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Federación Mexicana de Ginecología y Obstetricia, A.C.

2019, Número 01

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Ginecol Obstet Mex 2019; 87 (01)


Utilidad de la electrohisterografía como técnica de monitorización uterina en el ámbito clínico: revisión bibliográfica

Escalante-Gaytán J, Esquivel-Arizmendi CG, Ledesma-Ramírez CI, Pliego-Carrillo AC, García-González MT, Reyes-Lagos JJ

Idioma: Español
Referencias bibliográficas: 71
Paginas: 46-59
Archivo PDF: 611.03 Kb.


RESUMEN

Antecedentes: El análisis del registro de superficie de la actividad mioeléctrica uterina, o electrohisterograma, es uno de los marcadores biofísicos más prometedores para evaluar las contracciones y el estado electrofisiológico del útero. A pesar de las evidencias derivadas de la información clínica que proporciona el análisis electrohisterográfico, hasta la fecha no se ha logrado el esfuerzo significativo para introducir esta técnica en la práctica médica.
Objetivo: Mostrar la evidencia disponible acerca de la utilidad de la electrohisterografía como técnica alternativa para la monitorización de la actividad uterina en el ámbito clínico.
Metodología: Búsqueda bibliográfica en las bases de datos de PubMed, Google Scholar y Scopus, con las palabras clave: electrohysterogram, uterine electromyography y electrohysterography.
Resultados: Se seleccionaron 65 artículos originales, 5 de revisión y 1 capítulo de libro con metodología adecuada, claridad y relevancia clínica, enfocados en la aplicación clínica del electrohisterograma.
Conclusión: Las técnicas de monitoreo convencional de la actividad uterina tienen limitaciones para establecer, oportunamente, el diagnóstico de distocias durante el trabajo de parto. El análisis de registros electrohisterográficos permite explicar las alteraciones detectadas en la actividad eléctrica uterina, mediante el aporte de información del estado funcional, incluso predecir posibles complicaciones durante el trabajo de parto.


REFERENCIAS (EN ESTE ARTÍCULO)

  1. World Health Organization Human. Reproduction Programme, 10 April 2015. WHO Statements on cesarean section rates. Reprod Health Matters 2015;23(45):149-50. doi: 10.1016/j.rhm.2015.07.007

  2. Secretaría de Salud de los Estados Unidos Mexicanos. Norma Oficial Mexicana NOM-007-SSA2-2016, Para la atención de la mujer durante el embarazo, parto y puerperio, y de la persona recién nacida. Norma Oficial Mexicana 2016;1-67. doi: 10.1017/CBO9781107415324.004

  3. INEGI. Encuesta Nacional de la Dinámica Demográfica 2014. Boletín de Prensa núm. 271/15 2015 p. 41.

  4. Guzman E, et al. High Cesarean Section Rates in Latin America, a Reflection of a Different Approach to Labor? Open J Obstet Gynecol 2015;5(5):433-5. doi: 10.4236/ ojog.2015.58062

  5. Euliano TY, et al. Monitoring uterine activity during labor: A comparison of 3 methods. Am J Obstet Gynecol 2013;208(1). doi: 10.1016/j.ajog.2012.10.873

  6. Marque C, et al. Uterine EHG processing for obstetrical monitorng. IEEE Trans Biomed Eng 1986;33(12):1182-7. doi: 10.1109/TBME.1986.325698

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

  8. Bursztyn L, et al. Mathematical model of excitationcontraction in a uterine smooth muscle cell. Am J Physiol Cell Physiol 2007;292(5):C1816-29. doi: 10.1152/ajpcell. 00478.2006

  9. Wray S, et al. Calcium signaling and uterine contractility. J Soc Gynecol Investig 2003;10(5):252-64.

  10. Wray S, Arrowsmith S. Uterine smooth muscle. In: Hill and Olson, editor. Fundamental Biology and Mechanisms of Disease. London: Elsevier; 2012, 1207-1216.

  11. Bett G. Quantitative analysis of uterine action potentials. J Genit Syst Disord 2012;1(1):1000e102. doi: 10.4172/2325- 9728.1000e102

  12. Alvarez H, et al. The normal and abnormal contractile waves of the uterus during labour. Gynecol Obstet Invest 1954;138(2):190-212. doi: 10.1159/000308198

  13. Tong WC, et al. A computational model of the ionic currents, Ca2+ dynamics and action potentials underlying contraction of isolated uterine smooth muscle.[Erratum appears in PLoS One 2011;6(10). doi: 10.1371/annotation/ d317e049-4927-4906-95a5-cd0198a3feb9 Note: Karche. PLoS ONE [Electronic Resour. 2011;6(4):e18685.

  14. Larks SD. The human electrohysterogram: wave forms and implications. Proc Natl Acad Sci USA 1958;44(8):820-4. doi: 10.1073/pnas.44.8.820

  15. Ichijo M, Ujiie Y. Studies on Electrohysterogram. Tohoku J Exp Med 1966;90(1):9-24.

  16. Steer CM, Hertsch GJ. Electrical activity of the human uterus in labor; the electrohysterograph. Am J Obstet Gynecol 1950;59(1):25-40. doi: 10.1016/0002-9378(50)90337-1

  17. Vrhovec J, Macek A. An uterine electromyographic activity as a measure of labor progression. In: Steele C, editor. Applications of EMG in Clinical and Sports Medicine. Croatia: InTech, 2012;243-68. doi: 10.5772/25526

  18. Fele-Žorž G, et al. 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

  19. Terrien J, et al. Spectral characterization of human EHG frequency components based on the extraction and reconstruction of the ridges in the scalogram. Conf Proc IEEE Eng Med Biol Soc 2007;2007;1872-5. doi: 10.1109/ IEMBS.2007.4352680

  20. Schlembach D, et al. Monitoring the progress of pregnancy and labor using electromyography. Eur J Obstet Gynecol Reprod Biol 2009;144(Suppl 1):2-8. doi: 10.1016/j.ejogrb. 2009.02.016

  21. Gondry J, et al. Electrohysterography during pregnancy: Preliminary report. Biomed Instrum Technol 1993;27(4):318-24.

  22. Jain V, et al. Structure and function of the myometrium. Adv Organ Biol 2000;8:215-246. doi: 10.1016/S1569- 2590(00)08009-5

  23. Marshall JM. Regulation of activity in uterine smooth muscle. Physiol Rev Suppl 1962;5:213-27.

  24. Ye-Lin Y, et al. Feasibility and analysis of bipolar concentric recording of electrohysterogram with flexible active electrode. Ann Biomed Eng 2015;43(4):968-76. doi: 10.1007/ s10439-014-1130-5

  25. Ye-Lin Y, et al. Non-invasive electrohysterogram recording using flexible concentric ring electrode. Conf Proc IEEE Eng Med Biol Soc 2014;2014:4050-3. doi: 10.1109/ EMBC.2014.6944513

  26. De Lau H, et al. Study protocol: PoPE-Prediction of preterm delivery by electrohysterography. BMC Pregnancy Childbirth 2014;14;192. doi: 10.1186/1471-2393-14-192

  27. Huang H, et al. An analysis of EMG electrode configuration for targeted muscle reinnervation based neural machine interface. IEEE Trans Neural Syst Rehabil Eng 2008;16(1):37- 45. doi: 10.1109/TNSRE.2007.910282

  28. Garcia-Gonzalez MT, et al. Characterization of EHG contractions at term labor by nonlinear analysis. Conf Proc IEEE Eng Med Biol Soc 2013;7432-5. doi: 10.1109/ EMBC.2013.6611276

  29. Sletten J, et al. Effect of uterine contractions on fetal heart rate in pregnancy: a prospective observational study. Acta Obstet Gynecol Scand 2016;95(10):1129-35. doi: 10.1111/ aogs.12949

  30. Kieser E, et al. Development of a signal processing and feature extraction framework for the safe passage study. 2018 3rd Biennial South African Biomedical Engineering Conference, IEEE Xplore 2018;2018:1-4. doi: 10.1109/ SAIBMEC.2018.8363193

  31. Escalante-Gaytán J, et al. Associations of immunological markers and anthropometric measures with linear and nonlinear electrohysterographic parameters at term active labor. Adv Neuroimmune Biol 2018;7(1):17-26. doi: 10.3233/NIB-170127

  32. Alexandersson A, et al. The Icelandic 16-electrode electrohysterogram database. Sci Data 2015;2:1-9. doi: 10.1038/ sdata.2015.17

  33. Gao P, et al. Comparison of electrohysterogram signal measured by surface electrodes with different designs: A computational study with dipole band and abdomen models. Sci Rep 2017;7(1):17282. doi: 10.1038/s41598- 017-17109-3

  34. Jóhannsdóttir MS. The effect of different electrode design on the electrohysterogram signal the effect of different electrode design on the electrohysterogram signal. University of Iceland; 2015. Dirección URL: .

  35. Lyapina YA, et al. The patterns of changes in the electrohysterogram amplitude characteristics in healthy pregnant women during the third trimester. Hum Physiol 2011;37(2):213-6. doi: 10.1134/S0362119710061040

  36. Devedeux D, et al. Uterine electromyography: A critical review. Am J Obstet Gynecol 1993;169(6):1636-53. https:// doi.org/10.1016/0002-9378(93)90456-S

  37. Garcia-Casado J, et al. Electrohysterography in the diagnosis of preterm birth: A review. Physiol Meas 2018;39(2):02TR01. doi: 10.1088/1361-6579/aaad56

  38. Thongpanja S, et al. Mean and median frequency of emg signal to determine muscle force based on time-dependent power spectrum. Electron Electr Eng 2013;19(3):51-6. doi: 10.5755/j01.eee.19.3.3697

  39. Lemancewicz A, et al. Early diagnosis of threatened premature labor by electrohysterographic recordings. The use of digital signal processing. Biocybern Biomed Eng 2016;36(1):302-7. doi: 10.1016/j.bbe.2015.11.005

  40. Horoba K, et al. Algorithm for Detection of Uterine Contractions. Conf IEEE Eng Med Biol Soc. 2001;2161-4.

  41. Euliano TY, 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

  42. Cohen WR. Clinical assessment of uterine contractions. Int J Gynecol Obstet 2017;139(2). doi: 10.1002/ijgo.12270

  43. McDonald SC, et al. The identification and tracking of uterine contractions using template based cross-correlation. Ann Biomed Eng 2017;45(9):2196-210. doi: 10.1007/ s10439-017-1873-x

  44. Euliano TY, et al. Spatiotemporal electrohysterography patterns in normal and arrested labor. Am J Obstet Gynecol 2009;200(1):54.e1-54.e7. doi: 10.1016/j.ajog.2008.09.008

  45. 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. doi: 10.1016/j. ajog.2013.05.056

  46. Shafik A. Electrohysterogram: Study of the electromechanical activity of the uterus in humans. Eur J Obstet Gynecol Reprod Biol 1997;73(1):85-9. doi: 10.1016/S0301- 2115(97)02727-9

  47. Ye-Lin Y, et al. Prediction of labor using non-invasive laplacian EHG recordings. Proc Annu Int Conf IEEE Eng Med Biol Soc EMBS 2013;7428-31. doi: 10.1109/ EMBC.2013.6611275

  48. Gomez-Lopez N, et al. Immune cells in term and preterm labor. Cell Mol Immunol 2014;11(6):571-81. doi: 10.1038/ cmi.2014.46

  49. Léman H, et al. 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

  50. Acharya UR, et al. 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

  51. Alamedine D, et al. Selection algorithm for parameters to characterize uterine EHG signals for the detection of preterm labor. SIViP 2014;8(6):1169-78. doi: 10.1007/ s11760-014-0655-2

  52. Mischi M, et al. Dedicated entropy measures for early assessment of pregnancy progression from singlechannel electrohysterography. IEEE Trans Biomed Eng 2018;65(4):875-84. doi: 10.1109/TBME.2017.2723933

  53. Di Marco LY, et al. Recurring patterns in stationary intervals of abdominal uterine electromyograms during gestation. Med Biol Eng Comput 2014;52(8):707-16. doi: 10.1007/ s11517-014-1174-6

  54. Jager F, et al. Characterization and automatic classification of preterm and term uterine records. PLoS One 2018;13(8):e0202125. doi: 10.1371/journal.pone.0202125

  55. Fergus P, et al. Prediction of preterm deliveries from EHG signals using machine learning. PLoS One 2013;8(10). doi: 10.1371/journal.pone.0077154

  56. Fergus P, et al. Evaluation of advanced artificial neural network classification and feature extraction techniques for detecting preterm births using EHG records. Intell Conf Intell Comp 2014;309-14. doi: 10.1007/978-3-319- 09330-7_37

  57. Horoba K, et al. Analysis of uterine contractile wave propagation in electrohysterogram for assessing the risk of preterm birth. J Med Imaging Heal Informatics 2015;5(6):1287-94. doi: 10.1166/jmihi.2015.1531

  58. Mas-Cabo J, et al. Uterine electromyography for discrimination of labor imminence in women with threatened preterm labor under tocolytic treatment. Med Biol Eng Comput 2018;29;(in press). doi: 10.1007/s11517-018-1888-y

  59. Alberola-Rubio J, et al. Prediction of labor onset type: Spontaneous vs induced; role of electrohysterography? Comput Methods Programs Biomed 2017;144:127-33. doi: 10.1016/j.cmpb.2017.03.018

  60. Alberola-Rubio J. Estudio electrofisiológico del útero humano durante el embarazo a partir de registros no invasivos del electrohisterograma. Universidad de Valencia; 2017. Dirección URL: .

  61. Giner C. Caracterización de la actividad mioeléctrica uterina durante la inducción del parto . [Valencia, España]: Universidad Politécnica de Valencia; 2016. Dirección URL: .

  62. Rauf Z, et al. Home labour induction with retrievable prostaglandin pessary and continuous telemetric trans-abdominal fetal ECG monitoring. PLoS One 2011;28;6(11):e28129. doi: 10.1371/journal.pone.0028129

  63. Rabotti C, et al. Noninvasive estimation of the electrohysterographic action-potential conduction velocity. IEEE Trans Biomed Eng 2010;57(9):2178-87. doi: 10.1109/ TBME.2010.2049111

  64. Vlemminx MWC, et al. Electrohysterography for uterine monitoring during term labour compared to external tocodynamometry and intra-uterine pressure catheter. Eur J Obstet Gynecol Reprod Biol 2017;215:197-205. doi: 10.1016/j.ejogrb.2017.05.027

  65. Euliano T. Monitoring contractions in obese parturients. Obstet Gynecol 2007;109(5):1136-40.

  66. Jacod BC, et al. A validation of electrohysterography for uterine activity monitoring during labour. J Matern Neonatal Med 2010;23(1):17-22. doi: 10.3109/14767050903156668

  67. Sunwoo N, et al. Vaginal electrohysterography: the design and preliminary evaluation of a novel device for uterine contraction monitoring in an ovine model. J Matern Neonatal Med 2016;29(17):2742-7. doi: 10.3109/14767058.2015.1107538

  68. Euliano T, et al. Prediction of intrauterine pressure waveform from transabdominal electrohysterography. J Matern Neonatal Med 2006;19(12):803-8. doi: 10.1080/14767050601023657

  69. Rabotti C, et al. Estimation of internal uterine pressure by joint amplitude and frequency analysis of electrohysterographic signals. Physiol Meas 2008;29(7):829-41. doi: 10.1088/0967-3334/29/7/011

  70. Jezewski J, et al. 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. doi: 10.1088/0967-3334/26/5/014

  71. Benalcazar-Parra C, et al. Electrohysterographic characterization of the uterine myoelectrical response to labor induction drugs. Med Eng Phys 2018;56:27-35. doi: 10.1016/j. medengphy.2018.04.002




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