Assessment of Features between Multichannel Electrohysterogram for Differentiation of Labors [PDF]
Sensors, 2022 Electrohysterogram (EHG) is a promising method for noninvasive monitoring of uterine electrical activity. The main purpose of this study was to characterize the multichannel EHG signals to distinguish between term delivery and preterm birth, as well as ...
Yajun Zhang, Dongmei Hao, Xiya Zhou
exaly +10 more sources
Predicting preterm births from electrohysterogram recordings via deep learning. [PDF]
PLoS ONE, 2023 About one in ten babies is born preterm, i.e., before completing 37 weeks of gestation, which can result in permanent neurologic deficit and is a leading cause of child mortality. Although imminent preterm labor can be detected, predicting preterm births
Uri Goldsztejn, Arye Nehorai
doaj +6 more sources
Electrohysterogram for ANN-Based Prediction of Imminent Labor in Women with Threatened Preterm Labor Undergoing Tocolytic Therapy [PDF]
Sensors, 2020 Threatened preterm labor (TPL) is the most common cause of hospitalization in the second half of pregnancy and entails high costs for health systems. Currently, no reliable labor proximity prediction techniques are available for clinical use.
Gema Prats-Boluda +2 more
exaly +7 more sources
An open dataset with electrohysterogram records of pregnancies ending in induced and cesarean section delivery [PDF]
Scientific Data, 2023 The existing non-invasive automated preterm birth prediction methods rely on the use of uterine electrohysterogram (EHG) records coming from spontaneous preterm and term deliveries, and are indifferent to term induced and cesarean section deliveries.
Franc Jager
doaj +4 more sources
Automatic recognition of uterine contractions with electrohysterogram signals based on the zero-crossing rate [PDF]
Scientific Reports, 2021 Uterine contraction (UC) is an essential clinical indicator in the progress of labour and delivery. Electrohysterogram (EHG) signals recorded on the abdomen of pregnant women reflect the uterine electrical activity.
Xiaoxiao Song +6 more
doaj +5 more sources
Evaluation of electrohysterogram measured from different gestational weeks for recognizing preterm delivery: a preliminary study using random Forest [PDF]
Biocybernetics and Biomedical Engineering, 2020 Developing a computational method for recognizing preterm delivery is important for timely diagnosis and treatment of preterm delivery. The main aim of this study was to evaluate electrohysterogram (EHG) signals recorded at different gestational weeks ...
Jin Peng, Dongmei Hao, Lin Yang
exaly +4 more sources
Enhancing classification of preterm-term birth using continuous wavelet transform and entropy-based methods of electrohysterogram signals [PDF]
Frontiers in Endocrinology, 2023 IntroductionDespite vast research, premature birth's electrophysiological mechanisms are not fully understood. Prediction of preterm birth contributes to child survival by providing timely and skilled care to both mother and child.
Héctor Romero-Morales +6 more
doaj +3 more sources
Recognition of uterine contractions with electrohysterogram and exploring the best electrode combination. [PDF]
Technol Health Care, 2022 BACKGROUND: As an essential indicator of labour and delivery, uterine contraction (UC) can be detected by manual palpation, external tocodynamometry and internal uterine pressure catheter. However, these methods are not applicable for long-term monitoring.
Du M +5 more
europepmc +5 more sources
ETRI Journal, 2022 Signal decomposition is a computational technique that dissects a signal into its constituent components, providing supplementary information. In this study, the capability of two common signal decomposition techniques, including wavelet-based and ...
Suparerk Janjarasjitt
exaly +3 more sources
IEEE AccessThere are many difficulties in managing and detecting preterm pregnancies, especially in the early stages. Analyzing electrohysterogram data, which show the electrical activity of uterine muscles, is a promising non-invasive method for classifying term ...
Muhammad Omar Cheema +2 more
exaly +4 more sources