Literature DB >> 28727889

Clinical assessment of uterine contractions.

Wayne R Cohen1.   

Abstract

The assessment of uterine contractions is important in clinical decision-making, but the precise role for appraising contractions remains controversial. Four clinical approaches to assessing contractions are available: manual palpation; intrauterine pressure determination; external tocodynamometry; and electrohysterography. Palpation is inexpensive and harmless but requires the constant bedside presence of a trained observer. Intrauterine pressure measurement is considered the most sensitive and specific technique, and has become the standard by which other methods are judged; however, its quantitative measurements are not always precise or reproducible. Moreover, the availability of intrauterine pressure measurements does not seem to improve maternal or neonatal outcomes in most situations. External tocodynamometry is the most widely used technique. It is easy to apply and provides reasonably accurate information about the frequency and duration of contractions, but not their amplitude. It can require frequent adjustment during labor and might not work well in patients who are obese. Electrohysterography is a recently available noninvasive technology that detects uterine electrical activity using electrodes placed on the mother's abdominal wall. This approach is at least as reliable and accurate as tocodynamometry.
© 2017 International Federation of Gynecology and Obstetrics.

Entities:  

Keywords:  Electrohysterography; Intrauterine pressure; Labor; Montevideo units; Tocodynamometry; Uterine activity; Uterine contractility

Mesh:

Year:  2017        PMID: 28727889     DOI: 10.1002/ijgo.12270

Source DB:  PubMed          Journal:  Int J Gynaecol Obstet        ISSN: 0020-7292            Impact factor:   3.561


  3 in total

1.  Evaluation of convolutional neural network for recognizing uterine contractions with electrohysterogram.

Authors:  Dongmei Hao; Jin Peng; Ying Wang; Juntao Liu; Xiya Zhou; Dingchang Zheng
Journal:  Comput Biol Med       Date:  2019-08-19       Impact factor: 4.589

2.  Preliminary Study on the Efficient Electrohysterogram Segments for Recognizing Uterine Contractions with Convolutional Neural Networks.

Authors:  Jin Peng; Dongmei Hao; Haipeng Liu; Juntao Liu; Xiya Zhou; Dingchang Zheng
Journal:  Biomed Res Int       Date:  2019-10-13       Impact factor: 3.411

3.  Automatic recognition of uterine contractions with electrohysterogram signals based on the zero-crossing rate.

Authors:  Xiaoxiao Song; Xiangyun Qiao; Dongmei Hao; Lin Yang; Xiya Zhou; Yuhang Xu; Dingchang Zheng
Journal:  Sci Rep       Date:  2021-01-21       Impact factor: 4.379

  3 in total

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