用胎儿心电图自动检测孕期压力，准确率超95%。
Prenatal Stress Detection from Electrocardiography Using Self-Supervised Deep Learning: Development and External Validation
- 用自监督学习从心电图中提取多层特征，实现压力检测。
- 在自建数据集上准确率达98.6%，外部验证仍保持77.3%。
- 适合产科、心理健康监测及可穿戴设备研发人员参考。
孕期心理压力影响15%-25%的妊娠，增加早产、低出生体重和神经发育不良风险。现有筛查依赖主观问卷（PSS-10），难以连续监测。本研究基于FELICITy 1队列（151名孕32-38周女性）开发深度学习模型，利用ResNet-34编码器通过SimCLR对比学习对每名受试者40,692段心电图进行预训练。通过多层特征提取，实现母体（mECG）、胎儿（fECG）和腹壁（aECG）心电图的二分类与连续PSS评分预测。外部验证使用FELICITy 2随机对照试验（28名受试者，不同心电设备，瑜伽干预组与对照组）。在FELICITy 1上：mECG准确率98.6%（R²=0.88，MAE=1.90），fECG 99.8%（R²=0.95，MAE=1.19），aECG 95.5%（R²=0.75，MAE=2.80）。外部验证中：mECG准确率77.3%（R²=0.62，MAE=3.54，AUC=0.826），aECG 63.6%（R²=0.29，AUC=0.705）。基于信号质量的通道选择优于全通道平均（提升12% R²）。混合效应模型检测到干预显著响应（p=0.041）。自监督深度学习结合多层特征提取，显著优于单嵌入方法，可实现精准、客观的孕期压力评估。
原文摘要 · Abstract (English)
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.
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