新的深度学习模型可从PPG信号估算心输出量

作者 PulseAugur 编辑部 · [1 个来源] · 2026-05-19 10:59

研究人员开发了一种名为CVAF-Net的新型深度学习模型，用于从短时光电容积脉搏波（PPG）信号估算心输出量。该模型处理原始PPG数据和特征序列图，并使用跨视图注意力融合它们以提高准确性。CVAF-Net在多个数据集上表现强劲，在模拟数据上实现了0.19 L/min的平均绝对误差，性能优于大多数基准模型，同时比领先的基于Transformer的模型在计算效率上显著更高。 AI

影响引入了一种计算效率更高的方法，用于基于可穿戴设备的连续心输出量监测。

排序理由该集群包含一篇详细介绍新模型及其评估的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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Newsworthiness bucket: Tool; 该集群包含一篇详细介绍新模型及其评估的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Ying Wang · 2026-05-19 10:59

跨视图注意力融合网络：用于短时PPG信号心输出量估计的先验引导双视图表示学习

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 meaningfu…

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报道来源 [1]

跨视图注意力融合网络：用于短时PPG信号心输出量估计的先验引导双视图表示学习

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