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Recognition of premature threatening labour symptoms from bioelectrical uterine activity signals

  • Jerzy Sikora
  • , Adam Matonia
  • , Robert Czabański
  • , Krzysztof Horoba
  • , Janusz Jezewski
  • , Tomasz Kupka
  • Medical University of Silesia in Katowice
  • Institute of Medical Technology and Equipment

Research output: Contribution to journalArticlepeer-review

23 Citations (Scopus)

Abstract

Premature birth is the leading cause of a neonatal death, so, it is extremely important to distinguish the pregnancy at risk of preterm threatening labour. The electrohysterography seems very promising as a method which enables noninvasive recording of readable bioelectrical signal of uterine muscle. The developed instrumentation enabled recording of bioelectrical signals by means of electrodes attached to abdominal wall and determination of set of parameters describing the contractions detected. Noninvasive electrohysterography ensures higher sensitivity and specificity for recognition of uterine contraction activity in comparison to classical mechanical method. Material comprised 27 patients in physiological pregnancy (27-40 week), 21 patients (23-36 week) with the symptoms of threatening premature labour and 14 patients in the first labour period. The classification of electrohystrographic recordings in each pairs of the groups was performed by means Lagrangian Support Vector Machines. The obtained results show that the proposed approach is able to differentiate the contractile activity in physiological pregnancy from that caused by both threatening premature and at term labour. Identification of these pregnant women who are at risk of premature threatening labour leads to an enhanced perinatal surveillance which should enable efficient tocolitic treatment and stimulation of fetal lungs growth.

Original languageEnglish
Pages (from-to)97-103
Number of pages7
JournalArchives of Perinatal Medicine
Volume17
Issue number2
Publication statusPublished - 2011

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

Keywords

  • Feature extraction
  • Lagrangian support vector machine (LSVM)
  • Premature labour
  • Recordings classification
  • Uterine bioelectrical activity

ASJC Scopus subject areas

  • Obstetrics and Gynecology

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