Advertisement

Non-invasive Intrauterine Pressure Estimation Based on Nonlinear Parameters Computed from the Electrohysterogram

  • Monica Albaladejo-Belmonte
  • Gema Prats-Boluda
  • Yiyao Ye-Lin
  • Carlos Benalcazar-Parra
  • Ángel Lopez
  • Alfredo Perales
  • Javier Garcia-CasadoEmail author
Conference paper
Part of the IFMBE Proceedings book series (IFMBE, volume 76)

Abstract

Monitoring uterine contractions is essential during pregnancy and labor to obtain information on time-to-delivery and maternal and fetal wellbeing Intrauterine pressure (IUP) is considered the “gold standard” to monitor uterine activity, though it requires membrane rupture and is highly invasive. Considering that uterine mechanical activity is a direct consequence of uterine myoelectrical activity, IUP signal can be non-invasively estimated from abdominal electrohysterogram (EHG) recordings. Previous works have reported EHG-based IUP estimates with linear parameters as root-mean-square or Teager energy. Due to non-linear nature of biological processes, the aim of this study was to test the performance of different non-linear EHG parameters to estimate IUP signal. Simultaneous EHG and IUP recordings were conducted in 17 women during labour. Teager energy (TE), Sample entropy (SampEn), Spectral entropy (SpEn), Lempel-Ziv (LZ), and Poincaré parameters: SD1, SD2, SDRR and SD1/SD2 were computed from the EHG. Different window lengths for computation and for a smoothing moving average filter were tested. Monovariable linear regression models were used to obtain IUP estimates. The best results were obtained with TE and SD1, both computed and filtered with windows of 5 s and 20 s, respectively. In the latter case, the RMSerror was 12.25 ± 4.03 mmHg, which points that non-linear EHG parameters can provide relevant information for non-invasive uterine activity monitoring.

Keywords

Uterine activity Intrauterine pressure Electrohysterography Signal processing Non-linear analysis 

Notes

Acknowledgment

Nonetheless, this research has received funding from the Spanish Ministry of Economy and Competitiveness and the European Regional Development Fund (DPI2015-68397-R), the Generalitat Valenciana (GV/2018/104) and UPV-IIS La Fe (UPV_FE-2018-C03). These public entities provided only financial support and did not influence at all in the design, development or publication of the work.

Conflict of Interest

The authors have no conflict of interest in terms of personal financial interests or employment.

References

  1. 1.
    Euliano, T.Y., Nguyen, M.T., Darmanjian, S., McGorray, S.P., Euliano, N., Onkala, A., et al.: Monitoring uterine activity during labor: a comparison of 3 methods. Am. J. Obstet. Gynecol. 208(66), e1–e6 (2013).  https://doi.org/10.1016/j.ajog.2012.10.873CrossRefGoogle Scholar
  2. 2.
    Hassan, M.: Analysis of the propagation of uterine electrical activity applied to predict preterm labor. Bioengineering. UTC Compiègne; Reykjavik University (2011). English. fftel-01226162fGoogle Scholar
  3. 3.
    Benalcazar-Parra, C., Montfort-Orti, R., Ye-Lin, Y., Alberola-Rubio, J., Marin, A.P., Mas-Cabo, J., et al.: Characterization of uterine response to misoprostol based on electrohysterogram. In: Proceedings of 10th International Joint Conference on Biomedical Engineering and System Technology. SCITEPRESS – Science and Technology Publications (2017).  https://doi.org/10.5220/0006146700640069
  4. 4.
    Kaiser, J.F.: On a simple algorithm to calculate the “energy” of a signal. In: International Conference on Acoustics, Speech, and Signal Processing, pp. 381–384. IEEE (1990).  https://doi.org/10.1109/icassp.1990.115702
  5. 5.
    Richman, J.S., Moorman, J.R.: Physiological time-series analisis using approximate entropy and sample Entropy. Am. J. Physiol. Heart Circ. Physiol. 278, H2039–H2049 (2000).  https://doi.org/10.1152/ajpheart.2000.278.6.h2039CrossRefGoogle Scholar
  6. 6.
    Kullback, S.: Information Theory and Statistics. Wiley, New York (1959)zbMATHGoogle Scholar
  7. 7.
    Aboy, M., Hornero, R., Abásolo, D., Álvarez, D.: Interpretation of the Lempel-Ziv complexity measure in the context of biomedical signal analysis. IEEE Trans. Biomed. Eng. 53(11), 2282–2288 (2006).  https://doi.org/10.1109/tbme.2006.883696CrossRefGoogle Scholar
  8. 8.
    Tayel, M.B., AlSaba, E.I.: Poincaré plot for heart rate variability. Int. J. Biomed. Biol. Eng. 9(9) (2015). https://waset.org/Publication/10002615
  9. 9.
    Benalcazar-Parra, C., Sempere, C., Marin, A.P.: Improvement of non-invasive intrauterine pressure estimation based on electrohysterogram. In: XXXV Congreso Anual de la Sociedad Española de Ingeniería Biomédica, Bilbao, pp. 225–238 (2017)Google Scholar
  10. 10.
    Benalcazar-Parra, C., Ye-Lin, Y., Garcia-Casado, J., Monfort-Orti, R., et al.: Electrohysterographic characterization of the uterine myoelectrical response to labor induction drugs. Med. Eng. Phys. 56, 27–35 (2018)CrossRefGoogle Scholar
  11. 11.
    Di Marco, L.Y., et al.: Recurring patterns in stationary intervals of abdominal uterine eletromyograms during gestation. Med. Biol. Eng. Comput. 52, 707–716 (2014)CrossRefGoogle Scholar
  12. 12.
    Marple, L.: Resolution of conventional fourier, autoregressive, and special ARMA methods of spectrum analysis. In: IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 1977, pp. 74–77. IEEE (1977).  https://doi.org/10.1109/icassp.1977.1170219

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Monica Albaladejo-Belmonte
    • 1
  • Gema Prats-Boluda
    • 1
  • Yiyao Ye-Lin
    • 1
  • Carlos Benalcazar-Parra
    • 1
  • Ángel Lopez
    • 2
  • Alfredo Perales
    • 2
  • Javier Garcia-Casado
    • 1
    Email author
  1. 1.Centro de Investigación e Innovación en BioingenieríaUniversitat Politècnica de ValènciaValenciaSpain
  2. 2.Servicio de Obstetricia y GinecologíaHospital Universitario y Politécnico La Fe de ValenciaValenciaSpain

Personalised recommendations