用Transformer分析胎心监护图，提升胎儿健康评估的客观性。
PatchCTG: Patch Cardiotocography Transformer for Antepartum Fetal Health Monitoring
- 将胎心信号分块编码，结合归一化与独立通道处理，捕捉时间依赖性。
- 在2万条数据上测试，准确率AUC达77%，近产时敏感性52%、特异性88%。
- 适合临床辅助诊断，减少医生主观差异，提升产前监测可靠性。
产前胎心监护（CTG）对胎儿健康监测至关重要，但传统方法如Dawes-Redman系统常因观察者间差异大，导致解读不一致甚至误诊。本文提出PatchCTG，一种基于Transformer的CTG分析模型，采用分块标记、实例归一化及通道独立处理，以捕捉胎心信号中的局部与全局时间依赖关系。在包含超过20,000条符合标准的CTG记录的牛津产科数据集（OXMAT）上评估，经充分超参数优化后，模型在Youden阈值下达到AUC 77%，特异性88%，敏感性57%。在不同时间阈值下测试显示其鲁棒性，尤其在临近分娩的数据微调后，敏感性达52%，特异性保持88%。结果表明PatchCTG有望通过提供可靠、客观的胎儿健康评估工具，改善产前决策。源代码已开源：https://github.com/jaleedkhan/PatchCTG。
原文摘要 · Abstract (English)
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.
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