Skip to main content
SpringerLink
Log in

AI-based approach to automatic sleep classification

  • Published:
Biological Cybernetics Aims and scope Submit manuscript

Abstract

The primary goal of this paper is to introduce the potential of artificial intelligence (AI) methods to researchers in sleep classification. AI provides learning procedures for the construction of a sleep classifier, prescribing how to combine the observed parameters and how to derive the corresponding decision thresholds. A case study reporting a successful application of an automatic induction of decision trees and of a learning vector quantizer to this domain is presented.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Da Rosa AC, Kemp B, Paiva T, Lopes da Silva FH, Kamphuisen HAC (1991) A model-based detector of vertex waves and K complexes in sleep encephalogram. Electroencephalogr Clin Neurophysiol 78:71–79

    Google Scholar 

  • Flotzinger D (1991) Neural network-based classification of spatiotemporal EEG data. Technical Report 324, IIG-Report Series. MSc thesis Technical University of Graz, November 1991

  • Gaillard JK, Krassoievitch M, Tissot R (1972) Analyse automatique du sommeil par un systeme hybride: nouveaux résultats. Electroencephalogr Clin Neurophysiol 33:403–410

    Google Scholar 

  • Guilleminault C, Souquet M (1979) Sleep states and related pathology. In: Korobkin R, Guileminault C (eds) Advances in perinatal neurology, S. P. Medical and Scientific Books, New York, pp 225–247

    Google Scholar 

  • Jobert M, Scheuler W, Röske E, Poiseau E, Kubicki S (1991) Verfahren zur Mustererkennung in der Schlaf-Polygraphie. EEG-EMG 22:178–186

    Google Scholar 

  • Kemp B, Gröneveld EW, Janssen AJMW, Franzen JM (1987) A model-based monitor of human sleep stages. Biol Cybern 57:365–378

    Google Scholar 

  • Kohonen T (1990) The self-organizing map. Proc IEEE 78:1464–1480

    Google Scholar 

  • Kubat M, Flotzinger D, Pfurtscheller G (1993) Towards automated sleep classification in infants using symbolic and subsymbolic approaches. Biomed Tech (Berlin) 38 4:73–80

    Google Scholar 

  • Kubicki S, Scheuler W, Jobert M, Pastelak-Price C (1989) Der Einfluß des Alters auf die Schlafspindel- und K-Komplex-Dichte, EEG-EMG 20:59–63

    Google Scholar 

  • Lacroix B, Hanus R (1984) On-line automatic sleep scoring system involving Bayesian filtering. Measurement 2:156–160

    Google Scholar 

  • Litscher G, Pfurtscheller G (1991) Analysis of sleep patterns in babies using neural networks — preliminary results. In: Adlassnig K-P, Grabner G, Bengtsson S, Hansen R (eds) Lecture notes in medical informatics — Medical informatics Europe. Springer, Berlin Heidelberg, New York, pp 1014–1021

    Google Scholar 

  • Niblett T (1987) Constructing decision trees in noisy domains. In: Bratko I, Lavrač N (eds) Progress in machine learning. Sigma, Wilmslow

    Google Scholar 

  • Niedermeyer E, Lopes da Silva FH (1987) Electroencephalography — basic principles, clinical applications and related fields, 2nd edn, Urban & Schwarzenberg, Baltimore

    Google Scholar 

  • Pfurtscheller G, Flotzinger D, Kubat M (1992a) Sleep classification with a combination of symbolic learning and learning vector quantization. Proceedings of the 14th International Conference of the IEEE Engineering in Medicine and Biology Society, Vol 6, Paris, Oct. 29–Nov. 1

  • Pfurtscheller G, Flotzinger D, Matuschik K (1992b) Sleep classification in infants based on artificial neural networks. Biomed Tech (Berlin) 37:122–130

    Google Scholar 

  • Quinlan JP (1986) Induction of decision trees. Machine Learning 1:81–106

    Google Scholar 

  • Rechtschaffen A, Kales A (1968) A manual of standardized terminology, techniques, and scoring system for sleep stages of human subjects. US Government Printing Office, Washington, DC

    Google Scholar 

  • Smith JR, Karacan I (1971) EEG sleep stage scoring by an automatic hybrid system. Electroencephalogr Clin Neurophysiol 31:231–237

    Google Scholar 

  • Stanus EL (1985) Analyse automatisée des tracés polygraphiques de sommeil. Rev HF 13:3–11

    Google Scholar 

  • Stanus EL, Lacroix B, Kerkhofs M, Mendlewicz J (1987) Automated sleep scoring: a comparactive reliability study of two algorithms. Electroencephalogr Clin Neurophysiol 66:448–456

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Ludwig-Boltzmann Institute of Medical Informatics and Neuroinformatics, Graz University of Technology, Brockmanngasse 41, A-8010, Graz, Austria

    Miroslav Kubat & Gert Pfurtscheller

  2. Department of Medical Informatics, Institute of Biomedical Engineering, Graz University of Technology, Brockmanngasse 41, A-8010, Graz, Austria

    Doris Flotzinger

Authors
  1. Miroslav Kubat
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Gert Pfurtscheller
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Doris Flotzinger
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

About this article

Cite this article

Kubat, M., Pfurtscheller, G. & Flotzinger, D. AI-based approach to automatic sleep classification. Biol. Cybern. 70, 443–448 (1994). https://doi.org/10.1007/BF00203237

Download citation

  • Received:

  • Accepted:

  • Issue Date:

  • DOI: https://doi.org/10.1007/BF00203237

Keywords

Search

Navigation