Cross-View Attention Fusion Net: A Prior-Guided Dual-View Representation Learning for Cardiac Output Estimation from Short-Term PPG Signals
Researchers have developed a novel deep learning model called CVAF-Net for estimating cardiac output from short photoplethysmography (PPG) signals. This model processes both raw PPG data and a feature sequence map, fusing them using cross-view attention to improve accuracy. CVAF-Net demonstrated strong performance across multiple datasets, achieving a mean absolute error of 0.19 L/min on simulated data and outperforming most benchmarks while being significantly more computationally efficient than a leading Transformer-based model. AI
IMPACT Introduces a more computationally efficient deep learning approach for continuous, wearable-based cardiac output monitoring.