Prediction of Labor Induction Success from the Uterine Electrohysterogram [PDF]
Journal of Sensors, 2019 Pharmacological agents are often used to induce labor. Failed inductions are associated with unnecessarily long waits and greater maternal-fetal risks, as well as higher costs. No reliable models are currently able to predict the induction outcome from common obstetric data (area under the ROC curve (AUC) between 0.6 and 0.7). The aim of this study was
Carlos Benalcazar-Parra +6 more
semanticscholar +8 more sources
Development of Electrohysterogram Recording System for Monitoring Uterine Contraction. [PDF]
J Healthc Eng, 2019 Uterine contraction (UC) is an important clinical indictor for monitoring uterine activity. The purpose of this study is to develop a portable electrohysterogram (EHG) recording system (called PregCare) for monitoring UCs with EHG signals. The PregCare consisted of sensors, a signal acquisition device, and a computer with application software.
Hao D +5 more
europepmc +6 more sources
Preliminary Study on the Efficient Electrohysterogram Segments for Recognizing Uterine Contractions with Convolutional Neural Networks. [PDF]
Biomed Res Int, 2019 Background Uterine contraction (UC) is the tightening and shortening of the uterine muscles which can indicate the progress of pregnancy towards delivery.
Peng J +5 more
europepmc +4 more sources
Comparison of Oxytocin vs. Carbetocin Uterotonic Activity after Caesarean Delivery Assessed by Electrohysterography: A Randomised Trial [PDF]
Sensors, 2022 Electrohysterography has been used for monitoring uterine contractility in pregnancy and labour. Effective uterine contractility is crucial for preventing postpartum haemorrhage. The objective of our study was to compare postpartum electrohysterograms in
Ivana Paljk Likar +5 more
doaj +3 more sources
Detection of preterm birth in electrohysterogram signals based on wavelet transform and stacked sparse autoencoder. [PDF]
PLoS ONE, 2019 Based on electrohysterogram, this paper designed a new method using wavelet-based nonlinear features and stacked sparse autoencoder for preterm birth detection.
Lili Chen, Yaru Hao, Xue Hu
doaj +3 more sources
Prediction of Preterm Labor from the Electrohysterogram Signals Based on Different Gestational Weeks
Sensors, 2023 Timely preterm labor prediction plays an important role for increasing the chance of neonate survival, the mother’s mental health, and reducing financial burdens imposed on the family.
Somayeh Mohammadi Far +3 more
doaj +3 more sources
Nifedipine-Induced Changes in the Electrohysterogram of Preterm Contractions: Feasibility in Clinical Practice [PDF]
Obstetrics and Gynecology International, 2010 Objective. Evaluating changes in the power spectral density (PSD) peak frequency of the electrohysterogram (EHG) caused by nifedipine in women with preterm contractions. Methods.
Maartje P. G. C. Vinken +4 more
doaj +5 more sources
Objective and comprehensive characterization of uterine peristaltic activity throughout the menstrual cycle by means of intracavitary electrohysterography, a cohort study [PDF]
Acta Obstetricia et Gynecologica ScandinavicaIntroduction The uterus exhibits dynamic peristaltic activity across the menstrual cycle, playing a critical role in reproductive processes such as sperm transport and embryo implantation.
Jose Alberola‐Rubio +8 more
doaj +3 more sources
Toward a new modality for detecting a uterine rupture: electrohysterogram propagation analysis during trial of labor after cesarean [PDF]
Journal of Maternal-Fetal and Neonatal Medicine, 2017 Objective: Observational cohort study which aimed to explore the potential of electrohysterogram (EHG) analysis for detecting a uterine rupture during trial of labor after cesarean.
Hinke De Lau +2 more
exaly +3 more sources
Evaluation of convolutional neural network for recognizing uterine contractions with electrohysterogram [PDF]
Computers in Biology and Medicine, 2019 Uterine contraction (UC) activity is commonly used to monitor the approach of labour and delivery. Electrohysterograms (EHGs) have recently been used to monitor UC and distinguish between efficient and inefficient contractions. In this study, we aimed to identify UC in EHG signals using a convolutional neural network (CNN).
Dongmei Hao, Jin Peng, Juntao Liu
exaly +4 more sources