3 papers · 1 filter
Physics-Informed Deep Learning for False Ventricular Tachycardia Alarm Reduction in the ICU
Athanasios Papastathopoulos-Katsaros, Alexandra Stavrianidi, Zhandong Liu
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…
Interpretable EEG biomarkers with bag-of-waves: Spatial and temporal waveform dictionaries for low-data regimes
Athanasios Papastathopoulos-Katsaros, Steven T. Lee, Lin Yao +4
Electroencephalography (EEG) is widely used to diagnose neurological conditions, but its analysis usually relies on either predefined spectral features or deep neural networks. Pre…
Improving physics-informed neural network extrapolation via transfer learning and adaptive activation functions
Athanasios Papastathopoulos-Katsaros, Alexandra Stavrianidi, Zhandong Liu
Physics-Informed Neural Networks (PINNs) are deep learning models that incorporate the governing physical laws of a system into the learning process, making them well-suited for so…