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2020

Copyright (c) 2020 María Fernanda Acosta Romo, Marco Antonio Chamorro Lucero

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The Electrohysterographic Signals in Preterm Birth Prediction: A Narrative Review

https://doi.org/10.17081/innosa.98
María Fernanda Acosta Romo
Universidad Mariana. San Juan de Pasto, Colombia
https://orcid.org/0000-0003-0772-8021
Marco Antonio Chamorro Lucero
Universidad Mariana. San Juan de Pasto, Colombia

Corresponding Author(s) : María Fernanda Acosta Romo

macosta@umariana.edu.co

Ciencia e Innovación en Salud, 2020

Abstract

 






Background: In clinical practice, various methods or techniques are used to monitor uterine activity, so it is essential to identify, through a narrative review, ¿what are the techniques for diagnosing preterm delivery? Two thematic areas were addressed: comparison of signals electrohysterographic with other diagnostic tests and analysis of electrohysterographic signals. Methods: A review of scientific articles, in English and Spanish, was carried out in scientific databases, using combinations of keywords: electrohysterography, uterine electrical activity, uterine biosignals, preterm delivery, diagnosis of preterm delivery. It was taken into account that they were published between the years 2012 to 2019. Conclusions: Electrohysterography is the most sensitive technique for uterine monitoring compared to other non-invasive diagnostic tests used for diagnosis of preterm labor.





Keywords

Preterm labor, electromyography, early diagnosis, signal processing computer-assisted
1.
Acosta Romo MF, Chamorro Lucero MA. The Electrohysterographic Signals in Preterm Birth Prediction: A Narrative Review. Ciencia e Innovación en Salud [Internet]. 2020 Oct. 9 [cited 2024 Nov. 25];. Available from: https://revistas.unisimon.edu.co/index.php/innovacionsalud/article/view/3760
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References
  • Jager F, Libenšek S, Geršak K. Characterization and automatic classification of preterm and term uterine records. PLoS ONE. [Internet] 2018 [citado 2019 Nov 26]; 13(8): e0202125. DOI: 10.1371/journal.pone.0202125
  • Navas, L, Lankester, E. Abordaje del Parto Pretérmino. Rev Méd Costa Rica. 2014; 71(613), 725-773. https://www.medigraphic.com/pdfs/revmedcoscen/rmc-2014/rmc145i.pdf
  • Gladstone M, Oliver C, Van den Broek N. Survival, morbidity, growth and developmental delay for babies born preterm in low and middle income countries - a systematic review of outcomes measured. PLoS One. [Internet] 2015 [citado 2019 Nov 23]; 10(3): e0120566. DOI: 10.1371/journal.pone.0120566
  • Organización Mundial de la Salud. Nacimientos prematuros. 2018. [Acceso 30 de marzo de 2019]. https://www.who.int/es/news-room/fact-sheets/detail/preterm-birth
  • Manzanares S, López M, Redondo P, Garrote A, Molina F, Carrillo M, et al. Amenaza de parto prematuro. Valor de la cervicometría y la fibronectina. Actualización Obstetricia y Ginecología. 2009. [Acceso 05 de mayo de 2019]. https://www.huvn.es/asistencia_sanitaria/ginecologia_y_obstetricia
  • Reyna-Villasmil E, Mejia-Montilla J, Reyna-Villasmil N, Torres-Cepeda D, Santos-Bolívar J, Fernández-Ramírez A. Interleucina 6 cervicovaginal en la predicción de parto pretérmino. Rev. peru. ginecol. obstet. [Internet]. 2016 Abr [citado 2019 Nov 25]; 62(2): 175-181. http://www.scielo.org.pe/scielo.php?script=sci_arttext&pid=S2304-51322016000200005&lng=es
  • Rondón-Tapia M, Reyna-Villasmil E, Mejía Montilla J, Reyna-Villasmil N, Torres-Cepeda D, et al. Prolactina cervicovaginal en la predicción de parto pretérmino. Repert. Med. Cir. [Internet]. 2018 [citado 2019 Nov 24]; 27(1), 18-23. https://www.fucsalud.edu.co/sites/default/files/2018-07/Prolactina-cervicovaginal.pdf
  • Vasak B, Graatsma E, Hekman-Drost E, Eijkemans M, van Leeuwen J, Visser G, Jacod B. Uterine electromyography for identification of first-stage labor arrest in term nulliparous women with spontaneous onset of labor. Am J Obstet Gynecol MFM. [Internet] 2013 [citado 2019 Nov 25]; 209(3), 232.e1-8. DOI: 10.1016/j.ajog.2013.05.056
  • 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), 911-922. DOI: 10.1007/s11517-008-0350-y
  • Constantino MA. Análisis de la evolución del grado de sincronización de las señales mioeléctricas uterinas durante la gestación. [Trabajo de Grado]. Universitat Politècnica de València. Departamento de Ingeniería Electrónica, España. 2017. https://riunet.upv.es/handle/10251/85579
  • Pervolaraki E, Holden AV. Spatiotemporal patterning of uterine excitation patterns in human labour. Biosystems. [Internet] 2013 [citado 2019 Nov 24]; 112(2): 63-72. DOI: 10.1016/j.biosystems.2013.03.012
  • Asmi, S, Subramaniam K, Iqbal NV. A review of significant researches on prediction of preterm birth using uterine electromyogram signal. Future Gener Comput Syst. [Internet] 2019 [citado 2019 Nov 23]; 9: 135-143. Disponible en: DOI: 10.1155/2019/3168541
  • Ye-Lin Y, Garcia-Casado J, Prats-Boluda G, Alberola-Rubio J, Perales A. Automatic Identification of Motion Artifacts in EHG Recording for Robust Analysis of Uterine Contractions. Comput Math Methods Med. [Internet] 2014; 11 [citado 2019 Nov 25]. DOI: 10.1155/2014/470786
  • Vasak B, Graatsma E, Hekman-Drost E, Eijkemans M, van Leeuwen J, Visser G, Jacod B. Uterine electromyography for identification of first-stage labor arrest in term nulliparous women with spontaneous onset of labor. Am J Obstet Gynecol MFM. [Internet] 2013 [citado 2019 Nov 22]; 209(3), 232.e1-8. DOI: 10.1016/j.ajog.2013.05.056
  • Ren P, Yao S, Li J, Valdes-Sosa PA, Kendrick KM. Improved Prediction of Preterm Delivery Using Empirical Mode Decomposition Analysis of Uterine Electromyography Signals. PLoS ONE. [Internet] 2015 [citado 2019 Nov 25]; 10(7): e0132116. DOI: 10.1371/journal.pone.0132116
  • Lucovnik M, Kuon RJ, Garfield RE. Research Article Assessment of Parturition with Cervical Light-Induced Fluorescence and Uterine Electromyography. Computational and Mathematical Methods in Medicine. [Internet]. 2013 [citado 2019 Nov 26]; 165913. DOI: 10.1155/2013/165913
  • Vlemminx MW, Thijssen KM, Bajlekov GI, Dieleman JP, Van Der Hout-Van M B, Oei SE. Electrohysterography for uterine monitoring during term labour compared to external tocodynamometry and intra-uterine pressure catheter. Eur J Obstet Gynecol Reprod Biol X. [Internet] 2017; [citado 2019 Nov 25], 197-205. DOI: 10.1016/j.ejogrb.2017.05.027
  • Alberola-Rubio J, Prats-Boluda G, Ye-Lin Y, Valero J, Perales A, Garcia-Casado J. Comparison of non-invasive electrohysterographic recording techniques for monitoring uterine dynamics. Med. eng. phys. [Internet] 2013 [citado 2019 Nov 22]; 35(12): 1736-1743. D DOI: 10.1016/j.medengphy.2013.07.008
  • Cohen WR. Clinical assessment of uterine contractions. Int J Gynecol Obstet. [Internet] 2017 [citado 2019 Nov 23]; 139(2), 137-142. DOI: 10.1002/ijgo.12270
  • Euliano TY, Tam Nguyen M, Darmanjian S, McGorray SP, Euliano N, Onkala A, Gregg AR. Monitoring uterine activity during labor: a comparison of 3 methods. Am J Obstet Gynecol. [Internet]. 2013 [citado 2019 Nov 25]; 208: 66.e1-6. Disponible en: DOI: 10.1016/j.ajog.2012.10.873
  • Kuijsters N, Rabotti C, Sammali F, Mischi M, Schoot B. Automatic Contraction Detection During the Menstrual Cycle By Electrohysterography. J. minim. invasive gynecol. [Internet] 2015 [citado 2019 Nov 22]; 22(6), S101. DOI: 10.1016/j.jmig.2015.08.272
  • Suarez-Torres I, Reyna-Villasmil E, Mejia-Montilla J, Reyna-Villasmil N, Torres-Cepeda D, Santos-Bolívar J, et al. Original: Proteína C reactiva plasmática en el segundo trimestre para predicción de parto pretérmino. Perinatol Reprod Hum. 2016; 30(2), 57-62. DOI: 10.1016/j.rprh.2016.06.008
  • Lucovnik M, Chambliss LR, Blumrick R, Balducci J, Gersak K, Garfield RE. Effect of obesity on preterm delivery prediction by transabdominal recording of uterine electromyography. Taiwan J Obstet Gynecol. 2016; 55(5): 692-696. DOI: 10.1016/j.tjog.2015.05.005
  • Euliano TY, Nguyen MT, Darmanjian S, Busowski JD, Euliano N, Gregg AR., et al. Monitoring Uterine Activity during Labor: Clinician Interpretation of Electrohysterography versus Intrauterine Pressure Catheter and Tocodynamometry. Am J Perinatol. 2016; 33(9): 831-8. DOI: 10.1055/s-0036-1572425
  • Altini M, Rossetti E, Rooijakkers M, Dy E, Penders J, Grieten L, Gyselaers W. 762: Remote labour detection using maternal electrohysterography and heart rate data acquired with a wearable sensor. Am. j. obstet. gynecol. [Internet]. 2017 [citado 2019 Nov 23]; 216(1), S441-S442. DOI_ 10.1016/j.ajog.2016.11.495
  • Idowu P, Fergus A, Hussain C, Dobbins H, Al-askar P, Fergus A, et al. Técnicas avanzadas de clasificación de redes neuronales artificiales que utilizan EHG para detectar nacimientos prematuros. Conf. Softw inteligente complejo. Syst intensivo. 2014; 8(1).
  • Alamedine D, Marque K, Alamedine D, Khalil M. Binary particle swarm optimization for feature Selection on uterine electrohysterogram signal. 2nd International Conference on Advances in Biomedical Engineering Date of Conference; 11-13 Sept. 2013. Tripoli, Lebanon: IEEE Conference Location. https://ieeexplore.ieee.org/document/6648863/
  • Horoba K, Jezewski J, Matonia A, Wrobel J, Czabanski R, Jezewski M, et al. Early predicting a risk of preterm labour by analysis of antepartum electrohysterograhic signals. Biocybern Biomed Eng. [Internet]. 2016 [citado 2019 Nov 25]; 36(4), 574-583. Disponible en: DOI: 10.1016/j.bbe.2016.06.004.
  • Diab A, Hassan M, Marque C, Karlsson B. Quantitative performance analysis of four methods of evaluating signal nonlinearity: Application to uterine EMG signals. Conf Proc IEEE Eng Med Biol Soc. 2012; 1045-8. DOI: 10.1109/EMBC.2012.6346113
  • Punitha N, Ramakrishnan S. Analysis of uterine electromyography signals in preterm condition using multifractal algorithm. Conf Proc IEEE Eng Med Biol Soc. 2018; 1-4. DOI: 10.1109/EMBC.2018.8512891
  • Sim S, Ryou H, Kim H, Han J, Park K. Evaluation of Electrohysterogram Feature Extraction to Classify the Preterm and Term Delivery Groups. In: Goh J. (eds). The 15th International Conference on Biomedical Engineering. IFMBE Proceedings, vol 43. Springer, Cham.
  • Chen L, Hao Y, Hu X. Detection of preterm birth in electrohysterogram signals based on wavelet transform and stacked sparse autoencoder. PLoS ONE. [Internet]. 2019 [citado 2019 Nov. 23]; 14: 1-16. DOI: 10.1371/journal.pone.0214712
  • Lemancewicz A, Kuc P, Doroszkiewicz K, Laudanski P, Jasinska E, Oczeretko E, et al. O389 Early Diagnosis of Threatened Premature Labor by Electrohysterographic Recordings. Int. j. gynecol. obstet., Suppl. [Internet] 2012 [citado 2019 Nov 23]; 119(3), S398. DOI: 10.1016/S0020-7292(12)60819-4
  • Smrdel A, Jager F. Separating sets of term and pre-term uterine EMG records. Physiol Meas. [Internet] 2015 [citado 2019 Nov 22]; 36(2), 341-355. DOI: 10.1088/0967-3334/36/2/341
  • Moslem B, Diab M, Khalil M, Marque C. Combining data fusion with multiresolution analysis for improving the classification accuracy of uterine EMG signals. EURASIP J Adv Signal Process. [Internet]. 2012 [citado 2019 Nov 26]; (1), 167. Disponible en: https://link.springer.com/article/10.1186/1687-6180-2012-167
  • Lemancewicz A, Borowska M, Kuć P, Jasińska E, Laudański P, Laudański T, et al. Early diagnosis of threatened premature labor by electrohysterographic recordings – The use of digital signal processing. Biocybern Biomed Eng. [Internet]. 2016 [citado 2019 No. 23]; 36(1): 302-307. DOI: 10.1016/j.bbe.2015.11.005
  • Happillon T, Muszynski C, Zhang F, Marque C, Istrate D. Detection of Movement Artefacts and Contraction Bursts Using Accelerometer and Electrohysterograms for Home Monitoring of Pregnancy. IRBM. [Internet]. 2018 [citado 2019 Nov 25]; 39(6): 379-385. DOI: 10.1016/j.irbm.2018.10.008
  • 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. [Internet]. 2018 [citado 2019 Nov 23]; 46: 238-248. DOI: 10.1016/j.bspc.2018.07.018
  • Al-Askar H, Radi N, MacDermott A. Chapter 7 - Recurrent Neural Networks in Medical Data Analysis and Classifications. In: D. Al-Jumeily, A. Hussain, C. Mallucci, C. Oliver, Emerging Topics in Computer Science and Applied Computing, Applied Computing in Medicine and Health. 1ra. Ed. Boston: Morgan Kaufmann; 2016.
  • Borowska M, Brzozowska E, Kuć P, Oczeretko E, Mosdorf R, Laudański P. Identification of preterm birth based on RQA analysis of electrohysterograms. Comput Methods Programs Biomed. [Intenet]. 2018 [citado 2019 Nov 24]; (153): 227-236. Disponible en: DOI: 10.1016 / j.cmpb.2017.10.018
  • Sadi-Ahmed N, Kacha B, Taleb H. Relevant Features Selection for Automatic Prediction of Preterm Deliveries from Pregnancy ElectroHysterograhic (EHG) records. J Med Syst. 2017; 41: 204. https://link.springer.com/article/10.1007/s10916-017-0847-8
  • Jezewski J, Matonia A, Czabanski R, Horoba K, Kupka T. Clasificación de los patrones de actividad eléctrica uterina para la detección temprana de nacimientos prematuros. En: Burduk R, Jackowski K, Kurzynski M, Wozniak M, Zolnierek A. (eds). Actas de la 8ª Conferencia Internacional sobre Sistemas de Reconocimiento por Computadora CORES 2013. Avances en Sistemas Inteligentes y Computación, vol 226. Heidelberg: Springer; 2013.
  • Shahbakhti M, Beiramvand M, Bavi MR, Mohammadi S. A New Efficient Algorithm for Prediction of Preterm Labor. 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). Berlin, Germany; 23-27 July 2019. IEEE. https://ieeexplore.ieee.org/document/8857837
  • Hussain AJ, Fergus P, Al-Askar H, Al-Jumeily D, Jager F. Dynamic neural network architecture inspired by the immune algorithm to predict preterm deliveries in pregnant women. Neurocomputing. [Internet]. 2015 [citado 2019 Nov 23]; 151(3): 963-974. DOI: 10.1016/j.neucom.2014.03.087
  • Radomski DS. Reprint of ‘A nonlinear parameterization of multivariate electrohysterographical signals’. Comput. Biol. Med. 2016; 69: 254-260. DOI: 10.1016/j.compbiomed.2016.01.006
  • Al-Jumeily D, Hussain A, Mallucci C, Oliver C. Applied Computing in Medicine and Health. 1ra. Ed. Boston: Morgan Kaufmann; 2016.
  • Haibo H, Bai Y, García EA, Li S. ADASYN: Adaptive Synthetic Sampling Approach for Imbalanced Learning. April 14, 2010; Proc. de IEEE International Joint Conference on Neural Networks; 2008. https://www.ele.uri.edu/faculty/he/PDFfiles/adasyn.pdf
  • Fergus P, Idowu I, Hussain A, Dobbins C. Advanced artificial neural network classification for detecting preterm births using EHG records. Neurocomputing. [Internet]. 2016 [citado 2019 Nov. 23]; 188: 42-49. DOI: 10.1016/j.neucom.2015.01.107
  • Al-Askar H, Radi N, MacDermott A. Chapter 7 - Recurrent Neural Networks in Medical Data Analysis and Classifications. In: D. Al-Jumeily, A. Hussain, C. Mallucci, C. Oliver, Emerging Topics in Computer Science and Applied Computing, Applied Computing in Medicine and Health. 1ra. Ed. Boston: Morgan Kaufmann; 2016.
  • Ha, D, Qiu Q, Zhou X, An Y, Peng J, Yang L, Zheng D. Application of decision tree in determining the importance of surface electrohysterography signal characteristics for recognizing uterine contractions. Biocybern Biomed Eng. 2019; 39(3), 806-813. DOI: 10.1016 / j.bbe.2019.06.008
  • Fergus P, Cheung P, Hussain A, Al-Jumeily D, Dobbins C, Iram S. Prediction of Preterm Deliveries from EHG Signals Using Machine Learning. PLoS ONE. [Internet]. 2013 [citado 2019 Nov 24], 8(10): e77154. DOI: 10.1371/journal.pone.0077154
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Author Biographies

María Fernanda Acosta Romo, Universidad Mariana. San Juan de Pasto, Colombia
MSc in Epidemiology; Postgraduate Health Research Coordinator, member of the GIESUM research group, Faculty of Health Sciences, Mariana University, San Juan de Pasto, Nariño. Email: macosta@umariana.edu.co
Marco Antonio Chamorro Lucero, Universidad Mariana. San Juan de Pasto, Colombia
Master in Engineering, Biomedical Area, GIESUM Research Group. Faculty of Engineering, Mariana University, San Juan de Pasto, Colombia. Email: marcochamorro@umariana.edu.co