Automatic detection of uterine contractions before and during labor using EHG: A systematic review

Uterine activity analysis is valuable for monitoring pregnancy progression and detecting contractions to assess the risk of preterm birth and enable timely intervention in case of adverse events. Traditional monitoring tools, such as external palpation, tocography, and intrauterine pressure catheters, are limited by factors like invasiveness, lack of accuracy, or suitability only during labor. Electrohysterography (EHG) is a novel promising, non-invasive alternative; nonetheless, distinguishing contractions from basal uterine activity within EHG signals remains significantly challenging. This systematic review describes and compares automatic uterine contraction detection methods proposed in the literature so far. Following a structured screening process, studies published in English up to March 2026 that address the topic under investigation were considered eligible, while review articles and inaccessible studies were excluded. The search was conducted in accordance with the protocol CRD42025611340 registered in PROSPERO, using PubMed, Scopus, and Web of Science databases. In total, 50 studies were included and evaluated for risk of bias using the QUADAS-2 tool. The employed datasets, pre-processing steps, and data preparation methods were examined, followed by a categorization of all detection techniques into three main groups: thresholding methods, Machine Learning classifiers, and Deep Learning approaches. These were analyzed and qualitatively compared to highlight strengths, limitations, and areas for improvement in EHG-based contraction detection research. Findings indicated that adaptive thresholding methods, especially those based on peak detection and RMS-envelope extraction, achieved high performance values. Machine Learning and Deep Learning approaches also demonstrated strong potential, but required training with large annotated datasets to achieve sufficient robustness and generalizability, which may limit their clinical applicability. However, the interpretation of these results is partly constrained by limitations in the underlying evidence, including the absence of a reliable reference for validating detected contractions, restrictive patient selection criteria, potential bias due to lack of blinding during signal analysis, and substantial heterogeneity across studies precluding direct quantitative comparison.