PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The prolonged latent phase of Induction of Labour (IOL) is associated with increased risks of maternal mortality and morbidity. Electrohysterography (EHG) has outperformed traditional clinical measures monitoring labour progress. Although parity is agreed to be of particular relevance to the success of IOL, no previous EHG-related studies have been found in the literature. We thus aimed to identify EHG-biomarkers to predict IOL success (active phase of labour in ≤ 24 h) and determine the influence of the myoelectrical response on the parity of this group. Statistically significant and sustained differences between the successful and failed groups were found from 150 min in amplitude and non-linear parameters, especially in Spectral Entropy and in their progression rates. In the nulliparousparous comparison, parous women showed statistically significantly higher amplitude progression rate. These biomarkers would therefore be useful for early detection of the risk of induction failure and would help to develop more robust and generalizable IOL successprediction systems.
Twórcy
  • Centro de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València, Valencia, Spain
  • Servicio de Obstetricia, Hospital Universitari i Politècnic La Fe, Valencia, Spain
autor
  • Centro de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València, Valencia, Spain
  • Centro de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València, Valencia, Spain
  • Servicio de Obstetricia, Hospital Universitari i Politècnic La Fe, Valencia, Spain
  • Centro de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València, Valencia, Spain
  • Servicio de Obstetricia, Hospital Universitari i Politècnic La Fe, Valencia, Spain
  • Centro de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València 46022, Valencia, Spain
Bibliografia
  • [1] World Health Organization. WHO recommendations: Induction of labour at or beyond term. Geneva: 2018.
  • [2] Middleton P, Shepherd E, Crowther CA. Induction of labour for improving birth outcomes for women at or beyond term. Cochrane Database Syst Rev 2018;5:1-114. https://doi.org/ 10.1002/14651858.CD004945.pub4.
  • [3] Ashwal E, Livne MY, Benichou JIC, Unger R, Hiersch L, Aviram A, et al. Contemporary patterns of labor in nulliparous and multiparous women. American Journal of Obstetrics and Gynecology 2020;222:267.E1-267.E9. https://doi.org/10.1016/j. Ajog.2019.09.035.
  • [4] Krogh LQ, Boie S, Henriksen TB, Thornton J, Fuglsang J, Glavind J. Induction of labour at 39 weeks versus expectant management in low-risk obese women: study protocol for a randomised controlled study. BMJ Open 2022;12:e057688.
  • [5] Cans C, Colver A, Krägeloh-Mann I, Platt M-J, de la Cruz J, Curran R, et al. European perinatal health report. Health and care of pregnant women and babies in Europe in 2010. 2013.
  • [6] Benalcazar-Parra C, Ye-Lin Y, Garcia-Casado J, Monfort-Orti R, Alberola-Rubio J, Perales A, et al. Electrohysterographic characterization of the uterine myoelectrical response to labor induction drugs. Med Eng Phys 2018;56:27-35. https:// doi.org/10.1016/j.medengphy.2018.04.002.
  • [7] Batinelli L, Serafini A, Nante N, Petraglia F, Severi FM, Messina G. Induction of labour: clinical predictive factors for success and failure. J Obstet Gynaecol 2018;38:352-8. https://doi.org/10.1080/01443615.2017.1361388.
  • [8] Bakker R, Pierce S, Myers D. The role of prostaglandins E1 and E2, dinoprostone, and misoprostol in cervical ripening and the induction of labor: a mechanistic approach. Arch Gynecol Obstet2 2017;296:167-79. https://doi.org/10.1007/s00404-017-4418-5.
  • [9] Vasist SN, Bhat P, Ulman S, Hebbar H. Identification of contractions from Electrohysterography for prediction of prolonged labor. J Electric Bioimpedance 2022;13:4-9. https:// doi.org/10.2478/JOEB-2022-0002.
  • [10] Wagura P, Wasunna A, Laving A, Wamalwa D, Ng’ang’a P. Prevalence and factors associated with preterm birth at kenyatta national hospital. BMC Pregnancy and Childbirth 2018;18:107. https://doi.org/10.1186/s12884-018-1740-2.
  • [11] Verhoeven CJM, Oudenaarden A, Hermus MAA, Porath MM, Oei SG, Mol BWJ. Validation of models that predict Cesarean section after induction of labor. Ultrasound Obstet Gynecol 2009;34:316-21. https://doi.org/10.1002/uog.7315.
  • [12] Triebwasser JE, Vanartsdalen J, Kobernik EK, Seiler K, Langen ES. Assessing maternal and fetal risks associated with prolonged induction of labor. Am J Perinatol 2019;36:455-9. https://doi.org/10.1055/S-0038-1675642/ID/JR180255-14.
  • [13] Nicholson G, Cyr PL. Cost of failed labor induction: a Us hospital perspective. Value Health 2013;16:A75. https://doi. org/10.1016/J.JVAL.2013.03.339.
  • [14] Kaimal AJ, Little SE, Odibo AO, Stamilio DM, Grobman WA, Long EF, et al. Cost-effectiveness of elective induction of labor at 41 weeks in nulliparous women. Am J Obstet Gynecol 2011;204(137):e1-9. https://doi.org/10.1016/j.ajog.2010.08.012.
  • [15] Marconi AM. Recent advances in the induction of labor. F1000Research 2019;8:1-11. https://doi.org/10.12688/ f1000research.17587.1.
  • [16] Pitarello P da RP, Tadashi Yoshizaki C, Ruano R, Zugaib M. Prediction of successful labor induction using transvaginal sonographic cervical measurements. Journal of Clinical Ultrasound 2012;41:76-83. https://doi.org/10.1002/jcu.21929.
  • [17] Delvin Anggriani D, Herawati L, Ernawati. Parity as failure determinants of labor induction in Bangka Belitung. Materia Obstetrics & Gynecology 2016;24:79-83. https://doi.org/ 10.20473/mog.V24I32016.79-83.
  • [18] Friedman EA. Primigravid labor. Obstet Gynecol 1955;6:567-89. https://doi.org/10.1097/00006250-195512000-00001.
  • [19] Friedman E. Labor in multiparas: a graphicostatistical analysis. Obstet Gynecol 1956;8:691-703.
  • [20] Kandemir O, Dede H, Yalvac S, Aldemir O, Yirci B, Yerebasmaz N, et al. The effect of parity on labor induction with prostaglandin E2 analogue (Dinoprostone): an evaluation of 2090 cases. J Preg Child Health 2015;2:1-5. https://doi.org/10.4172/2376-127X.1000149.
  • [21] Tan PC, Othman A, Win ST, Hong JGS, Elias N, Omar SZ. Induction of labour from 39 weeks in low-risk multiparas with ripe cervixes: a randomised controlled trial. Aust N Z J Obstet Gynaecol 2021;61:882-90. https://doi.org/10.1111/AJO.13377.
  • [22] Caughey AB, Cahill AG, Guise J-M, Rouse DJ. Safe prevention of the primary cesarean delivery. Am J Obstet Gynecol 2014;210:179-93. https://doi.org/10.1016/j.ajog.2014.01.026.
  • [23] Wormer KC, Bauer A, Williford AE. Bishop Score 2021. https:// www.ncbi.nlm.nih.gov/books/NBK470368/ (accessed September 15, 2022).
  • [24] Kolkman DGE, Verhoeven CJM, Brinkhorst SJ, Van Der Post JAM, Pajkrt E, Opmeer BC, et al. The bishop score as a predictor of labor induction success: a systematic review. Am J Perinatol 2013;30:625-30. https://doi.org/10.1055/S-0032- 1331024/ID/JR12M0145-39.
  • [25] Bastani P, Hamdi K, Abasalizadeh F, Pourmousa P, Ghatrehsamani F. Transvaginal ultrasonography compared with Bishop score for predicting cesarean section after induction of labor. Int J Women’s Health 2011;3:277-80. https://doi.org/10.2147/IJWH.S20387.
  • [26] Prado CA de C, Araujo Júnior E, Duarte G, Quintana SM, Tonni G, Cavalli R de C, et al. Predicting success of labor induction in singleton term pregnancies by combining maternal and ultrasound variables. The Journal of Maternal-Fetal & Neonatal Medicine : The Official Journal of the European Association of Perinatal Medicine, the Federation of Asia and Oceania Perinatal Societies, the International Society of Perinatal Obstetricians 2016;29:3511-8. https://doi.org/ 10.3109/14767058.2015.1135124.
  • [27] Xu J, Chen Z, Lou H, Shen G, Pumir A. Review on EHG signal analysis and its application in preterm diagnosis. Biomed Signal Process Control 2022;71. https://doi.org/10.1016/J. BSPC.2021.103231 103231.
  • [28] Mas-Cabo J, Ye-Lin Y, Garcia-Casado J, Díaz-Martinez A, Perales-Marin A, Monfort-Ortiz R, et al. Robust characterization of the uterine myoelectrical activity in different obstetric scenarios. Entropy 2020;22:743. https://doi. org/10.3390/e22070743.
  • [29] Zhang Y, Hao D, Yang L, Zhou X, Ye-Lin Y, Yang Y. Assessment of Features between Multichannel Electrohysterogram for Differentiation of Labors. Sensors 2022, Vol 22, Page 3352 2022;22:3352. https://doi.org/10.3390/S22093352.
  • [30] Terrien J, Marque C, Gondry J, Steingrimsdottir T, Karlsson B. Uterine electromyogram database and processing function interface: An open standard analysis platform for electrohysterogram signals. Comput Biol Med 2010;40:223-30. https://doi.org/10.1016/J.COMPBIOMED.2009.11.019.
  • [31] Song X, Qiao X, Hao D, Yang L, Zhou X, Xu Y, et al. Automatic recognition of uterine contractions with electrohysterogram signals based on the zero-crossing rate. Sci Rep 2021;11:1-10. https://doi.org/10.1038/s41598-021-81492-1.
  • [32] Mas-Cabo J, Prats-Boluda G, Perales A, Garcia-Casado J, Alberola-Rubio J, Ye-Lin Y. Uterine electromyography for discrimination of labor imminence in women with threatened preterm labor under tocolytic treatment. Med Biol Eng Compu 2019;57:401-11. https://doi.org/10.1007/s11517-018-1888-y.
  • [33] Hao D, Qiu Q, Zhou X, An Y, Peng J, Yang L, et al. Application of decision tree in determining the importance of surface electrohysterography signal characteristics for recognizing uterine contractions. Biocybernet Biomed Eng 2019;39:806-13. https://doi.org/10.1016/J.BBE.2019.06.008.
  • [34] 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. Ann Int Conf IEEE Eng Med Biol - Proc 2007;54:1872-5. https://doi.org/ 10.1109/IEMBS.2007.4352680.
  • [35] 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 2018;46:238-48. https://doi.org/10.1016/j.bspc.2018.07.018.
  • [36] García-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;39:1-23. https://doi.org/10.1088/1361-6579/aaad56.
  • [37] Horoba K, Jezewski J, Matonia A, Wrobel J, Czabanski R, Jezewski M. Early predicting a risk of preterm labour by analysis of antepartum electrohysterograhic signals. Biocybernet Biomed Eng 2016;36:574-83. https://doi.org/ 10.1016/J.BBE.2016.06.004.
  • [38] 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. Biocybernet Biomed Eng 2016;36:302-7. https://doi.org/10.1016/J.BBE.2015.11.005.
  • [39] Asmi PS, Subramaniam K, Iqbal NV. Classification of fractal features of uterine EMG signal for the prediction of preterm birth. Biomed Pharmacol J 2018;11:369-74. https://doi.org/ 10.13005/BPJ/1381.
  • [40] Benalcazar-Parra C, Ye-Lin Y, Garcia-Casado J, Monfort-Ortiz R, Alberola-Rubio J, Perales A, et al. Prediction of labor induction success from the uterine electrohysterogram. Hindawi J Sensors 2019;2019:1-12. https://doi.org/10.1155/ 2019/6916251.
  • [41] Mischi M, Chen C, Ignatenko T, De Lau H, Ding B, Guid Oei SG, et al. Dedicated entropy measures for early assessment of pregnancy progression from single-channel electrohysterography. IEEE Trans Biomed Eng 2018;65:875-84. https://doi.org/10.1109/TBME.2017.2723933.
  • [42] Chen L, Hao Y, Hu X. Detection of preterm birth in electrohysterogram signals based on wavelet transform and stacked sparse autoencoder. PLoS One 2019;14:e0214712.
  • [43] Prats-Boluda G, Pastor-Tronch J, Garcia-Casado J, Monfort-Ortíz R, Perales Marín A, Diago V, et al. Optimization of imminent labor prediction systems in women with threatened preterm labor based on electrohysterography. Sensors 2021;21:2496. https://doi.org/10.3390/S21072496.
  • [44] Mohammadi Far S, Beiramvand M, Shahbakhti M, Augustyniak P. Prediction of preterm delivery from unbalanced EHG database. Sensors 2022;22:1507. https://doi. org/10.3390/S22041507.
  • [45] Saleem S, Saeed A, Usman S, Ferzund J, Arshad J, Mirza J, et al. Granger causal analysis of electrohysterographic and tocographic recordings for classification of term vs. preterm births. Biocybernet Biomed Eng 2020;40:454-67. https://doi. org/10.1016/J.BBE.2020.01.007.
  • [46] Mas-Cabo J, Prats-Boluda G, Garcia-Casado J, Alberola-Rubio J, Perales A, Ye-Lin Y. Design and assessment of a robust and generalizable expert system for the prediction of premature birth by means of multi-channel electrohysterographic records. J Title: J Sensors 2019;7:1-13. https://doi.org/10.1155/ 2019/5373810.
  • [47] 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 2013;8:1-16. https://doi.org/ 10.1371/journal.pone.0077154.
  • [48] Idowu IO, Fergus P, Hussain A, Dobbins C, Al-Askar H. Advance artificial neural network classification techniques using EHG for detecting preterm births. Proceedings - 2014 8th International Conference on Complex, Intelligent and Software Intensive Systems, CISIS 2014 2014:95-100. https://doi.org/10.1109/CISIS.2014.14.
  • [49] Sadi-Ahmed N, Kacha B, Taleb H, Kedir-Talha M. Relevant features selection for automatic prediction of preterm deliveries from pregnancy ElectroHysterograhic (EHG) records. J Med Syst 2017;41:204. https://doi.org/10.1007/S10916-017-0847-8.
  • [50] Muszynski C, Happillon T, Azudin K, Tylcz JB, Istrate D, Marque C. Automated electrohysterographic detection of uterine contractions for monitoring of pregnancy: feasibility and prospects. BMC Pregnancy Childbirth 2018;18:1-8. https://doi.org/10.1186/S12884-018-1778-1/TABLES/2.
  • [51] Jager F, Libenšek S, Geršak K. Characterization and automatic classification of preterm and term uterine records. PLoS One 2018;13:e0202125.
  • [52] You J, Kim Y, Seok W, Lee S, Sim D, Park KS, et al. Multivariate time-frequency analysis of electrohysterogram for classification of term and preterm labor. J Electr Eng Technol 2019;14:897-916. https://doi.org/10.1007/s42835-019-00118-9.
  • [53] Allahem H, Sampalli S. Automated uterine contractions pattern detection framework to monitor pregnant women with a high risk of premature labour. Inf Med Unlocked 2020;20. https://doi.org/10.1016/J.IMU.2020.100404 100404.
  • [54] Peng J, Hao D, Yang L, Du M, Song X, Jiang H, et al. Evaluation of electrohysterogram measured from different gestational weeks for recognizing preterm delivery: a preliminary study using random Forest. Biocybernet Biomed Eng 2020;40:352-62. https://doi.org/10.1016/J.BBE.2019.12.003.
  • [55] Xu J, Chen Z, Lu Y, Yang X, Pumir A. Improved Preterm Prediction Based on Optimized Synthetic Sampling of EHG Signal 2020. https://doi.org/https://doi.org/10.48550/ arXiv.2007.01447.
  • [56] Nieto-Del-amor F, Prats-Boluda G, Martinez-De-juan JL, DiazMartinez A, Monfort-Ortiz R, Diago-Almela VJ, et al. Optimized feature subset selection using genetic algorithm for preterm labor prediction based on electrohysterography. Sensors 2021;21:3350. https://doi.org/10.3390/S21103350.
  • [57] Lou H, Liu H, Chen Z, Zhen Z, Dong B, Xu J. Bio-process inspired characterization of pregnancy evolution using entropy and its application in preterm birth detection. Biomed Signal Process Control 2022;75. https://doi.org/ 10.1016/J.BSPC.2022.103587 103587.
  • [58] Allahem H, Sampalli S. Automated labour detection framework to monitor pregnant women with a high risk of premature labour using machine learning and deep learning. Inf Med Unlocked 2022;28. https://doi.org/10.1016/J. IMU.2021.100771 100771.
  • [59] Aviram A, Melamed N, Hadar E, Raban O, Hiersch L, Yogev Y. Effect of prostaglandin E2 on myometrial electrical activity in women undergoing induction of labor. Am J Perinatol 2014;31:413-8. https://doi.org/10.1055/S-0033-1352486.
  • [60] Toth T. Transcutaneous electromyography of uterus in prediction of labor outcome induced by oxytocine and prostaglandine shapes. Gynaecol Perinatol 2005;14:75-85.
  • [61] Xu Y, Hao D, Taggart MJ, Zheng D. Regional identification of information flow termination of electrohysterographic signals: towards understanding human uterine electrical propagation. Comput Methods Programs Biomed 2022;223. https://doi.org/10.1016/J.CMPB.2022.106967 106967.
  • [62] Rabotti C, Mischi M, Van Laar JOEH, Oei GS, Bergmans JWM. Estimation of internal uterine pressure by joint amplitude and frequency analysis of electrohysterographic signals. Physiol Meas 2008;29:829-41. https://doi.org/10.1088/0967- 3334/29/7/011.
  • [63] Ye-Lin Y, Bueno-Barrachina JM, Prats-boluda G, Rodriguez de Sanabria R, Garcia-Casado J. Wireless sensor node for noninvasive high precision electrocardiographic signal acquisition based on a multi-ring electrode. Measurement 2017;97:195-202. https://doi.org/10.1016/J.MEASUREMENT.2016.11.009.
  • [64] Diaz-Martinez A, Monfort-Ortiz R, Ye-Lin Y, Garcia-Casado J, Nieto-Del-Amor F, Diago-Almela VJ, et al. Comparative study of uterine myoelectrical response to labour induction drugs in nulliparous and parous women with different EHG analysis techniques. Int Conf E-Health Bioeng (EHB) 2021;2021:1-4. https://doi.org/10.1109/EHB52898.2021.9657548.
  • [65] 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 2013;35:1736-43. https://doi. org/10.1016/j.medengphy.2013.07.008.
  • [66] Xu J, Wang M, Zhang J, Chen Z, Huang W, Shen G, et al. Network theory based EHG signal analysis and its application in preterm prediction. IEEE J Biomed Health Inform 2022;26:2876-87. https://doi.org/10.1109/JBHI.2022.3140427.
  • [67] Talebinejad M, Chan ADC, Miri A. A Lempel-Ziv complexity measure for muscle fatigue estimation. J Electromyogr Kinesiol : Off J Int Soc Electrophysiol Kinesiol 2011;21:236-41. https://doi.org/10.1016/j.jelekin.2010.12.003.
  • [68] Kesić S, Spasić SZ. Application of Higuchi’s fractal dimension from basic to clinical neurophysiology: a review. Comput Methods Programs Biomed 2016;133:55-70. https://doi.org/10.1016/J.CMPB.2016.05.014.
  • [69] Richman JS, Moorman JR. Physiological time-series analysis using approximate and sample entropy. Am J Physiol Heart Circ Physiol 2000;278:2039-49. https://doi.org/10.1152/ ajpheart.2000.278.6.H2039.
  • [70] Fele-Žorž G, Kavšek G, Novak-Antolič Ž , 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 Compu 2008;46:911-22. https:// doi.org/10.1007/s11517-008-0350-y.
  • [71] Delgado-Bonal A, Marshak A. Entropy approximate entropy and sample entropy: a comprehensive tutorial. Entropy 2019;21:541. https://doi.org/10.3390/e21060541.
  • [72] Devi D, Sophia S, Boselin Prabhu SR. Deep learning-based cognitive state prediction analysis using brain wave signal. Cognitive Comput Hum-Robot Interact: Principles Pract 2021:69-84. https://doi.org/10.1016/B978-0-323-85769-7.00017-3.
  • [73] Shannon CE. A mathematical theory of communication. Bell Syst Tech J 1948;27:379-423. https://doi.org/10.1002/J.1538- 7305.1948.TB01338.X.
  • [74] Lempel A, Ziv J. On the complexity of finite sequences. IEEE Trans Inf Theory 1976;22:75-81. https://doi.org/10.1109/ TIT.1976.1055501.
  • [75] 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 2006;53:2282-8. https://doi.org/10.1109/TBME.2006.883696.
  • [76] Rhomadona SW, Widyawati MN, Suryono S. Monitoring of uterus electrical activities using electromyography in stage I induction labor. J Phys Conf Ser 2018;1179:1-6. https://doi.org/ 10.1088/1742-6596/1179/1/012133.
  • [77] Yount SM, Lassiter N. The pharmacology of prostaglandins for induction of labor. J Midwifery Womens Health 2013;58:133-44. https://doi.org/10.1111/JMWH.12022.
  • [78] Konopka CK, Glanzner WG, Rigo ML, Rovani MT, Comim FV, Gonc¸alves PBD, et al. Responsivity to PGE2 labor induction involves concomitant differential prostaglandin E receptor gene expression in cervix and myometrium. Genet Mol Res: GMR 2015;14:10877-87. https://doi.org/10.4238/2015. SEPTEMBER.9.25.
  • [79] Roos N, Blesson CS, Stephansson O, Masironi B, Vladic Stjernholm Y, Ekman-Ordeberg G, et al. The expression of prostaglandin receptors EP3 and EP4 in human cervix in postterm pregnancy differs between failed and successful labor induction. Acta Obstet Gynecol Scand 2014;93:159-67. https:// doi.org/10.1111/AOGS.12300.
  • [80] Indraccolo U, Scutiero G, Greco P. Sonographic cervical shortening after labor induction is a predictor of vaginal delivery. Revista Brasileira de Ginecologia e Obstetricia 2016;38:585-8. https://doi.org/10.1055/s-0036-1597629.
  • [81] Juhasova J, Kreft M, Zimmermann R, Kimmich N. Impact factors on cervical dilation rates in the first stage of labor. J Perinat Med 2018;46:59-66. https://doi.org/10.1515/jpm-2016- 0284.
  • [82] Ryan GA, Nicholson SM, Crankshaw DJ, Morrison JJ. Maternal parity and functional contractility of human myometrium in vitro in the third trimester of pregnancy. J Perinatol 2019;39:439-44. https://doi.org/10.1038/s41372-019-0312-2.
  • [83] Gerli S, Favilli A, Giordano C, Bini V, Di Renzo GC. Single indications of induction of labor with prostaglandins and risk of cesarean delivery: a retrospective cohort study. J Obstet Gynaecol Res 2013;39:926-31. https://doi.org/10.1111/JOG.12000.
  • [84] Ryan GA, Crankshaw DJ, Morrison JJ. Effects of maternal parity on response of human myometrium to oxytocin and ergometrine in vitro. Eur J Obstet Gynecol Reprod Biol 2019;242:99-102. https://doi.org/10.1016/j.ejogrb.2019.09.006.
  • [85] Benalcazar-Parra C, Garcia-Casado J, Ye-Lin Y, Alberola-Rubio J, Lopez Á, Perales-Marin A, et al. New electrohysterogram-based estimators of intrauterine pressure signal, tonus and contraction peak for non-invasive labor monitoring. Physiol Meas 2019;40:1-24. https://doi.org/10.1088/1361-6579/ab37db.
  • [86] Feola A, Abramowitch S, Jones K, Stein S, Moalli P. Parity negatively impacts vaginal mechanical properties and collagen structure in rhesus macaques. Am J Obstet Gynecol 2010;203(595):e1-8. https://doi.org/10.1016/J. AJOG.2010.06.035.
  • [87] Prevost M, Zelkowitz P, Tulandi T, Hayton B, Feeley N, Carter CS, et al. Oxytocin in pregnancy and the postpartum: relations to labor and its management. Front Public Health 2014;2:1-9. https://doi.org/10.3389/FPUBH.2014.00001/BIBTEX.
  • [88] Garfield RE, Maner WL. Physiology and electrical activity of uterine contractions. Semin Cell Dev Biol 2007;18:289-95. https://doi.org/10.1016/j.semcdb.2007.05.004.
  • [89] Garfield RE, Blennerhassett MG, Miller SM. Control of myometrial contractility: role and regulation of gap junctions. Oxf Rev Reprod Biol 1988;10:436-90.
  • [90] Govindan RB, Siegel E, McKelvey S, Murphy P, Lowery CL, Eswaran H. Tracking the changes in synchrony of the electrophysiological activity as the uterus approaches labor using magnetomyographic technique. Reprod Sci 2015;22:595-601. https://doi.org/10.1177/1933719114556484.
  • [91] Mas-Cabo J, Prats-Boluda G, Ye-Lin Y, Alberola-Rubio J, Perales A, Garcia-Casado J. Characterization of the effects of Atosiban on uterine electromyograms recorded in women with threatened preterm labor. Biomed Signal Process Control 2019;52:198-205. https://doi.org/10.1016/j.bspc.2019.04.001.
  • [92] 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 2014;2014:1-11. https://doi.org/10.1155/ 2014/470786.
  • [93] Kagwisage J, S Balandya B, B Pembe A, GM Mujinja P. Health Related Quality of Life Post Labour Induction with Misoprostol Versus Dinoprostone At Muhimbili National Hospital in Dar Es Salaam, Tanzania: A cross Sectional Study. The East African Health Research Journal 2020;4:58-64. https://doi.org/10.24248/EAHRJ.V4I1.622.
Uwagi
PL
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023)
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-d411a009-fb11-4422-9041-ea67df13098c
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.