用深度学习同时识别并修复胎心监护中的多种伪影，提升诊断准确性。
CleanCTG: A Deep Learning Model for Multi-Artefact Detection and Reconstruction in Cardiotocography
- 分两阶段处理：先多尺度检测伪影类型，再针对性重建受损信号
- 合成数据上伪影检测完美（AU-ROC=1.00），重建误差比最优方法低60%以上
- 临床验证中灵敏度83.44%，集成后诊断特异性提升，决策时间缩短三分之一
胎心监护（CTG）对胎儿监测至关重要，但常受多种伪影干扰，掩盖真实胎心率模式，导致误诊或延误干预。现有深度学习方法多忽略全面噪声处理，仅做简单预处理或专注下游分类；传统方法依赖插值或规则过滤，仅能处理缺失数据，无法应对复杂伪影。本文提出CleanCTG，一种端到端双阶段模型：首先通过多尺度卷积与上下文感知交叉注意力识别多种伪影类型，再由针对不同伪影的修正分支重建受损段。训练使用超过80万分钟由专家验证的“干净”记录生成的合成伪影数据。在合成数据上，模型实现伪影检测完美表现（AU-ROC=1.00），重建均方误差降至2.74×10⁻⁴（干净段为2.40×10⁻⁶），优于次优方法60%以上。外部验证在10,190分钟临床标注片段上取得AU-ROC=0.95（敏感度83.44%，特异度94.22%），超越六种对比分类器。最终，在933例临床CTG记录中集成Dawes-Redman系统，去噪后特异度从80.70%升至82.70%，中位决策时间缩短33%。结果表明，显式伪影去除与信号重建可在保持诊断准确的同时缩短监测时长，为更可靠的CTG解读提供可行路径。
原文摘要 · Abstract (English)
Cardiotocography (CTG) is essential for fetal monitoring but is frequently compromised by diverse artefacts which obscure true fetal heart rate (FHR) patterns and can lead to misdiagnosis or delayed intervention. Current deep-learning approaches typically bypass comprehensive noise handling, applying minimal preprocessing or focusing solely on downstream classification, while traditional methods rely on simple interpolation or rule-based filtering that addresses only missing samples and fail to correct complex artefact types. We present CleanCTG, an end-to-end dual-stage model that first identifies multiple artefact types via multi-scale convolution and context-aware cross-attention, then reconstructs corrupted segments through artefact-specific correction branches. Training utilised over 800,000 minutes of physiologically realistic, synthetically corrupted CTGs derived from expert-verified "clean" recordings. On synthetic data, CleanCTG achieved perfect artefact detection (AU-ROC = 1.00) and reduced mean squared error (MSE) on corrupted segments to 2.74 x 10^-4 (clean-segment MSE = 2.40 x 10^-6), outperforming the next best method by more than 60%. External validation on 10,190 minutes of clinician-annotated segments yielded AU-ROC = 0.95 (sensitivity = 83.44%, specificity 94.22%), surpassing six comparator classifiers. Finally, when integrated with the Dawes-Redman system on 933 clinical CTG recordings, denoised traces increased specificity (from 80.70% to 82.70%) and shortened median time to decision by 33%. These findings suggest that explicit artefact removal and signal reconstruction can both maintain diagnostic accuracy and enable shorter monitoring sessions, offering a practical route to more reliable CTG interpretation.
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