Sleep classification with a combination of symbolic learning and learning vector quantization

Gert Pfurtscheller, Doris Flotzinger, Miroslav Kubat

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Besides statistical methods, various Artificial Intelligence approaches can be used for sleep classification. Learning vector quantization (LVQ) and the top-down induction of decision trees (TDIDT) were applied on 8-hour sleep data from infants. It was shown that with a combination of TDIDT and LVQ the input dimension of the LVQ can be reduced without decreasing the classification accuracy. Classification accuracy was between 67 and 76%, depending on the infant.

Original languageEnglish (US)
Title of host publicationProceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1992
EditorsRobert Plonsey, Swamy Laxminarayan, Jean Louis Coatrieux, Jean Pierre Morucci
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2748-2749
Number of pages2
ISBN (Electronic)0780307852
DOIs
StatePublished - Jan 1 1992
Externally publishedYes
Event14th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1992 - Paris, France
Duration: Oct 29 1992Nov 1 1992

Publication series

NameProceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
Volume6
ISSN (Print)1557-170X

Conference

Conference14th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1992
CountryFrance
CityParis
Period10/29/9211/1/92

ASJC Scopus subject areas

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

Fingerprint Dive into the research topics of 'Sleep classification with a combination of symbolic learning and learning vector quantization'. Together they form a unique fingerprint.

  • Cite this

    Pfurtscheller, G., Flotzinger, D., & Kubat, M. (1992). Sleep classification with a combination of symbolic learning and learning vector quantization. In R. Plonsey, S. Laxminarayan, J. L. Coatrieux, & J. P. Morucci (Eds.), Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 1992 (pp. 2748-2749). [5761661] (Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS; Vol. 6). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IEMBS.1992.5761661