ArticleScientific reports2021
Automatic recognition of uterine contractions with electrohysterogram signals based on the zero-crossing rate.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 27 citations in OpenAlex.
- Analysis of electrohysterogram signals for predicting obstetric outcome using machine learning methods: a scoping review.BMC pregnancy and childbirth · 2026Article
- Enhancing uterine contraction detection through novel EHG signal processing: a pilot study leveraging the relationship between slow and fast wave components to improve signal quality and noise resilience.Frontiers in physiology · 2025Article
- Characteristics of phase synchronization in electrohysterography and tocodynamometry for preterm birth prediction.Heliyon · 2024Article
- Assessment of Features between Multichannel Electrohysterogram for Differentiation of Labors.Sensors (Basel, Switzerland) · 2022Article
- Enhancing classification of preterm-term birth using continuous wavelet transform and entropy-based methods of electrohysterogram signals.Frontiers in endocrinology · 2022Article
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Authors and funding
7 authors at 3 institutions in 2 countries.
Funding
Abstract
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. This study proposes a novel algorithm for automatic recognition of UCs with EHG signals to improve the accuracy of detecting UCs. EHG signals by electrodes, the tension of the abdominal wall by tocodynamometry (TOCO) and maternal perception were recorded simultaneously in 54 pregnant women. The zero-crossing rate (ZCR) of the EHG signal and its power were calculated to modulate the raw EHG signal and highlight the EHG bursts. Then the envelope was extracted from the modulated EHG for UC recognition. Besides, UC was also detected by the conventional TOCO signal. Taking maternal perception as a reference, the UCs recognized by EHG and TOCO were evaluated with the sensitivity, positive predictive value (PPV), and UC parameters. The results show that the sensitivity and PPV are 87.8% and 93.18% for EHG, and 84.04% and 90.89% for TOCO. EHG detected a larger number of UCs than TOCO, which is closer to maternal perception. The duration and frequency of UC obtained from EHG and TOCO were not significantly different (p > 0.05). In conclusion, the proposed UC recognition algorithm has high accuracy and simple calculation which could be used for real-time analysis of EHG signals and long-term monitoring of UCs.
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