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.

翻译：产前胎心宫缩图（CTG）对于胎儿健康监测至关重要，但诸如Dawes-Redman系统等传统方法常受观察者间高变异性的限制，导致解读不一致和潜在的误诊。本文介绍了PatchCTG，一种专门为CTG分析设计的基于Transformer的模型，它采用基于补丁的标记化、实例归一化和通道独立处理，以捕捉CTG信号中关键的局部和全局时间依赖性。PatchCTG在牛津产科（OXMAT）数据集上进行了评估，该数据集在应用纳入和排除标准后，包含了涵盖不同临床结局的超过20,000条CTG记录。经过广泛的超参数优化，PatchCTG在约登指数阈值下实现了77%的AUC、88%的特异性和57%的灵敏度，显示出适应不同临床需求的能力。在不同时间阈值下的测试表明其具有稳健的预测性能，特别是在对接近分娩的数据进行微调后，对于临近分娩的病例实现了52%的灵敏度和88%的特异性。这些发现表明，PatchCTG有潜力通过提供一个可靠、客观的胎儿健康评估工具来加强产前护理中的临床决策。源代码可在 https://github.com/jaleedkhan/PatchCTG 获取。