Physics-Informed Deep Learning for False Ventricular Tachycardia Alarm Reduction in the ICU
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...
| Authors | Athanasios Papastathopoulos-Katsaros, Alexandra Stavrianidi, Zhandong Liu |
|---|---|
| Primary category | cs.LG |
| Published | 8 September 2026 |
| Last updated at source | 5 October 2026 |
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