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Hellenic Conference on Artificial Intelligence

SETN 2014: Artificial Intelligence: Methods and Applications pp 521–531Cite as

Discriminating Normal from “Abnormal” Pregnancy Cases Using an Automated FHR Evaluation Method

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8445))

Abstract

Electronic fetal monitoring has become the gold standard for fetal assessment both during pregnancy as well as during delivery. Even though electronic fetal monitoring has been introduced to clinical practice more than forty years ago, there is still controversy in its usefulness especially due to the high inter- and intra-observer variability. Therefore the need for a more reliable and consistent interpretation has prompted the research community to investigate and propose various automated methodologies. In this work we propose the use of an automated method for the evaluation of fetal heart rate, the main monitored signal, which is based on a data set, whose labels/annotations are determined using a mixture model of clinical annotations. The successful results of the method suggest that it could be integrated into an assistive technology during delivery.

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Author information

Authors and Affiliations

  1. Department of Cybernetics, Czech Technical University, Prague, Czech

    Jiří Spilka, Václav Chudáček & Lenka Lhotská

  2. Department of Computer Engineering Arta, Laboratory of Knowledge and Intelligent Computing, Technological Educational Institute of Epirus, Greece

    George Georgoulas, Petros Karvelis & Chrysostomos D. Stylios

Authors
  1. Jiří Spilka
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  2. George Georgoulas
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  3. Petros Karvelis
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  4. Václav Chudáček
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  5. Chrysostomos D. Stylios
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  6. Lenka Lhotská
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Editor information

Editors and Affiliations

  1. Department of Computer Science, University of Ioannina, GR 45110, Ioannina, Greece

    Aristidis Likas

  2. Department of Computer Science, University of Ioannina, P.O. Box 1186, 45110, Ioannina, Greece

    Konstantinos Blekas

  3. Hellenic Open University, GR 26335, Peribola, Patras, Greece

    Dimitris Kalles

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Spilka, J., Georgoulas, G., Karvelis, P., Chudáček, V., Stylios, C.D., Lhotská, L. (2014). Discriminating Normal from “Abnormal” Pregnancy Cases Using an Automated FHR Evaluation Method. In: Likas, A., Blekas, K., Kalles, D. (eds) Artificial Intelligence: Methods and Applications. SETN 2014. Lecture Notes in Computer Science(), vol 8445. Springer, Cham. https://doi.org/10.1007/978-3-319-07064-3_45

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  • DOI: https://doi.org/10.1007/978-3-319-07064-3_45

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-07063-6

  • Online ISBN: 978-3-319-07064-3

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