发表机构
Shenzhen Technology University; OPPO Health Lab; Peking University; Southeast University（深圳技术大学; OPPO健康实验室; 北京大学; 东南大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对PPG血压估计中人群异质性和一对多映射问题，提出双路径网络SIFPBPNet，通过稳态与瞬时特征分离及交叉注意力融合，在可穿戴数据集上MAE达8.57/5.97 mmHg，且SFP模块可即插即用提升多种模型性能。
AI 中文摘要
利用光电容积脉搏波（PPG）进行连续、无袖带的血压（BP）监测，对于低成本、个性化的心血管健康管理具有重要意义。然而，显著的人群异质性以及“一对多映射”问题（即不同个体间相似的波形对应不同的血压水平）限制了传统基于人群模型的准确性。为应对这一挑战，我们提出了一种名为SIFPBPNet的双路径架构，通过稳态特征路径（SFP）和瞬时特征路径（IFP）分别表示稳态和瞬时特征。SFP采用图注意力网络（GAT）从多日历史PPG轨迹中提取个体特异性和长期特征。与此同时，IFP从当前PPG片段中捕捉短期动态，并通过交叉注意力机制融入稳态先验。在大规模可穿戴数据集上的实验表明，SIFPBPNet在收缩压和舒张压上分别实现了8.57和5.97 mmHg的平均绝对误差（MAE），优于现有最先进模型。此外，SFP模块在集成到各种骨干架构时均能持续提升性能，使收缩压的MAE相对降低2.8%至13.1%。这些结果凸显了SFP模块的强大泛化能力和即插即用的可迁移性，彰显了其在准确无袖带血压监测方面的巨大潜力。
英文摘要
Continuous and cuffless blood pressure (BP) monitoring using photoplethysmography (PPG) is of great interest for low-cost and personalized cardiovascular health management. However, significant population heterogeneity and the "one-to-many mapping" problem, where similar waveforms across individuals correspond to different BP levels, limit the accuracy of conventional population-based models. To address this challenge, we propose a dual-path architecture termed SIFPBPNet, which separately represents steady-state and instantaneous features, through a Steady-state Feature Path (SFP) and an Instantaneous Feature Path (IFP). The SFP employs a Graph Attention Network (GAT) to extract individual-specific and long-term characteristics from multi-day historical PPG trajectories. In parallel, the IFP captures short-term dynamics from current PPG segments and incorporates the steady-state prior via a cross-attention mechanism. Experiments on a large-scale wearable dataset demonstrate that SIFPBPNet achieves a Mean Absolute Error (MAE) of 8.57 and 5.97 mmHg for systolic and diastolic BP, respectively, outperforming state-of-the-art models. Furthermore, the SFP module consistently improves performance when integrated into various backbone architectures, yielding 2.8-13.1% relative MAE reductions for systolic BP. These results highlight the strong generalizability and plug-and-play transferability of the SFP module, underscoring its great potential for accurate cuffless BP monitoring.
CommentsAccepted for publication at the 48th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2026), Toronto, Canada
