ArticleComputational and mathematical methods in medicine2014
Automatic identification of motion artifacts in EHG recording for robust analysis of uterine contractions.
Article in Computational and mathematical methods in medicine, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- 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
- Assessment of Features between Multichannel Electrohysterogram for Differentiation of Labors.Sensors (Basel, Switzerland) · 2022Article
- Recognition of uterine contractions with electrohysterogram and exploring the best electrode combination.Technology and health care : official journal of the European Society for Engineering and Medicine · 2022Article
- Assessment of Dispersion and Bubble Entropy Measures for Enhancing Preterm Birth Prediction Based on Electrohysterographic Signals.Sensors (Basel, Switzerland) · 2021Article
- Advanced Bioelectrical Signal Processing Methods: Past, Present, and Future Approach-Part III: Other Biosignals.Sensors (Basel, Switzerland) · 2021Review
- Optimization of Imminent Labor Prediction Systems in Women with Threatened Preterm Labor Based on Electrohysterography.Sensors (Basel, Switzerland) · 2021Article
- Phase Entropy Analysis of Electrohysterographic Data at the Third Trimester of Human Pregnancy and Active Parturition.Entropy (Basel, Switzerland) · 2020Article
- A Comparative Study of Vaginal Labor and Caesarean Section Postpartum Uterine Myoelectrical Activity.Sensors (Basel, Switzerland) · 2020Article
- Electrohysterogram for ANN-Based Prediction of Imminent Labor in Women with Threatened Preterm Labor Undergoing Tocolytic Therapy.Sensors (Basel, Switzerland) · 2020Article
- Evaluation of convolutional neural network for recognizing uterine contractions with electrohysterogram.Computers in biology and medicine · 2019Article
- Uterine electromyography for discrimination of labor imminence in women with threatened preterm labor under tocolytic treatment.Medical & biological engineering & computing · 2019Article
- Development of Electrohysterogram Recording System for Monitoring Uterine Contraction.Journal of healthcare engineering · 2019Article
- Improved Prediction of Preterm Delivery Using Empirical Mode Decomposition Analysis of Uterine Electromyography Signals.PloS one · 2015Article
- Application of decision tree in determining the importance of surface electrohysterography signal characteristics for recognizing uterine contractions.Biocybernetics and biomedical engineeringArticle
- On the prediction of premature births in Hispanic labour patients using uterine contractions, heart beat signals and prediction machines.Healthcare technology lettersArticle
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Authors and funding
5 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Electrohysterography (EHG) is a noninvasive technique for monitoring uterine electrical activity. However, the presence of artifacts in the EHG signal may give rise to erroneous interpretations and make it difficult to extract useful information from these recordings. The aim of this work was to develop an automatic system of segmenting EHG recordings that distinguishes between uterine contractions and artifacts. Firstly, the segmentation is performed using an algorithm that generates the TOCO-like signal derived from the EHG and detects windows with significant changes in amplitude. After that, these segments are classified in two groups: artifacted and nonartifacted signals. To develop a classifier, a total of eleven spectral, temporal, and nonlinear features were calculated from EHG signal windows from 12 women in the first stage of labor that had previously been classified by experts. The combination of characteristics that led to the highest degree of accuracy in detecting artifacts was then determined. The results showed that it is possible to obtain automatic detection of motion artifacts in segmented EHG recordings with a precision of 92.2% using only seven features. The proposed algorithm and classifier together compose a useful tool for analyzing EHG signals and would help to promote clinical applications of this technique.
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