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

Introduction: 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. Methods: We analyzed 72 ten-minute single-channel EHG recordings from low-risk labors, yielding 648 segments of 120 s. Signals were filtered into three sub-bands. Twenty-one linear and nonlinear EHG descriptors were combined with four clinical variables (maternal age, gestational age, and counts of low- and high-frequency 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 Trees 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 Trees (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. Results: The best cross-validated model was a Bagged-Trees 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, and counts of low- and high-frequency contractions—that achieved F1, recall, precision, specificity, and accuracy of 1.000 on the independent test set for classifying cervical dilation stage (Low, Moderate, Advanced). Conclusion: An EHG-derived digital biomarker combining minimal clinical variables 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.