Fig. 4: Grad-CAM visualization of the model’s decision-making process.
From: Real-time detection of respiratory circuit events in mechanical ventilation using deep learning
The heatmaps generated using the Grad-CAM method highlight the key areas the model focuses on when identifying normal waveforms, leakage-like patterns, and fluid-accumulation-like patterns. For normal respiratory waveforms, the heatmap is evenly distributed across inspiratory and expiratory phase, capturing the respiratory rhythm characteristics. In the case of leakage-like patterns, the model significantly concentrates on areas where the volume-time curve is incomplete return to baseline. For fluid-accumulation-like patterns, the model responds strongly to the flow-time sawtooth oscillations.
