Prenatal Stress Detection from Electrocardiography Using Self-Supervised Deep Learning: Development and External Validation

Prenatal psychological stress affects 15-25% of pregnancies and increases risks of preterm birth, low birth weight, and adverse neurodevelopmental outcomes. Current screening relies on subjective questionnaires (PSS-10), limiting continuous monitoring. We developed deep learning models for stress detection from electrocardiography (ECG) using the FELICITy 1 cohort (151 pregnant women, 32-38 weeks gestation). A ResNet-34 encoder was pretrained via SimCLR contrastive learning on 40,692 ECG segments per subject. Multi-layer feature extraction enabled binary classification and continuous PSS prediction across maternal (mECG), fetal (fECG), and abdominal ECG (aECG). External validation used the FELICITy 2 RCT (28 subjects, different ECG device, yoga intervention vs. control). On FELICITy 1 (5-fold CV): mECG 98.6% accuracy (R2=0.88, MAE=1.90), fECG 99.8% (R2=0.95, MAE=1.19), aECG 95.5% (R2=0.75, MAE=2.80). External validation on FELICITy 2: mECG 77.3% accuracy (R2=0.62, MAE=3.54, AUC=0.826), aECG 63.6% (R2=0.29, AUC=0.705). Signal quality-based channel selection outperformed all-channel averaging (+12% R2 improvement). Mixed-effects models detected a significant intervention response (p=0.041). Self-supervised deep learning on pregnancy ECG enables accurate, objective stress assessment, with multi-layer feature extraction substantially outperforming single embedding approaches.

💡 Research Summary

This study addresses the pressing need for objective, continuous monitoring of prenatal psychological stress, which affects up to a quarter of pregnancies and is linked to adverse maternal and fetal outcomes. Traditional screening relies on self‑report questionnaires such as the Perceived Stress Scale (PSS‑10), which provide only snapshot assessments and cannot capture dynamic physiological changes. The authors propose a novel framework that leverages raw electrocardiography (ECG) recordings from pregnant women to automatically detect stress levels using self‑supervised deep learning.

Two independent cohorts were used. The development cohort (FELICITy 1) comprised 151 women at 32–38 weeks gestation who underwent a 10‑minute abdominal ECG recording with a 5‑electrode array (Monica AN‑24, 900 Hz). Simultaneously, each participant completed the PSS‑10 questionnaire. The recordings were band‑pass filtered (0.5–40 Hz), down‑sampled to 256 Hz, segmented into overlapping 10‑second windows, and artifacts were removed, yielding roughly 40 000 segments per ECG type (maternal ECG