用可穿戴设备信号精准重建动脉血压波形，减少个体差异影响。
ArterialNet: Reconstructing Arterial Blood Pressure Waveform with Wearable Pulsatile Signals, a Cohort-Aware Approach
- 融合通用转换与个性化特征提取，通过混合损失函数优化
- 在MIMIC-III数据集上误差仅5.41±1.35 mmHg，标准差降低58%
- 适合远程健康监测，对数据质量变化具有强鲁棒性
持续动脉血压（ABP）波形虽具侵入性但对血流动力学监测至关重要。现有非侵入式方法基于脉动信号重建ABP波形，但所得收缩压（SBP）/舒张压（DBP）不准确，且易受个体差异影响。本文提出ArterialNet，结合通用脉动信号到ABP的映射与个性化特征提取，采用混合损失函数与正则化。在MIMIC-III数据集上，其均方根误差（RMSE）为5.41±1.35 mmHg，标准差较现有技术降低58%；在远程健康场景中，RMSE为7.99±1.91 mmHg。结果表明，ArterialNet在ABP重建与SBP/DBP估计上表现优异，显著降低个体差异影响，具备远程医疗应用潜力。通过系列消融实验，验证了各模块贡献及模型对数据质量与可用性的鲁棒性。
原文摘要 · Abstract (English)
Goal: Continuous arterial blood pressure (ABP) waveform is invasive but essential for hemodynamic monitoring. Current non-invasive techniques reconstruct ABP waveforms with pulsatile signals but derived inaccurate systolic and diastolic blood pressure (SBP/DBP) and were sensitive to individual variability. Methods: ArterialNet integrates generalized pulsatile-to-ABP signal translation and personalized feature extraction using hybrid loss functions and regularizations. Results: ArterialNet achieved a root mean square error (RMSE) of 5.41 -+ 1.35 mmHg on MIMIC-III, achieving 58% lower standard deviation than existing signal translation techniques. ArterialNet also reconstructed ABP with RMSE of 7.99 -+ 1.91 mmHg in remote health scenario. Conclusion: ArterialNet achieved superior performance in ABP reconstruction and SBP/DBP estimations with significantly reduced subject variance, demonstrating its potential in remote health settings. We also ablated ArterialNet's architecture to investigate contributions of each component and evaluated ArterialNet's translational impact and robustness by conducting a series of ablations on data quality and availability.
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