PatchCTG: Patch Cardiotocography Transformer for Antepartum Fetal Health Monitoring
M. Jaleed Khan, Manu Vatish, Gabriel Davis Jones

TL;DR
PatchCTG is a transformer-based model that improves fetal health monitoring from CTG signals by capturing local and global dependencies, showing promising results on a large clinical dataset.
Contribution
The paper introduces PatchCTG, a novel transformer architecture with patch-based tokenisation for CTG analysis, enhancing interpretability and predictive performance.
Findings
Achieved 77% AUC on the Oxford Maternity dataset.
Demonstrated robustness across different temporal thresholds.
Outperformed traditional methods in predictive accuracy.
Abstract
Antepartum Cardiotocography (CTG) is vital for fetal health monitoring, but traditional methods like the Dawes-Redman system are often limited by high inter-observer variability, leading to inconsistent interpretations and potential misdiagnoses. This paper introduces PatchCTG, a transformer-based model specifically designed for CTG analysis, employing patch-based tokenisation, instance normalisation and channel-independent processing to capture essential local and global temporal dependencies within CTG signals. PatchCTG was evaluated on the Oxford Maternity (OXMAT) dataset, comprising over 20,000 CTG traces across diverse clinical outcomes after applying the inclusion and exclusion criteria. With extensive hyperparameter optimisation, PatchCTG achieved an AUC of 77%, with specificity of 88% and sensitivity of 57% at Youden's index threshold, demonstrating adaptability to various…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsCongenital Heart Disease Studies · Neonatal and fetal brain pathology · Neonatal Respiratory Health Research
