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Classification of Uterine Electrical Activity Patterns for Early Detection of Preterm Birth

  • Conference paper
Proceedings of the 8th International Conference on Computer Recognition Systems CORES 2013

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 226))

Abstract

Preterm birth is the leading cause of a neonatal death, so it is extremely important to distinguish the pregnancy at risk of preterm threatening labour. Monitoring an electrical activity of the uterine muscle seems very promising as a method which enables noninvasive recording of good quality electrohysterographic signals. The developed instrumentation enabled recording of signals by means of electrodes attached to abdominal wall and determination of quantitative parameters describing the contractions detected. Our research material comprised 3 groups of patients: with physiological pregnancy, with the symptoms of premature threatening labour and patients during at term labour. The classification of uterine activity signals in each pair of groups was made using nonlinear Lagrangian Support Vector Machines which allows for improving the computational efficiency and learning quality of the SVM algorithm. The obtained results show that the proposed approach is able to differentiate between the contractile activity in physiological pregnancy and that connected with a risk of premature labour. Identification of these high-risk pregnancies leads to an enhanced perinatal surveillance.

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Authors and Affiliations

  1. Biomedical Signal Processing, Institute of Medical Technology and Equipment, Zabrze, Poland

    Janusz Jezewski, Adam Matonia, Krzysztof Horoba & Tomasz Kupka

  2. Institute of Electronics, Silesian University of Technology, Gliwice, Poland

    Robert Czabanski

Authors
  1. Janusz Jezewski
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  2. Adam Matonia
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  3. Robert Czabanski
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  4. Krzysztof Horoba
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  5. Tomasz Kupka
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Corresponding author

Correspondence to Janusz Jezewski .

Editor information

Editors and Affiliations

  1. , Department of Systems and Computer, Wroclaw University of Technology, WybrzezeWyspianskiego St 27, Wroclaw, 50-370, Poland

    Robert Burduk

  2. , Department of Systems and Computer, Wroclaw University of Technology, WybrzezeWyspianskiego St 27, Wroclaw, 50-370, Poland

    Konrad Jackowski

  3. , Department of Systems and Computer, Wroclaw University of Technology, WybrzezeWyspianskiego St 27, Wroclaw, 50-370, Poland

    Marek Kurzynski

  4. , Department of Systems and Computer, Wroclaw University of Technology, WybrzezeWyspianskiego St 27, Wroclaw, 50-370, Poland

    Michał Wozniak

  5. , Department of Systems and Computer, Wroclaw University of Technology, WybrzezeWyspianskiego St, Wroclaw, 50-370, Poland

    Andrzej Zolnierek

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© 2013 Springer International Publishing Switzerland

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Jezewski, J., Matonia, A., Czabanski, R., Horoba, K., Kupka, T. (2013). Classification of Uterine Electrical Activity Patterns for Early Detection of Preterm Birth. In: Burduk, R., Jackowski, K., Kurzynski, M., Wozniak, M., Zolnierek, A. (eds) Proceedings of the 8th International Conference on Computer Recognition Systems CORES 2013. Advances in Intelligent Systems and Computing, vol 226. Springer, Heidelberg. https://doi.org/10.1007/978-3-319-00969-8_55

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  • DOI: https://doi.org/10.1007/978-3-319-00969-8_55

  • Publisher Name: Springer, Heidelberg

  • Print ISBN: 978-3-319-00968-1

  • Online ISBN: 978-3-319-00969-8

  • eBook Packages: EngineeringEngineering (R0)

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