Skip to main content
SpringerLink
Log in

Methods to distinguish labour and pregnancy contractions: a systematic literature review

  • Review Paper
  • Published:
Health and Technology Aims and scope Submit manuscript

Abstract

Uterine contractions monitoring is essential during pregnancy progression for due date prediction and the screening of preterm deliveries, i.e., those related to labour contractions occurring before 37 weeks of gestation. As there are different kinds of uterine contraction, distinguishing between true labour ones and normal physiological ones during pregnancy is not trivial. Thus, early identification is necessary for the effective and efficient care of pregnant women to avoid unnecessary and costly hospitalization. In this regard, while classical clinical methods proved their limitations, real-time monitoring of uterine contractions is now possible thanks to a new technique called ElectroHysteroGraphy which measures the uterine electrical activity that is a proxy for the mechanical activity of the muscles of the uterus. An exhaustive and comprehensive literature review was conducted to retrieve and report the state-of-the-art of the methods to distinguish labour contractions from pregnancy contractions. A systematic search was run on different search engines using a search string. A snowball sampling approach was applied to the references of the retrieved articles to identify further appropriate papers. According to this, the relevant references included in the bibliography of each analysed article led to other appropriate articles. Thus, papers dealing with the methods to distinguish labour from pregnancy (normal physiological) contractions and published between 2001 and 2020 were selected. Linear and nonlinear methods have been developed for uterine contractions signals (EHG/EMG) analysis to distinguish labour from pregnancy contractions. Nonlinear methods yielded better results compared to the linear methods, but not all nonlinear methods are promising in terms of clinical application.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Similar content being viewed by others

References

  1. Garfield RE, Maner WL. Physiology and electrical activity of uterine contractions. Semin Cell Dev Biol. 2007;18(3):289–95. https://doi.org/10.1016/j.semcdb.2007.05.004.

    Article  Google Scholar 

  2. Chawanpaiboon S, et al. Global, regional, and national estimates of levels of preterm birth in 2014: a systematic review and modelling analysis. Lancet Glob Health. 2019;7(1):e37–46. https://doi.org/10.1016/S2214-109X(18)30451-0.

    Article  Google Scholar 

  3. Piaggio D, Medenou D, Houessouvo RC, Pecchia L, “Donation of Medical Devices in Low-Income Countries: Preliminary Results from Field Studies”, in CMBEBIH,. vol. 73, A. Badnjevic, R. Škrbić, and L. Gurbeta Pokvić. Eds Cham: Springer International Publishing. 2019;2020:423–7.

    Google Scholar 

  4. Pecchia L, Pallikarakis N, Magjarevic R, Iadanza E. Health Technology Assessment and Biomedical Engineering: Global trends, gaps and opportunities. Med Eng Phys. 2019;72:19–26. https://doi.org/10.1016/j.medengphy.2019.08.008.

    Article  Google Scholar 

  5. Di Pietro L, et al. A Framework for Assessing Healthcare Facilities in Low-Resource Settings: Field Studies in Benin and Uganda. J Med Biol Eng. 2020. https://doi.org/10.1007/s40846-020-00546-3.

    Article  Google Scholar 

  6. Maner WL, Garfield RE. Identification of human term and preterm labor using artificial neural networks on uterine electromyography data. Ann Biomed Eng. 2007;35(3):465–73. https://doi.org/10.1007/s10439-006-9248-8.

    Article  Google Scholar 

  7. Jezewski J, Horoba K, Matonia A, Wrobel J. Quantitative analysis of contraction patterns in electrical activity signal of pregnant uterus as an alternative to mechanical approach. Physiol Meas. 2005;26(5):753–67. https://doi.org/10.1088/0967-3334/26/5/014.

    Article  Google Scholar 

  8. Devedeux D, Marque C, Mansour S, Germain G, Duchêne J. Uterine electromyography: A critical review. Am J Obstet Gynecol. 1993;169(6):1636–53. https://doi.org/10.1016/0002-9378(93)90456-S.

    Article  Google Scholar 

  9. Vrhovec J, Lebar AM. An Uterine Electromyographic Activity as a Measure of Labor Progression. Appl EMG Clin Sports Med. 2012. https://doi.org/10.5772/25526.

    Article  Google Scholar 

  10. Wolfs GMJA, van Leeuwen M. Electromyographic observations on the human uterus during labour. Acta Obstet Gynecol Scand. 1979;58(s90):1–61. https://doi.org/10.3109/00016347909156375.

    Article  Google Scholar 

  11. Rabotti C, Mischi M, van Laar JOEH, Oei SG, Bergmans JWM. Myometrium electromechanical modeling for internal uterine pressure estimation by electrohysterography in 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Minneapolis MN. 2009;6259–6262. https://doi.org/10.1109/IEMBS.2009.5332397.

  12. Gondry J, Duchene J, Marque C. First results on uterine EMG monitoring during pregnancy in Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Paris, France. 1992;2609–2610. https://doi.org/10.1109/IEMBS.1992.5761611.

  13. Diab MO, Marque C, Khalil MA. Classification for uterine emg signals: comparison between ar model and statistical classification method. 2007;5(1):8.

  14. Hassan MM, Terrien J, Muszynski C, Alexandersson A, Marque C, Karlsson B. Better Pregnancy Monitoring Using Nonlinear Correlation Analysis of External Uterine Electromyography. IEEE Trans Biomed Eng. 2013;60(4):1160–6. https://doi.org/10.1109/TBME.2012.2229279.

    Article  Google Scholar 

  15. Alamedine D, Diab A, Muszynski C, Karlsson B, Khalil M, Marque C. Selection algorithm for parameters to characterize uterine EHG signals for the detection of preterm labor. Signal Image Video Process. 2014;8(6):1169–78. https://doi.org/10.1007/s11760-014-0655-2.

    Article  Google Scholar 

  16. Miles AM, Monga M, Richeson KS. Correlation of External and Internal Monitoring of Uterine Activity in a Cohort of Term Patients. Am J Perinatol. 2001;18(03):137–40. https://doi.org/10.1055/s-2001-14522.

    Article  Google Scholar 

  17. Maul H, Maner W, Olson G, Saade G, Garfield R. Non-invasive transabdominal uterine electromyography correlates with the strength of intrauterine pressure and is predictive of labor and delivery. J Matern Fetal Neonatal Med. 2004;15(5):297–301. https://doi.org/10.1080/14767050410001695301.

    Article  Google Scholar 

  18. Lucovnik M, et al. Use of uterine electromyography to diagnose term and preterm labor. Acta Obstet Gynecol Scand. 2011;90(2):150–7. https://doi.org/10.1111/j.1600-0412.2010.01031.x.

    Article  Google Scholar 

  19. Hassan M, Terrien J, Karlsson B, Marque C. Spatial analysis of uterine EMG signals: Evidence of increased in synchronization with term in 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Minneapolis, MN. 2009;6296–6299. https://doi.org/10.1109/IEMBS.2009.5332795.

  20. Vasak B, et al. Uterine electromyography for identification of first-stage labor arrest in term nulliparous women with spontaneous onset of labor. Am J Obstet Gynecol. 2013;209(3):232.e1-232.e8. https://doi.org/10.1016/j.ajog.2013.05.056.

    Article  Google Scholar 

  21. Qian X, Li P, Shi S-Q, Garfield RE, Liu H. Simultaneous recording and analysis of uterine and abdominal muscle electromyographic activity in nulliparous women during labor. Reprod Sci. 2017;24(3):471–7. https://doi.org/10.1177/1933719116658704.

    Article  Google Scholar 

  22. Namadurai P, Padmanabhan V, Swaminathan R. Multifractal analysis of uterine electromyography signals for the assessment of progression of pregnancy in term conditions. IEEE J Biomed Health Inform. 2019;23(5):1972–9. https://doi.org/10.1109/JBHI.2018.2878059.

    Article  Google Scholar 

  23. Hayes-Gill B, et al. Accuracy and reliability of uterine contraction identification using abdominal surface electrodes. Clin. Med. Insights Womens Health, 2012;5: CMWH.S10444. https://doi.org/10.4137/CMWH.S10444.

  24. Euliano TY, et al. Monitoring uterine activity during labor: a comparison of 3 methods. Am J Obstet Gynecol. 2013;208(1):66.e1-66.e6. https://doi.org/10.1016/j.ajog.2012.10.873.

    Article  Google Scholar 

  25. Bajlekov GI, Rabotti C, Oei SG, Mischi M. Electrohysterographic detection of uterine contractions in term pregnancy in 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Milan, 2015;5851–5854. https://doi.org/10.1109/EMBC.2015.7319722.

  26. Mas-Cabo J, Ye-Lin Y, Garcia-Casado J, Alberola-Rubio J, Perales A, Prats-Boluda G. Uterine contractile efficiency indexes for labor prediction: A bivariate approach from multichannel electrohysterographic records. Biomed Signal Process Control. 2018;46:238–48. https://doi.org/10.1016/j.bspc.2018.07.018.

    Article  Google Scholar 

  27. Hao D, Peng J, Wang Y, Liu J, Zhou X, Zheng D. Evaluation of convolutional neural network for recognizing uterine contractions with electrohysterogram. Comput Biol Med. 2019;113:103394. https://doi.org/10.1016/j.compbiomed.2019.103394.

    Article  Google Scholar 

  28. Peng J, Hao D, Liu H, Liu J, Zhou X, Zheng D. Preliminary Study on the Efficient Electrohysterogram Segments for Recognizing Uterine Contractions with Convolutional Neural Networks. BioMed Res Int. 2019;1–9. https://doi.org/10.1155/2019/3168541.

  29. Hassan M, Terrien J, Alexandersson A, Marque C, Karlsson B. Nonlinearity of EHG signals used to distinguish active labor from normal pregnancy contractions in 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology, Buenos Aires, 2010;2387–2390. https://doi.org/10.1109/IEMBS.2010.5627413.

  30. Hassan M, Terrien J, Marque C, Karlsson B. Comparison between approximate entropy, correntropy and time reversibility: Application to uterine electromyogram signals. Med Eng Phys. 2011;33(8):980–6. https://doi.org/10.1016/j.medengphy.2011.03.010.

    Article  Google Scholar 

  31. Terrien J, Hassan M, Germain G, Marque C, Karlsson B. Nonlinearity testing in the case of non-Gaussian surrogates, applied to improving analysis of synchronicity in uterine contraction in 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Minneapolis MN. 2009;3477–3480. https://doi.org/10.1109/IEMBS.2009.5334563.

  32. Garcia-Gonzalez MT, Charleston-Villalobos S, Vargas-Garcia C, Gonzalez-Camarena R, Aljama-Corrales T. Characterization of EHG contractions at term labor by nonlinear analysis in 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Osaka, 2013;7432–7435. https://doi.org/10.1109/EMBC.2013.6611276.

  33. Moslem B, Karlsson B, Diab MO, Khalil M, Marque C. Classification performance of the frequency-related parameters derived from uterine EMG signals in 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Boston. MA, 2011;3371–3374. https://doi.org/10.1109/IEMBS.2011.6090913.

  34. Kandil M, Emarh M, Ellakwa H. Abdominal electromyography in laboring and non-laboring pregnant women at term and its clinical implications. Arch Gynecol Obstet. 2013;288(2):293–7. https://doi.org/10.1007/s00404-013-2757-4.

    Article  Google Scholar 

  35. Alamedine D, Khalil M, Marque C. Comparison of Different EHG Feature Selection Methods for the Detection of Preterm Labor. Comput Math Methods Med. 2013;1–9. https://doi.org/10.1155/2013/485684.

  36. Terrien J, Steingrimsdottir T, Marque C, Karlsson B. Synchronization between EMG at different uterine locations investigated using time-frequency ridge reconstruction: comparison of pregnancy and labor contractions. EURASIP J Adv Signal Process. 2010;2010(1):242493. https://doi.org/10.1155/2010/242493.

    Article  Google Scholar 

  37. Alamedine D, Khalil M, Marque C. Parameters extraction and monitoring in uterine EMG signals. Detection of preterm deliveries. IRBM. 2013;34(4–5):322–5. https://doi.org/10.1016/j.irbm.2013.08.003.

    Article  Google Scholar 

  38. Al-Omar S, Diab A, Nader N, Khalil M, Karlsson B, Marque C. Detecting labor using graph theory on connectivity matrices of uterine EMG in 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Milan, 2015;2195–2198. https://doi.org/10.1109/EMBC.2015.7318826.

  39. Nader N, et al. Classification of pregnancy and labor contractions using a graph theory based analysis in 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Milan, 2015;2876–2879. https://doi.org/10.1109/EMBC.2015.7318992.

  40. Nader N, Hassan M, Falou W, Khalil M, Karlsson B, Marque C. Uterine muscle networks: Connectivity analysis of the EHG during pregnancy and Labor. 2017. p. 23.

  41. Chen L, Hao Y. Feature extraction and classification of ehg between pregnancy and labour group using hilbert-huang transform and extreme learning machine. Comput Math Methods Med. 2017;2017:1–9. https://doi.org/10.1155/2017/7949507.

    Article  Google Scholar 

  42. Athira T, Asmi PS. Analysis of Unipolar and Bipolar 4x4 EHG Signal for Classifying Uterine Contraction. Biomed Pharmacol J. 2019;12(2):1009–14. https://doi.org/10.13005/bpj/1729.

    Article  Google Scholar 

  43. Shero FF, Al-Ani GTS, Khadim EJ, Khaleel HZ. Assessment of linear parameters of Electrohysterograph (EHG) in diagnosis of true labor. Ann Trop Med Public Health. 2020;23(04):139–47. https://doi.org/10.36295/ASRO.2020.23418.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Biomedical Engineering, Ecole Polytechnique D’Abomey-Calavi, University of Abomey-Calavi, Abomey-Calavi, Benin

    Thierry R. Jossou, Daton Medenou & Latif Fagbemi

  2. Materials, Energy and Acoustics Team, Mohammed V University in Rabat, Ecole Supérieure de Technologie de Salé, Rabat, Morocco

    Thierry R. Jossou, Aziz ET-Tahir, Abdelmajid Bybi & Mohamed Sbihi

  3. School of Engineering, University of Warwick, Coventry, CV4 7AL, UK

    Davide Piaggio

Authors
  1. Thierry R. Jossou
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Aziz ET-Tahir
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Daton Medenou
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Abdelmajid Bybi
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Latif Fagbemi
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Mohamed Sbihi
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Davide Piaggio
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Thierry R. Jossou.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Jossou, T.R., ET-Tahir, A., Medenou, D. et al. Methods to distinguish labour and pregnancy contractions: a systematic literature review. Health Technol. 11, 745–757 (2021). https://doi.org/10.1007/s12553-021-00563-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12553-021-00563-5

Keywords

Search

Navigation