Robust Characterization of the Uterine Myoelectrical Activity in Different Obstetric Scenarios

Overview

Journal Entropy (Basel)

Publisher MDPI

Date 2020 Dec 8

PMID 33286515

Citations 7

Authors

Javier Mas-Cabo

Yiyao Ye-Lin

Javier Garcia-Casado

Alba Diaz-Martinez

Alfredo Perales-Marin

Rogelio Monfort-Ortiz

Alba Roca-Prats

Angel Lopez-Corral

Gema Prats-Boluda

Affiliations

Soon will be listed here.

Abstract

Electrohysterography (EHG) has been shown to provide relevant information on uterine activity and could be used for predicting preterm labor and identifying other maternal fetal risks. The extraction of high-quality robust features is a key factor in achieving satisfactory prediction systems from EHG. Temporal, spectral, and non-linear EHG parameters have been computed to characterize EHG signals, sometimes obtaining controversial results, especially for non-linear parameters. The goal of this work was to assess the performance of EHG parameters in identifying those robust enough for uterine electrophysiological characterization. EHG signals were picked up in different obstetric scenarios: antepartum, including women who delivered on term, labor, and post-partum. The results revealed that the 10th and 90th percentiles, for parameters with falling and rising trends as labor approaches, respectively, differentiate between these obstetric scenarios better than median analysis window values. Root-mean-square amplitude, spectral decile 3, and spectral moment ratio showed consistent tendencies for the different obstetric scenarios as well as non-linear parameters: Lempel-Ziv, sample entropy, spectral entropy, and SD1/SD2 when computed in the fast wave high bandwidth. These findings would make it possible to extract high quality and robust EHG features to improve computer-aided assessment tools for pregnancy, labor, and postpartum progress and identify maternal fetal risks.

Citing Articles

An open dataset with electrohysterogram records of pregnancies ending in induced and cesarean section delivery.

Jager F Sci Data. 2023; 10(1):669.

PMID: 37783671 PMC: 10545725. DOI: 10.1038/s41597-023-02581-6.

Prediction of Preterm Labor from the Electrohysterogram Signals Based on Different Gestational Weeks.

Far S, Beiramvand M, Shahbakhti M, Augustyniak P Sensors (Basel). 2023; 23(13).

PMID: 37447815 PMC: 10346803. DOI: 10.3390/s23135965.

Comparison of Oxytocin vs. Carbetocin Uterotonic Activity after Caesarean Delivery Assessed by Electrohysterography: A Randomised Trial.

Paljk Likar I, Becic E, Pezdirc N, Gersak K, Lucovnik M, Trojner Bregar A Sensors (Basel). 2022; 22(22).

PMID: 36433591 PMC: 9698977. DOI: 10.3390/s22228994.

Combination of Feature Selection and Resampling Methods to Predict Preterm Birth Based on Electrohysterographic Signals from Imbalance Data.

Nieto-Del-Amor F, Prats-Boluda G, Garcia-Casado J, Diaz-Martinez A, Diago-Almela V, Monfort-Ortiz R Sensors (Basel). 2022; 22(14).

PMID: 35890778 PMC: 9319575. DOI: 10.3390/s22145098.

Assessment of Dispersion and Bubble Entropy Measures for Enhancing Preterm Birth Prediction Based on Electrohysterographic Signals.

Nieto-Del-Amor F, Beskhani R, Ye-Lin Y, Garcia-Casado J, Diaz-Martinez A, Monfort-Ortiz R Sensors (Basel). 2021; 21(18).

PMID: 34577278 PMC: 8471282. DOI: 10.3390/s21186071.

References

Liu L, Johnson H, Cousens S, Perin J, Scott S, Lawn J . Global, regional, and national causes of child mortality: an updated systematic analysis for 2010 with time trends since 2000. Lancet. 2012; 379(9832):2151-61. DOI: 10.1016/S0140-6736(12)60560-1. View

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. DOI: 10.1080/14767050410001695301. View

Terrien J, Marque C, Karlsson B . Spectral characterization of human EHG frequency components based on the extraction and reconstruction of the ridges in the scalogram. Annu Int Conf IEEE Eng Med Biol Soc. 2007; 2007:1872-5. DOI: 10.1109/IEMBS.2007.4352680. View

Batista A, Najdi S, Godinho D, Martins C, Serrano F, Ortigueira M . A multichannel time-frequency and multi-wavelet toolbox for uterine electromyography processing and visualisation. Comput Biol Med. 2016; 76:178-91. DOI: 10.1016/j.compbiomed.2016.07.003. View

Garfield R, Maner W, MacKay L, Schlembach D, Saade G . Comparing uterine electromyography activity of antepartum patients versus term labor patients. Am J Obstet Gynecol. 2005; 193(1):23-9. DOI: 10.1016/j.ajog.2005.01.050. View

Ren P, Yao S, Li J, Valdes-Sosa P, Kendrick K . Improved Prediction of Preterm Delivery Using Empirical Mode Decomposition Analysis of Uterine Electromyography Signals. PLoS One. 2015; 10(7):e0132116. PMC: 4498691. DOI: 10.1371/journal.pone.0132116. View

Garcia-Casado J, Ye-Lin Y, Prats-Boluda G, Mas-Cabo J, Alberola-Rubio J, Perales A . Electrohysterography in the diagnosis of preterm birth: a review. Physiol Meas. 2018; 39(2):02TR01. DOI: 10.1088/1361-6579/aaad56. View

Mischi M, Chen C, Ignatenko T, de Lau H, Ding B, Oei S . Dedicated Entropy Measures for Early Assessment of Pregnancy Progression From Single-Channel Electrohysterography. IEEE Trans Biomed Eng. 2017; 65(4):875-884. DOI: 10.1109/TBME.2017.2723933. View

Rajendra Acharya U, Sudarshan V, Rong S, Tan Z, Lim C, Koh J . Automated detection of premature delivery using empirical mode and wavelet packet decomposition techniques with uterine electromyogram signals. Comput Biol Med. 2017; 85:33-42. DOI: 10.1016/j.compbiomed.2017.04.013. View

10.

Smrdel A, Jager F . Separating sets of term and pre-term uterine EMG records. Physiol Meas. 2015; 36(2):341-55. DOI: 10.1088/0967-3334/36/2/341. View

11.

Leman H, Marque C, Gondry J . Use of the electrohysterogram signal for characterization of contractions during pregnancy. IEEE Trans Biomed Eng. 1999; 46(10):1222-9. DOI: 10.1109/10.790499. View

12.

Dimitrov G, Arabadzhiev T, Mileva K, Bowtell J, Crichton N, Dimitrova N . Muscle fatigue during dynamic contractions assessed by new spectral indices. Med Sci Sports Exerc. 2006; 38(11):1971-9. DOI: 10.1249/01.mss.0000233794.31659.6d. View

13.

Maner W, Garfield R . Identification of human term and preterm labor using artificial neural networks on uterine electromyography data. Ann Biomed Eng. 2007; 35(3):465-73. DOI: 10.1007/s10439-006-9248-8. View

14.

Fele-Zorz G, Kavsek G, Novak-Antolic Z, Jager F . 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. 2008; 46(9):911-22. DOI: 10.1007/s11517-008-0350-y. View

15.

Karlsson H, Perez Sanz C . [Postpartum haemorrhage]. An Sist Sanit Navar. 2009; 32 Suppl 1:159-67. DOI: 10.23938/ASSN.0185. View

16.

Euliano T, Nguyen M, Darmanjian S, McGorray S, Euliano N, Onkala A . Monitoring uterine activity during labor: a comparison of 3 methods. Am J Obstet Gynecol. 2012; 208(1):66.e1-6. PMC: 3529844. DOI: 10.1016/j.ajog.2012.10.873. View

17.

Nagarajan R, Eswaran H, Wilson J, Murphy P, Lowery C, Preissl H . Analysis of uterine contractions: a dynamical approach. J Matern Fetal Neonatal Med. 2003; 14(1):8-21. DOI: 10.1080/jmf.14.1.8.21. View

18.

Verdenik I, Pajntar M, Leskosek B . Uterine electrical activity as predictor of preterm birth in women with preterm contractions. Eur J Obstet Gynecol Reprod Biol. 2001; 95(2):149-53. DOI: 10.1016/s0301-2115(00)00418-8. View

19.

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. DOI: 10.1016/j.medengphy.2011.03.010. View

20.

Garfield R, Maner W, Maul H, Saade G . Use of uterine EMG and cervical LIF in monitoring pregnant patients. BJOG. 2005; 112 Suppl 1:103-8. DOI: 10.1111/j.1471-0528.2005.00596.x. View