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 clinical needs. Testing across varying temporal thresholds showed robust predictive performance, particularly with finetuning on data closer to delivery, achieving a sensitivity of 52% and specificity of 88% for near-delivery cases. These findings suggest the potential of PatchCTG to enhance clinical decision-making in antepartum care by providing a reliable, objective tool for fetal health assessment. The source code is available at https://github.com/jaleedkhan/PatchCTG.

本研究针对传统心音监测方法高观察者间变异性的问题，提出了一种名为PatchCTG的新型Transformer模型，通过补丁基础的标记化、实例归一化和信道独立处理，捕捉CTG信号中的关键局部与全球时间依赖性。该模型在多样的临床结果上表现出适应性强与可靠的预测能力，能够显著提升临床决策的客观性。

PatchCTG：基于补丁的胎儿监护Transformer模型