Electrohysterography in the diagnosis of preterm birth: a review
Author:
Publisher
IOP Publishing
Subject
Physiology (medical),Biomedical Engineering,Physiology,Biophysics
Link
http://iopscience.iop.org/article/10.1088/1361-6579/aaad56/pdf
Reference119 articles.
1. Automated detection of premature delivery using empirical mode and wavelet packet decomposition techniques with uterine electromyogram signals
2. Practice Bulletin No. 171: Management of Preterm Labor
3. A Multivariate Multiscale Fuzzy Entropy Algorithm with Application to Uterine EMG Complexity Analysis
4. Comparison of Different EHG Feature Selection Methods for the Detection of Preterm Labor
5. Comparison of non-invasive electrohysterographic recording techniques for monitoring uterine dynamics
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2. Peak amplitude of the normalized power spectrum of the electromyogram of the uterus in the low frequency band is an effective predictor of premature birth;PLOS ONE;2024-09-12
3. Automatic semantic segmentation of EHG recordings by deep learning: An approach to a screening tool for use in clinical practice;Computer Methods and Programs in Biomedicine;2024-09
4. Machine Learning Techniques for the Prediction of Preterm Birth Using Electrohysterography Signals;Advances in Medical Technologies and Clinical Practice;2024-07-12
5. Overdistention Accelerates Electrophysiological Changes in Uterine Muscle Towards Labour in Multiple Gestations;IRBM;2024-06
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