arXiv：cs.LG· Yaowen Zhang, Bo Cui, Libera Fresiello, Peter H. Veltink, Dirk W. Donker, Ying Wang·· 13 小时前
CVAF-Net：先验引导双视图表示学习用于短期 PPG 信号心输出量估计
Cross-View Attention Fusion Net: A Prior-Guided Dual-View Representation Learning for Cardiac Output Estimation from Short-Term PPG Signals
AI 导读
CVAF-Net 是一种先验引导的双视图深度学习模型，通过跨视图注意力融合原始 PPG 时序视图与特征序列图（FSM），从 5、15、30 秒短时 PPG 片段估计心输出量。
正文
Abstract:Accurate cardiac output (CO) estimation from photoplethysmography (PPG) is promising for unobtrusive hemodynamic monitoring, but remains difficult since CO is jointly determined by cardiac function and vascular tone. Conventional feature-based models use physiologically meaningful PPG descriptors, yet depend on accurate pulse detection and may miss latent temporal relationships. In contrast, fully end-to-end deep learning models learn directly from raw PPG but often underuse established PPG-derived prior information. Here, we introduce the Cross-View Attention Fusion Network (CVAF-Net), a prior-guided dual-view deep learning model for CO estimation from short, fixed-length PPG segments. CVAF-Net processes raw PPG as a temporal view and a feature sequence map (FSM) as a structured prior-guided view, and fuses the two representations through cross-view attention. The model was independently evaluated using 5-, 15-, and 30-s segments from three datasets: simulated pulse waves (3323 subjects), vasoconstriction provocation (79 subjects), and resting/cycling activities (10 subjects), and was compared with multiple machine learning and deep learning benchmarks. CVAF-Net outperformed most benchmark methods and achieved performance comparable to a state-of-the-art Transformer-based model, with a mean absolute error (MAE) of 0.19 L/min (MAPE: 3.95%) on simulated data and high accuracy in real-world settings (minimum MAE: 1.20 L/min). Importantly, CVAF-Net reduced FLOPs by twelvefold compared with the leading Transformer-based model. Plausibility analysis showed physiologically consistent CO estimates, with expected correlations with age ($\rho = -0.274$), heart rate ($\rho = 0.894$), and systemic vascular resistance ($\rho = -0.740$). These findings indicate that CVAF-Net provides an accurate, computationally efficient, and generalizable approach for continuous wearable-based CO monitoring.
| Subjects: | Medical Physics (physics.med-ph); Machine Learning (cs.LG) |
| Cite as: | arXiv:2605.19666 [physics.med-ph] |
| (or arXiv:2605.19666v2 [physics.med-ph] for this version) | |
| https://doi.org/10.48550/arXiv.2605.19666 arXiv-issued DOI via DataCite |
Submission history
From: Yaowen Zhang [view email]
[v1]
Tue, 19 May 2026 10:59:49 UTC (17,763 KB)
[v2]
Fri, 9 Oct 2026 02:09:25 UTC (1,348 KB)
来源：arXiv：cs.LG · arxiv.org