用多波长心电图实现精准血压估计，跨人群泛化能力强。
Generalizable Blood Pressure Estimation from Multi-Wavelength PPG Using Curriculum-Adversarial Learning
- 分阶段训练：先分类后回归，提升模型学习效率
- 多通道融合优于单通道，收缩压误差14.2mmHg，舒张压6.4mmHg
- 对抗训练隐藏个体特征，适合临床通用场景
精准且具备泛化能力的血压（BP）估测对心血管疾病早期发现与管理至关重要。本研究在公开的多波长光电容积脉搏波（PPG）数据集上采用受试者级数据划分，并提出一种基于课程-对抗学习的泛化性血压估测框架。方法结合课程学习（从高血压分类逐步过渡到血压回归）与域对抗训练（混淆受试者身份以促进学习个体无关特征）。实验表明，多通道融合始终优于单通道模型。在四波长PPG数据集上，该方法在严格的受试者级划分下表现优异，收缩压（SBP）平均绝对误差（MAE）为14.2mmHg，舒张压（DBP）为6.4mmHg。消融实验验证了课程学习与对抗机制的有效性。结果表明，利用多波长PPG中的互补信息及课程-对抗策略，可实现准确且鲁棒的血压估测。
原文摘要 · Abstract (English)
Accurate and generalizable blood pressure (BP) estimation is vital for the early detection and management of cardiovascular diseases. In this study, we enforce subject-level data splitting on a public multi-wavelength photoplethysmography (PPG) dataset and propose a generalizable BP estimation framework based on curriculum-adversarial learning. Our approach combines curriculum learning, which transitions from hypertension classification to BP regression, with domain-adversarial training that confuses subject identity to encourage the learning of subject-invariant features. Experiments show that multi-channel fusion consistently outperforms single-channel models. On the four-wavelength PPG dataset, our method achieves strong performance under strict subject-level splitting, with mean absolute errors (MAE) of 14.2mmHg for systolic blood pressure (SBP) and 6.4mmHg for diastolic blood pressure (DBP). Additionally, ablation studies validate the effectiveness of both the curriculum and adversarial components. These results highlight the potential of leveraging complementary information in multi-wavelength PPG and curriculum-adversarial strategies for accurate and robust BP estimation.
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