Cervical Dilation Classification from Electrohysterography and Clinical Features: A Machine-Learning-Derived Digital...

Noninvasive tracking of cervical dilation could reduce discomfort and infection risk from repeated digital examinations during labor. We present an electrohysterography (EHG)-based model framed as a digital biomarker of labor progression that leverages objective physiological signals with minimal clinical context.

We analyzed 72 ten-minute single-channel EHG recordings from low-risk labor cases, yielding 648 segments of 120 s. Signals were filtered into three sub-bands. Twenty-one linear and nonlinear EHG descriptors were combined with two clinical variables, maternal age and gestational age, and two EHG-derived contraction-count features, namely counts of low (LC) and high (HC) uterine contractions, to form 25 predictors. Segments were labeled as low (1-4 cm), moderate (5-6 cm), or advanced (7-10 cm) dilation. Data were split 70/30 into training (n = 454) and independent test (n = 194) sets. Feature importance was estimated using χ², ANOVA, and Kruskal-Wallis ranking. Thirty-three classifiers were evaluated using five-fold cross-validation within the training set, with the 10 top-ranked features. Among all models, a bagged tree ensemble achieved the highest macro-averaged F1 score and was therefore selected as the baseline classifier for this study. We then used a "Genetic Algorithm Ensemble Bagged Tree (GA-EBT)" approach, in which a binary-encoded genetic algorithm optimizes the bagged tree classifier's feature combination using stratified five-fold cross-validation with 50 repetitions on the training set.

The best cross-validated model was a bagged tree ensemble. Performance plateaued at 17 predictors (median macro-F1 = 0.898) under progressive inclusion. The GA-EBT identified a four-feature subset - maternal age, gestational age, LC count, and HC count - that achieved F1, recall, precision, specificity, and accuracy of 1.000 on the independent test set for classifying cervical dilation stage (low, moderate, advanced).

An EHG-derived digital biomarker combining a minimal set of clinical variables and EHG-derived contraction-count features enables accurate classification of cervical dilation stages from single-channel recordings. This pilot-stage classification approach showed maximal internal and independent test performance and may support real-time, noninvasive intrapartum monitoring while potentially reducing repeated digital examinations.