New deep learning model estimates cardiac output from 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

影响 Introduces a more computationally efficient deep learning approach for continuous, wearable-based cardiac output monitoring.

排序理由 The cluster contains an academic paper detailing a new model and its evaluation. [lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

• cardiac output
• CVAF-Net
• photoplethysmography

• 论文
• 其他

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →