Gated Self Attention Convolutional Neural Networks for Predicting Adverse Birth Outcomes

Early detection of adverse birth outcomes is vital as they are major contributors to neonatal mortality and irreversible neurological complications in infants. These outcomes are typically linked to impaired blood and oxygen flow to the baby brain during or shortly after labour, making its early detection vital. Monitoring fetal heart rate (FHR) is crucial in identifying and capturing these complications. This study proposes a deep learning (DL) framework for enhancing the early detection of the babies at risk, leveraging both raw FHR signals and standard cardiotocography (CTG) features. Unlike traditional methods that primarily focus on abnormal CTG traces (but not birth outcomes), this approach, backed by a substantial cohort of patient records, demonstrates the potential of DL in predicting actual adverse outcomes as early as possible. The DL model combines a convolutional mechanism with a self-attention network, enhanced by a gating mechanism for more accurate feature learning. The investigated DL architecture is trained on a dataset of over 37,000 births, including 1,291 abnormal ones, and is evaluated on a holdout set of 6,459 births, as well as the open-access CTU-CHB CTG dataset of 552 births. The proposed DL model demonstrates superior diagnostic accuracy, outperforming state-of-the-art baseline methods and clinical benchmarks. It achieved sensitivity of 49.08% (95% CI, 46.01-53.36%) at 15% false positive rate (FPR), compared to the clinical benchmark sensitivity of 37.70% (33.10-42.30%) and a previous model's 32.60% (28.20-37.30%) at a similar FPR.