Optimized Hierarchical Fused Fuzzy Deep Reinforcement Learning model for predicting non-sinus (higher-risk) cardiac rhythms from PQRST-analyzed 12-lead ECG data. The three-stage approach combines data preprocessing, reinforcement learning, and fuzzy deep learning to classify sinus vs. non-sinus rhythms. Evaluated on a 12-lead ECG dataset of 10,646 patients, OHFFDRL achieved 94% accuracy, an AUC of 0.91, and was interpreted using SHAP, LIME, calibration curves, adversarial vulnerability analysis, and integrated gradients; TAxis (ventricular repolarization movement range) was found to be the most important distinguishing feature.

Self-supervised learning (SSL) model that identifies chronically stressed mother-fetus dyads from raw maternal abdominal ECG (aECG), which contains both maternal and fetal cardiac signals. Built on a self-supervised representation-learning approach originally developed for ECG-based emotion recognition, the model is pretrained on public ECG datasets and evaluated on a cohort of pregnant women with chronic stress exposure validated by psychological inventory, maternal hair cortisol, and the fetal stress index (FSI). Using maternal ECG alone with the publicly pretrained model, it detected the chronic-stress-exposure group with AUROC 0.982 and predicted psychological stress score (R2 0.943), FSI (R2 0.946), and maternal hair cortisol (R2 0.931).