Physics-Informed Deep Learning for False Ventricular Tachycardia Alarm Reduction in the ICU
Ranking
Overall
74
Content
90
Popularity
37
Observed public metrics from 1 member.
Merged summary
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 2Ă— label efficiency and more clinically meaningful ECG localization.
Sources (1)
Physics-Informed Deep Learning for False Ventricular Tachycardia Alarm Reduction in the ICU
Public signals
Semantic Scholar citations 0 · Semantic Scholar influential citations 0
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 2Ă— label efficiency and more clinically meaningful ECG localization.