Communications - Scientific Letters of the University of Zilina 2011, 13(1):47-50 | DOI: 10.26552/com.C.2011.1.47-50

Automatic Creation of Hypnogram

Michal Gala1, Branko Babusiak1, Vilem Novak2
1 Department of Electromagnetic and Biomedical Engineering, Faculty of Electrical Engineering, University of Zilina, Slovakia
2 Children's neurology clinic, University Hospital Ostrava, Ostrava-Poruba, Czech Republic

Polysomnography is an important diagnostic method used in sleep monitoring of patients with various sleep disorders. The formulation of a precise diagnosis requires correct evaluation and interpretation of polysomnography EEG and this evaluation is provided by a doctor. In order to save time, various automatic evaluations methods have been developed. One of these methods is based on signal division into 30 second long sections which are subsequently analyzed and reduced using adaptive segmentation and parameters are computed from these segments. Cluster analysis is used to divide individual segments and the class with biggest quantity of segments is called "priority class". Definition of sleep states is then based on priority class. The end result is a hypnogram and the average success rate of the proposed method is about 68%.

Keywords: no keywords

Published: March 31, 2011  Show citation

ACS AIP APA ASA Harvard Chicago Chicago Notes IEEE ISO690 MLA NLM Turabian Vancouver
Gala, M., Babusiak, B., & Novak, V. (2011). Automatic Creation of Hypnogram. Communications - Scientific Letters of the University of Zilina, 13(1), 47-50. doi: 10.26552/com.C.2011.1.47-50
Share...
Download citation
  • BibTeX (.bib)
  • Bookends (.ris)
  • EasyBib (.ris)
  • EndNote (.enw)
  • EndNote 8 (.xml)
  • ISI WoS (.isi)
  • Medlars (.medlars)
  • Mendeley (.ris)
  • MODS (.xml)
  • Papers (.ris)
  • RefWorks (.txt)
  • RefManager (.ris)
  • RIS (.ris)
  • MS Word (.xml)
  • Zotero (.ris)
    • Open full article

    References

    1. IBER, C., ANCOLI, S., CHESSON, A: The AASM Manual of the Scoring of Sleep and Associated Events, American Academy of Sleep Medicine, Westchester, 2007.
    2. VONDRAK, I.: Artificial Intelligence and Neural Networks, FEI, VSB - TU : Ostrava, 2002.
    3. GALA M., MOHYLOVA, J.: Detection of K-Complex in the EEG Signal, www.springerlink.com:, Springer Berlin Heidelberg, 2010. Ed. O. Dossel, W C. Schlegel, Vol. 25, ISSN 1680-0737 (Print) 1433-9277 (Online); ISBN 978-3-642-03881-5 (Print) 978-3-642-03882-2 (Online)
    4. MOHYLOVA, J., KRAJCA, V. Signal Processing in Medicine, ISBN 80-8070-341-8, 2005.
    5. HARTIGAN, J. A., WONG, M. A. A.: K-means Clustering Algorithm, Applied Statistics 28, 1979, pp. 100-108. Go to original source...
    6. KARDI, T.: K-mean Clustering Tutorials [online]. [2007-11-12]. WWW

    This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits use, distribution, and reproduction in any medium, provided the original publication is properly cited. No use, distribution or reproduction is permitted which does not comply with these terms.


    Return to the content