Clustering of EEG-Segments Using Hierarchical Agglomerative Methods and Self-Organizing Maps

  • David Sommer
  • Martin Golz
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2130)

Abstract

EEG segments recorded during microsleep events were transformed to the frequency domain and were subsequently clustered without the common summation of power densities in spectral bands. Any knowledge about the number of clusters didn’t exist. The hierarchical agglomerative clustering procedures were terminated with several standard measures of intracluster and intercluster variances. The results were inconsistent. The winner histogram of Self-organizing maps showed also no evidence. The analysis of the U-matrix together with the watershed transform, a method from image processing, resulted in separable clusters. As in many other procedures the number of clusters was determined with one threshold parameter. The proposed method is working fully automatically.

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References

  1. [1]
    Thorpy, MJ; Yager, J; The Encyclopedia of Sleep and Sleep Disorders; NewYork: Facts on File, 1991.Google Scholar
  2. [2]
    Santamaria, J; Chiappa, KH; The EEG of Drowsiness in Normal Adults; J Clin Neurol; 4(4), 1987, 327–382.CrossRefGoogle Scholar
  3. [3]
    Liberson, WT; Liberson, CT; EEG recordings, reaction times, eye movements, respiration and mental content during drowsiness; Proc Soc Biol Psychiat, 19, 1966, 295–302.Google Scholar
  4. [4]
    SAS Institute Inc., SAS/STAT User’s Guide, Version 6, Fourth Edition, Volume 1, Cary, NC: SAS Institute Inc., 1989, 943 pp.Google Scholar
  5. [5]
    Milligan, GW; Cooper, MC; An examination of procedures for determining the number of clusters in a data set; Psychometrika, 50(2), 1985, 159–179.CrossRefGoogle Scholar
  6. [6]
    Deichsel, G; Trampisch, H; Clusteranalyse und Diskriminanzanalyse; Gustav Fisher Verlag, Stuttgart, 1985.Google Scholar
  7. [7]
    Backhaus, K; Erichson, B; Plinke, W; Weiber, R; Multivariate Analyseverfahren; (6. Aufl.), Berlin, Heidelberg, New York. Springer, 1996.Google Scholar
  8. [8]
    Kohonen, T; Self-organized formation of topologically correct feature maps; Biol Cybern, 43, 1982, 59–69.MATHCrossRefMathSciNetGoogle Scholar
  9. [9]
    Kohonen, T; Self-Organizing Maps; 3rd edition, Springer, Berlin, 2000.Google Scholar
  10. [10]
    Fritzke, B; Wachsende Zellstrukturen-ein selbstorganisierendes neuronales Netzwerkmodell; PhD thesis; University of Erlangen, 1992 (in german).Google Scholar
  11. [11]
    Villmann, T; Topologieerhaltung in selbstorganisierenden neuronalen Merkmalskarten; PhD thesis, University of Leipzig, 1996 (in german).Google Scholar
  12. [12]
    Ultsch, A; Siemon, HP; Exploratory Data Analysis: Using Kohonen Networks on Transputers; Univ. of Dortmund, Technical Report 329, Dortmund, Dec1989.Google Scholar
  13. [13]
    Costa, JAF; Netto, MLA; Estimating the Number of Clusters in Multivariate Data by Self-Organizing Maps; Int. J Neural Systems, 9(3), 1999, 195–202.CrossRefGoogle Scholar
  14. [14]
    Vincent, L; Soille, P; Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulation; IEEE Transaction on Pattern Analysis and Machine Intelligence, 1991.Google Scholar
  15. [15]
    Bauer, HU; Pawelzik, KR; Quantifying the neighborhood preservation of Self-Organizing Feature Maps. IEEE Trans. Neural Networks, 3(4), 1992, 570–579.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • David Sommer
    • 1
  • Martin Golz
    • 1
  1. 1.Department of Computer ScienceUniversity of Applied SciencesSchmalkaldenGermany

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