Physics-Informed Deep Learning for False Ventricular Tachycardia Alarm Reduction in the ICU

TL;DR - A physics-informed deep learning system reduces false ventricular tachycardia alarms by enforcing physiologically plausible arterial-pressure reconstructions. It improves the VTaC benchmark Challenge Score by 5 points under a real-time ICU protocol, potentially helping mitigate alarm fatigue.

• Combines a 1D SE-ResNet with ICU-realistic data augmentation and a differentiable three-element Windkessel simulation.
• Uses an auxiliary reconstruction objective to penalize ECG artifacts while preserving true VT patterns coherent across modalities.
• Operates on a 10-second pre-alarm window, supporting real-time use.
• Ablations attribute the main gains to the physics-informed objective, including 2× label efficiency and more clinically meaningful ECG localization.