用生理模型约束深度学习，减少重症监护室的假性室速报警
Physics-Informed Deep Learning for False Ventricular Tachycardia Alarm Reduction in the ICU
- 结合1D SE-ResNet与血流动力学物理模型，实现可微分仿真
- 在10秒预报警窗口下提升5分挑战得分，误报率显著降低
- 适合临床医生和算法工程师参考，尤其关注重症监护报警优化
假性室速（VT）报警是重症监护室（ICU）中导致报警疲劳的主要原因。本文提出一种深度学习框架，结合一维SE-ResNet、ICU真实数据增强及基于三元件Windkessel血流动力学模型的物理信息辅助重建任务，该任务以可微分前向仿真方式实现。通过要求网络潜在表示生成生理上合理的动脉压波形，使由伪影引起的心电图（ECG）模式受到惩罚，而真实的室速在多模态间保持一致性。在VTaC基准测试中，采用严格的实时协议（10秒预报警窗口），本方法相较先前最先进方法提升5分挑战得分。消融实验表明，物理信息目标是性能提升的关键驱动因素，带来准确率提升、2倍标签效率改善，以及更局部化且临床上有意义的ECG片段。
原文摘要 · Abstract (English)
False ventricular tachycardia (VT) alarms are a leading contributor to alarm fatigue in intensive care units. We propose a deep learning framework combining a 1D SE-ResNet with ICU-realistic data augmentations and a physics-informed auxiliary reconstruction task based on the three-element Windkessel hemodynamic model, implemented as a differentiable forward simulation. By requiring the network's latent representation to produce physiologically plausible arterial pressure waveforms, artifact-driven ECG patterns are penalized while true VT remains coherent across modalities. Evaluated on the VTaC benchmark under a strict real-time protocol (10-second pre-alarm window), our method achieves a 5-point Challenge Score improvement over prior state-of-the-art. Ablation studies confirm that the physics-informed objective is the primary performance driver, providing gains in accuracy, 2x label efficiency, and more localized and clinically meaningful ECG segments.
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