发表机构
Baylor College of Medicine; Jan and Dan Duncan Neurological Research Institute; Texas Children’s Hospital; Reed College（贝勒医学院; 简和丹·邓肯神经学研究所; 得克萨斯儿童医院; 里德学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出结合一维SE-ResNet、数据增强与Windkessel物理信息重建的深度学习框架，在VTaC基准上以5分挑战赛评分提升降低ICU假性室速警报，物理信息目标为主要性能驱动。
AI 中文摘要
假性室性心动过速（VT）警报是重症监护病房（ICU）中警报疲劳的主要原因。我们提出了一种深度学习框架，该框架结合了一维SE-ResNet、ICU真实数据增强以及基于三元件Windkessel血流动力学模型的物理信息辅助重建任务，该任务以可微分的正向模拟方式实现。通过要求网络的潜在表示生成生理上合理的动脉血压波形，由伪影驱动的ECG模式受到惩罚，而真正的VT在模态间保持连贯。在VTaC基准上，在严格的实时协议（10秒警报前窗口）下进行评估，我们的方法相比先前的最先进技术实现了5分的挑战赛评分提升。消融研究证实，物理信息目标函数是性能提升的主要驱动因素，在准确性、2倍标签效率以及更局部化和临床有意义的ECG片段方面带来了增益。
英文摘要
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.
CommentsPublished at CinC 2026
