AI RESEARCH
Prenatal Stress Detection from Electrocardiography Using Self-Supervised Deep Learning: Development and External Validation
arXiv CS.LG
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ArXi:2602.03886v3 Announce Type: replace-cross 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.