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A neuro-fuzzy approach to the classification of fetal cardiotocograms

  • Institute of Medical Technology and Equipment

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

26 Citations (Scopus)

Abstract

Cardiotocography (CTG) is a primary biophysical method of fetal monitoring. The assessment of the printed CTG traces is based on the visual analysis of patterns describing the variability of fetal heart rate signal. The correct interpretation of traces from a bedside monitor is rather difficult even for experienced clinicians, so computer-aided fetal monitoring systems have become very popular. At present effective techniques enabling automated conclusion generation based on cardiotocograms are still being searched. The presented work describes an application the Artificial Neural Network Based on Logical Interpretation of fuzzy if-then Rules (ANBLIR) to classification of the fetal state as being normal or abnormal. A set of quantitative parameters describing fetal cardiotocograms is the system input. To evaluate the quality of the classification we proposed the overall validity index as a function of various prognostic indices. The obtained results confirm the usability of the ANBLIR neuro-fuzzy system for records classification within computer-aided fetal surveillance systems.

Original languageEnglish
Title of host publication14th Nordic-Baltic Conference on Biomedical Engineering and Medical Physics, NBC 2008
PublisherSpringer Verlag
Pages446-449
Number of pages4
ISBN (Print)9783540693666
DOIs
Publication statusPublished - 2008

Publication series

NameIFMBE Proceedings
Volume20 IFMBE
ISSN (Print)1680-0737

Keywords

  • fetal monitoring
  • neurofuzzy systems
  • signal classification

ASJC Scopus subject areas

  • Bioengineering
  • Biomedical Engineering

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