Analysis of electrohysterogram signals for predicting obstetric outcome using machine learning methods: a scoping review.

Authors

Rubana H Chowdhury, Roma Sultana, Mithila Arman, Yasir Rahman, Quazi D Hossain, Mohiuddin Ahmad

Published in

BMC pregnancy and childbirth. Jul 06, 2026. Epub Jul 06, 2026.

Abstract

Efficiently detecting obstetric outcomes, such as preterm birth or mode of delivery, is crucial for enhancing mother and newborn health. Proactive screening, identification, and prevention in asymptomatic pregnant women exhibiting risk factors for preterm birth or c-section delivery can mitigate incidence and fatality rates.
This study thoroughly reviewed prediction models for obstetric outcomes, described the EHG signal acquisition protocols, pre-processing methods, feature extraction from EHG, and model properties, and compared their quality to establish the most effective prediction model for clinical decision-making. For this study, the biomedical databases (PubMed, Scopus, Web of Science, Embase) of published publications were searched from December 2000 to February 2025. In addition to electrohysterography, other search terms to consider are electrohysterogram, uterine electromyography, Term-preterm labor, birth delivery mode, and EHG in machine learning.
Based on a literature review, the prevailing recording technique for acquiring EHG signals across various applications, including pregnancy monitoring, preterm risk evaluation, and birth delivery mode detection, commonly employs four bipolar electrodes. A bandpass filter of minimum bandwidth of 0.1 to 4 Hz is most commonly used for pre-process the EHG signal. High discriminative performance was reported in 5 studies, with an Area Under the Curve (AUC) ranging from 0.93 to 0.99. A single classifier may suffice for predicting obstetric outcomes using an EHG signal, eliminating the need for a combined classifier. A total of 98.5% of the studies exhibited a high risk of bias in the analysis domain, primarily due to the limited sample size and the absence of external validation.
This review will familiarize academics and obstetricians with the comprehensive EHG analytical process and its prospective uses in clinical decision support systems.

PMID:
42410377
Bibliographic data and abstract were imported from PubMed on 07 Jul 2026.

Read full publication at:
Please sign in to see all details.

Stats

1-terrible, 9-excellent. How would you rate this publication? Sign in in to submit your rating.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.