ArticleEntropy (Basel, Switzerland)2020
Robust Characterization of the Uterine Myoelectrical Activity in Different Obstetric Scenarios.
Article in Entropy (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it, 26 citations in OpenAlex.
- Objective monitoring of postpartum uterine activity: a systematic scoping review.Frontiers in medicine · 2026Pooled it
- Comparison of Oxytocin vs. Carbetocin Uterotonic Activity after Caesarean Delivery Assessed by Electrohysterography: A Randomised Trial.Sensors (Basel, Switzerland) · 2022Trial
- Exploratory Non-invasive Estimation of Cervical Dilation from Electrohysterography and Maternal Data: A Machine-Learning Proof-of-Concept Study.Annals of biomedical engineering · 2026Article
- A Comprehensive Study of Uterine Muscle Activity During the Third Trimester: Comparison of Singleton and Multiple Gestations.IEEE journal of translational engineering in health and medicine · 2026Article
- An open dataset with electrohysterogram records of pregnancies ending in induced and cesarean section delivery.Scientific data · 2023Article
- Prediction of Preterm Labor from the Electrohysterogram Signals Based on Different Gestational Weeks.Sensors (Basel, Switzerland) · 2023Article
- Combination of Feature Selection and Resampling Methods to Predict Preterm Birth Based on Electrohysterographic Signals from Imbalance Data.Sensors (Basel, Switzerland) · 2022Article
- Assessment of Dispersion and Bubble Entropy Measures for Enhancing Preterm Birth Prediction Based on Electrohysterographic Signals.Sensors (Basel, Switzerland) · 2021Article
- Optimized Feature Subset Selection Using Genetic Algorithm for Preterm Labor Prediction Based on Electrohysterography.Sensors (Basel, Switzerland) · 2021Article
- Optimization of Imminent Labor Prediction Systems in Women with Threatened Preterm Labor Based on Electrohysterography.Sensors (Basel, Switzerland) · 2021Article
Corrections and comments
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Authors and funding
9 authors at 1 institution in 1 country.
Funding
Abstract
Electrohysterography (EHG) has been shown to provide relevant information on uterine activity and could be used for predicting preterm labor and identifying other maternal fetal risks. The extraction of high-quality robust features is a key factor in achieving satisfactory prediction systems from EHG. Temporal, spectral, and non-linear EHG parameters have been computed to characterize EHG signals, sometimes obtaining controversial results, especially for non-linear parameters. The goal of this work was to assess the performance of EHG parameters in identifying those robust enough for uterine electrophysiological characterization. EHG signals were picked up in different obstetric scenarios: antepartum, including women who delivered on term, labor, and post-partum. The results revealed that the 10th and 90th percentiles, for parameters with falling and rising trends as labor approaches, respectively, differentiate between these obstetric scenarios better than median analysis window values. Root-mean-square amplitude, spectral decile 3, and spectral moment ratio showed consistent tendencies for the different obstetric scenarios as well as non-linear parameters: Lempel-Ziv, sample entropy, spectral entropy, and SD1/SD2 when computed in the fast wave high bandwidth. These findings would make it possible to extract high quality and robust EHG features to improve computer-aided assessment tools for pregnancy, labor, and postpartum progress and identify maternal fetal risks.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.