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Investigating pH based evaluation of fetal heart rate (FHR) recordings
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.
Laboratory of Knowledge and Intelligent Computing, Department of Computer Engineering, Technological Educational Institute of Epirus, Arta.
CIIRC, Czech Technical, University in Prague.
CIIRC, Czech Technical, University in Prague.
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2017 (English)In: Health and Technology, ISSN 2190-7188, E-ISSN 2190-7196Article in journal (Refereed) Epub ahead of print
Abstract [en]

Cardiotocography (CTG) is a standard tool for the assessment of fetal well-being during pregnancy and delivery. However, its interpretation is associated with high inter- and intra-observer variability. Since its introduction there have been numerous attempts to develop computerized systems assisting the evaluation of the CTG recording. Nevertheless these systems are still hardly used in a delivery ward. Two main approaches to computerized evaluation are encountered in the literature; the first one emulates existing guidelines, while the second one is more of a data-driven approach using signal processing and computational methods. The latter employs preprocessing, feature extraction/selection and a classifier that discriminates between two or more classes/conditions. These classes are often formed using the umbilical cord artery pH value measured after delivery. In this work an approach to Fetal Heart Rate (FHR) classification using pH is presented that could serve as a benchmark for reporting results on the unique open-access CTU-UHB CTG database, the largest and the only freely available database of this kind. The overall results using a very small number of features and a Least Squares Support Vector Machine (LS-SVM) classifier, are in accordance to the ones encountered in the literature and outperform the results of a baseline classification scheme proving the utility of using advanced data processing methods. Therefore the achieved results can be used as a benchmark for future research involving more informative features and/or better classification algorithms

Place, publisher, year, edition, pages
2017.
National Category
Control Engineering
Research subject
Control Engineering
Identifiers
URN: urn:nbn:se:ltu:diva-64813DOI: 10.1007/s12553-017-0201-7OAI: oai:DiVA.org:ltu-64813DiVA: diva2:1120515
Available from: 2017-07-06 Created: 2017-07-06 Last updated: 2017-07-06

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
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More styles
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  • de-DE
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  • en-US
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  • nn-NB
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  • Other locale
More languages
Output format
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  • asciidoc
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