. 16.
    Zhang, J., Wu, Y., Bai, J., Chen, F.: Automatic sleep stage classification based on sparse deep belief net and combination of multiple classifiers. Trans. Inst. Meas. Control 38, 435–451 (2015)CrossRefGoogle Scholar
  17. 17.
    Jain, K., Murty, M.N., Flynn, P.J.: Data clustering a review. ACM Comput. Surv. 31(3), 264–323 (1999). CrossRefGoogle Scholar
  18. 18.
    Kouchaki, S., Sanei, S., Arbon, E., Dijk, D.J.: Tensor based singular spectrum analysis for automatic scoring of sleep EEG. IEEE Trans. Neural Syst. Rehabil. Eng. 23(1), 1–9 (2015).$$n
  19. 19.
    Krakovská, A., Mezeiová, K.: Automatic sleep scoring: a search for an optimal combination of measures. Artif. Intell. Med. 53(1), 25–33 (2011)CrossRefGoogle Scholar
  20. 20.
    Lan, K.C., Chang, D.W., Kuo, C.E., Wei, M.Z., Li, Y.H., Shaw, F.Z., Liang, S.F.: Using off-the-shelf lossy compression for wireless home sleep staging. J. Neurosci. Methods 246, 142–152 (2015)CrossRefGoogle Scholar
  21. 21.
    Mallat, S.G.: A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans. Pattern Anal. Mach. Intell. 11(7), 674–693 (1989)CrossRefzbMATHGoogle Scholar
  22. 22.
    Oropesa, E., Cycon, H., Jobert, M.: Sleep stage classification using wavelet transform and neural network, International computer science institute (1999).
  23. 23.
    Pastor, J., Fernández-Lorente, J., Ortega, B., Galán, J.M.: Comparative analysis of the clinical history and polysomnography in sleep disorders. Diagnostic relevance of polysomnography. Revista De Neurologia 32(1), 22–29 (2001). Google Scholar
  24. 24.
    Peker, M.: A new approach for automatic sleep scoring: combining Taguchi based complex-valued neural network and complex wavelet transform. Comput. Methods Programs Biomed. 129, 203–216 (2016)CrossRefGoogle Scholar
  25. 25.
    Rechtschaffen, A., Kales, A.: A Manual of Standardised Terminology, Techniques, and Scoring System for Sleep stages of Human Subjects. UCLA Brain Information Service, Los Angelos (1968)Google Scholar
  26. 26.
    Robert, C., Guilpin, C., Limoge, A.: Review of neural network applications in sleep research. J. Neurosci. Methods 79(2), 187–193 (1998)CrossRefGoogle Scholar
  27. 27.
    Ronzhina, M., Janoušek, O., Kolárová, J., Nováková, M., Honzík, P., Provazník, I.: Sleep scoring using artificial neural networks. Sleep Med. Rev. 16(3), 251–263 (2012)CrossRefGoogle Scholar
  28. 28.
    Su, B.L., Luo, Y., Hong, C.Y., Nagurka, M.L., Yen, C.W.: Detecting slow wave sleep using a single EEG signal channel. J. Neurosci. Methods 243, 47–52 (2015)CrossRefGoogle Scholar
  29. 29.
    Wang, Y., Loparo, K.A., Kelly, M.R., Kaplan, R.F.: Evaluation of an automated single-channel sleep staging algorithm. Nat. Sci. Sleep 7, 101–111 (2015)Google Scholar
  30. 30.
    Weiss, B., Clemens, Z., Bódizs, R., Halász, P.: Comparison of fractal and power spectral EEG features: effects of topography and sleep stages. Brain Res. Bull. 84(6), 359–375 (2011)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • J. L. Rodríguez-Sotelo
    • 1
    Email author
  • A. Osorio-Forero
    • 2
  • A. Jiménez-Rodríguez
    • 3
  • F. Restrepo-de-Mejía
    • 1
  • D. H. Peluffo-Ordoñez
    • 4
  • J. Serrano
    • 5
  1. 1.Universidad Autónoma de ManizalesManizalesColombia
  2. 2.Universidad de los AndesBogotáColombia
  3. 3.University of FreiburgFreiburg im BreisgauGermany
  4. 4.Universidad Técnica del NorteIbarraEcuador
  5. 5.Universidad de YachaySan Miguel de Urcuqui CantonEcuador

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