Skip to main content
SpringerLink
Log in

Uterine electromyography for discrimination of labor imminence in women with threatened preterm labor under tocolytic treatment

  • Original Article
  • Published:
Medical & Biological Engineering & Computing Aims and scope Submit manuscript

Abstract

As one of the main aims of obstetrics is to be able to detect imminent delivery in patients with threatened preterm labor, the techniques currently used in clinical practice have serious limitations in this respect. The electrohysterogram (EHG) has now emerged as an alternative technique, providing relevant information about labor onset when recorded in controlled checkups without administration of tocolytic drugs. The studies published to date mainly focus on EHG-burst analysis and, to a lesser extent, on whole EHG window analysis. The study described here assessed the ability of EHG signals to discriminate imminent labor (< 7 days) in women with threatened preterm labor undergoing tocolytic therapy, using both EHG-burst and whole EHG window analyses, by calculating temporal, spectral, and non-linear parameters. Only two non-linear EHG-burst parameters and four whole EHG window analysis parameters were able to distinguish the women who delivered < 7 days from the others, showing that EHG can provide relevant information on the approach of labor, even in women with threatened preterm labor under the effects of tocolytic therapy. The whole EHG window outperformed the EHG-burst analysis and is seen as a step forward in the development of real-time EHG systems able to predict imminent labor in clinical praxis.

The ability of EHG recordings to predict imminent labor (< 7 days) was analyzed in preterm threatened patients undergoing tocolytic therapies by means of EHG-burst and whole EHG window analysis. The non-linear features were found to have better performance than the temporal and spectral parameters in separating women who delivered in less than 7 days from those who did not.

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
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Aboy M, Cuesta-Frau D, Austin D, Micó-Tormos P (2007) Characterization of sample entropy in the context of biomedical signal analysis. Conf Proc IEEE Eng Med Biol Soc:5942–5945. https://doi.org/10.1109/IEMBS.2007.4353701

  2. Aboy M, Hornero R, Abásolo D, Álvarez D (2006) Interpretation of the Lempel-Ziv complexity measure in the context of biomedical signal analysis. IEEE Trans Biomed Eng 53:2282–2288. https://doi.org/10.1109/TBME.2006.883696

    Article  PubMed  Google Scholar 

  3. Chkeir A, Fleury MJ, Karlsson B, Hassan M, Marque C (2013) Patterns of electrical activity synchronization in the pregnant rat uterus. Biomed 3:140–144. https://doi.org/10.1016/j.biomed.2013.04.007

    Article  Google Scholar 

  4. Crandon AJ (1979) Maternal anxiety and neonatal wellbeing. J Psychosom Res 23:113–115. https://doi.org/10.1016/0022-3999(79)90015-1

    Article  CAS  PubMed  Google Scholar 

  5. Devedeux D, Marque C, Mansour S, Germain G, Duchêne J (1993) Uterine electromyography: a critical review. Am J Obstet Gynecol 169:1636–1653. https://doi.org/10.1016/0002-9378(93)90456-S

    Article  CAS  PubMed  Google Scholar 

  6. Fele-Žorž G, Kavšek G, Novak-Antolič Ž, Jager F (2008) A comparison of various linear and non-linear signal processing techniques to separate uterine EMG records of term and pre-term delivery groups. Med Biol Eng Comput 46:911–922. https://doi.org/10.1007/s11517-008-0350-y

    Article  PubMed  Google Scholar 

  7. Fergus P, Cheung P, Hussain A, al-Jumeily D, Dobbins C, Iram S (2013) Prediction of preterm deliveries from EHG signals using machine learning. PLoS One 8:e77154. https://doi.org/10.1371/journal.pone.0077154

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Garfield RE, Maner WL (2006) Biophysical methods of prediction and prevention of preterm labor: uterine electromyography and cervical light-induced fluorescence—new obstetrical diagnostic techniques. In: Preterm Birth pp 131–144

  9. Garfield RE, Maner WL (2007) Physiology and electrical activity of uterine contractions. Semin Cell Dev Biol 18:289–295. https://doi.org/10.1016/j.semcdb.2007.05.004

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Garfield RE, Maner WL, MacKay LB et al (2005) Comparing uterine electromyography activity of antepartum patients versus term labor patients. Am J Obstet Gynecol 193:23–29. https://doi.org/10.1016/j.ajog.2005.01.050

    Article  PubMed  Google Scholar 

  11. Goldenberg RL, Culhane JF, Iams JD, Romero R (2008) Epidemiology and causes of preterm birth. Lancet 371:75–84. https://doi.org/10.1016/S0140-6736(08)60074-4

    Article  PubMed  Google Scholar 

  12. American College of Obstetricians and Gynecologists and Committee on Practice Bulletins— Obstetrics (2012) Practice bulletin no. 127. Obstet Gynecol 119(6):1308–1317.

    Article  Google Scholar 

  13. Hadar E, Biron-Shental T, Gavish O, Raban O, Yogev Y (2015) A comparison between electrical uterine monitor, tocodynamometer and intra uterine pressure catheter for uterine activity in labor. J Matern Neonatal Med 28:1367–1374. https://doi.org/10.3109/14767058.2014.954539

    Article  Google Scholar 

  14. Hans P, Dewandre P, Brichant JF, Bonhomme V (2005) Comparative effects of ketamine on Bispectral Index and spectral entropy of the electroencephalogram under sevoflurane anaesthesia. Br J Anaesth 94:336–340. https://doi.org/10.1093/bja/aei047

    Article  CAS  PubMed  Google Scholar 

  15. Hassan M, Terrien J, Marque C, Karlsson B (2011) Comparison between approximate entropy, correntropy and time reversibility: application to uterine electromyogram signals. Med Eng Phys 33:980–986. https://doi.org/10.1016/j.medengphy.2011.03.010

    Article  PubMed  Google Scholar 

  16. Hassan M, Terrien J, Muszynski C et al (2013) Better pregnancy monitoring using nonlinear correlation analysis of external uterine electromyography. IEEE Trans Biomed Eng 60:1160–1166. https://doi.org/10.1109/TBME.2012.2229279

    Article  PubMed  Google Scholar 

  17. Horoba K, Jezewski J, Matonia A, Wrobel J, Czabanski R, Jezewski M (2016) Early predicting a risk of preterm labour by analysis of antepartum electrohysterograhic signals. Biocybern Biomed Eng 36:574–583. https://doi.org/10.1016/j.bbe.2016.06.004

    Article  Google Scholar 

  18. Lawn JE, Wilczynska-Ketende K, Cousens SN (2006) Estimating the causes of 4 million neonatal deaths in the year 2000. Int J Epidemiol 35:706–718. https://doi.org/10.1093/ije/dyl043

    Article  PubMed  Google Scholar 

  19. Lemancewicz A, Borowska M, Kuć P, Jasińska E, Laudański P, Laudański T, Oczeretko E (2016) Early diagnosis of threatened premature labor by electrohysterographic recordings—the use of digital signal processing. Biocybern Biomed Eng 36:302–307. https://doi.org/10.1016/j.bbe.2015.11.005

    Article  Google Scholar 

  20. M L WLM, LR C (2012) Noninvasive uterine electromyography for prediction of preterm delivery. Am J Obstet Gynecol 204:1–20. https://doi.org/10.1016/j.ajog.2010.09.024.Noninvasive

    Article  Google Scholar 

  21. Maner WL, Garfield RE (2007) Identification of human term and preterm labor using artificial neural networks on uterine electromyography data. Ann Biomed Eng 35:465–473. https://doi.org/10.1007/s10439-006-9248-8

    Article  PubMed  Google Scholar 

  22. Maner WL, Garfield RE, Maul H, Olson G, Saade G (2003) Predicting term and preterm delivery with transabdominal uterine electromyography. Obstet Gynecol 101:1254–1260. https://doi.org/10.1016/S0029-7844(03)00341-7

    Article  PubMed  Google Scholar 

  23. Marque C, Gondry J (1999) Use of the electrohysterogram signal for characterization of contractions during pregnancy. IEEE Trans Biomed Eng 46:1222–1229

    Article  PubMed  Google Scholar 

  24. Maul H, Maner WL, Olson G, Saade GR, Garfield RE (2004) Non-invasive transabdominal uterine electromyography correlates with the strength of intrauterine pressure and is predictive of labor and delivery. J Matern Fetal Neonatal Med 15:297–301

    Article  CAS  PubMed  Google Scholar 

  25. Mischi M, Chen C, Ignatenko T, de Lau H, Ding B, Oei SGG, Rabotti C (2018) Dedicated entropy measures for early assessment of pregnancy progression from single-channel electrohysterography. IEEE Trans Biomed Eng 65:875–884. https://doi.org/10.1109/TBME.2017.2723933

    Article  PubMed  Google Scholar 

  26. Most O, Langer O, Kerner R, Ben David G, Calderon I (2008) Can myometrial electrical activity identify patients in preterm labor? Am J Obstet Gynecol 199:378. https://doi.org/10.1016/j.ajog.2008.08.003

    Article  PubMed  Google Scholar 

  27. Petrou S (2005) The economic consequences of preterm birth during the first 10 years of life. BJOG 112:10–15. https://doi.org/10.1111/j.1471-0528.2005.00577.x

    Article  PubMed  Google Scholar 

  28. Rabotti C, Sammali F, Kuijsters N, et al (2015) Analysis of uterine activity in nonpregnant women by electrohysterography: a feasibility study. In: Proc Annu Int Conf IEEE Eng Med Biol Soc EMBS pp 5916–5919

  29. Schlembach D, Maner WL, Garfield RE, Maul H (2009) Monitoring the progress of pregnancy and labor using electromyography. Eur J Obstet Gynecol Reprod Biol 144:2–8. https://doi.org/10.1016/j.ejogrb.2009.02.016

    Article  Google Scholar 

  30. Sikora J, Matonia A, Czabański R et al (2011) Recognition of premature threatening labour symptoms from bioelectrical uterine activity signals. Arch Perinat Med 17:97–103

    Google Scholar 

  31. Vinken MPGC, Rabotti C, Mischi M, van Laar JOEH, Oei SG (2010) Nifedipine-induced changes in the electrohysterogram of preterm contractions: feasibility in clinical practice. Obstet Gynecol Int 2010:325635. https://doi.org/10.1155/2010/325635

    Article  PubMed  PubMed Central  Google Scholar 

  32. Vrhovec J, Lebar AM (2012) An uterine electromyographic activity as a measure of labor progression. Appl EMG Clin Sport Med 243–268. doi: https://doi.org/10.5772/25526

  33. Vrhovec J, Macek-Lebar A, Rudel D (2007) Evaluating uterine electrohysterogram with entropy. 11th Mediterr Conf Med Biomed Eng Comput 144–147. https://doi.org/10.1007/978-3-540-73044-6_36

  34. Ye-Lin Y, Bueno-Barrachina JM, Prats-boluda G, Rodriguez de Sanabria R, Garcia-Casado J (2017) Wireless sensor node for non-invasive high precision electrocardiographic signal acquisition based on a multi-ring electrode. Measurement 97:195–202. https://doi.org/10.1016/J.MEASUREMENT.2016.11.009

    Article  Google Scholar 

  35. Ye-Lin Y, Garcia-Casado J, Prats-Boluda G, Alberola-Rubio J, Perales A (2014) Automatic identification of motion artifacts in EHG recording for robust analysis of uterine contractions. Comput Math Methods Med 2014:1–11. https://doi.org/10.1155/2014/470786

    Article  Google Scholar 

  36. Zhang XS, Roy RJ, Jensen EW (2001) EEG complexity as a measure of depth of anesthesia for patients. IEEE Trans Biomed Eng 48:1424–1433. https://doi.org/10.1109/10.966601

    Article  CAS  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Centro de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València, Camino de Vera s/n Ed.7F, 46022, Valencia, Spain

    Javier Mas-Cabo, Gema Prats-Boluda, Javier Garcia-Casado & Yiyao Ye-Lin

  2. Servicio de Obstetricia, H.U. P. La Fe, Valencia, Spain

    Alfredo Perales & José Alberola-Rubio

Authors
  1. Javier Mas-Cabo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Gema Prats-Boluda
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Alfredo Perales
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Javier Garcia-Casado
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. José Alberola-Rubio
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Yiyao Ye-Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Gema Prats-Boluda.

Ethics declarations

Ethical approval

“All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.”

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mas-Cabo, J., Prats-Boluda, G., Perales, A. et al. Uterine electromyography for discrimination of labor imminence in women with threatened preterm labor under tocolytic treatment. Med Biol Eng Comput 57, 401–411 (2019). https://doi.org/10.1007/s11517-018-1888-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11517-018-1888-y

Keywords

Search

Navigation