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arXiv Machine Learning By Yaowen Zhang, Bo Cui, Libera Fresiello, Peter H. Veltink, Dirk W. Donker, Ying Wang

Cross-View Attention Fusion Net: A Prior-Guided Dual-View Representation Learning for Cardiac Output Estimation from Short-Term PPG Signals

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The Cross-View Attention Fusion Network (CVAF‑Net) is a dual‑view deep learning model that estimates cardiac output from short photoplethysmography (PPG) segments by combining raw PPG signals with a structured prior‑guided feature sequence map through cross‑view attention. Evaluated on simulated, vasoconstriction, and real‑world activity datasets, CVAF‑Net achieved a mean absolute error of 0.19 L/min on simulated data and as low as 1.20 L/min in real settings, outperforming most benchmarks while reducing computational cost by twelvefold compared to a leading Transformer model. Plausibility checks confirmed physiologically consistent correlations with age, heart rate, and systemic vascular resistance.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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