用仿真数据+无标签临床数据，从脉搏波无创估算心输出量等关键指标
Hybrid Modeling of Photoplethysmography for Non-invasive Monitoring of Cardiovascular Parameters
- 融合仿真与真实数据，用变分自编码器建模脉搏波与动脉波关系
- 在未标注数据上训练密度估计器，实现对心输出量波动的精准追踪
- 适合需要长期无创心血管监测的临床场景，如重症监护
持续心血管监测在精准健康中至关重要。然而，诸如每搏输出量和心输出量等关键心脏生物标志物通常需通过侵入式动脉压波形（APW）测量。作为替代方案，医院常规采集非侵入性脉搏波描记图（PPG）。遗憾的是，仅基于PPG预测这些关键指标仍是开放挑战，尤其受限于标注良好的PPG数据稀缺。为此，我们提出一种混合方法，结合血流动力学仿真与无标签临床数据，直接从PPG信号估计心血管生物标志物。该混合模型由两部分组成：一是基于配对PPG-APW数据训练的条件变分自编码器；二是基于标注仿真APW片段训练的条件密度估计器。实验表明，该方法可有效检测心输出量与每搏输出量的动态变化，在追踪这些指标的时间演变方面优于监督基线模型。
原文摘要 · Abstract (English)
Continuous cardiovascular monitoring can play a key role in precision health. However, some fundamental cardiac biomarkers of interest, including stroke volume and cardiac output, require invasive measurements, e.g., arterial pressure waveforms (APW). As a non-invasive alternative, photoplethysmography (PPG) measurements are routinely collected in hospital settings. Unfortunately, the prediction of key cardiac biomarkers from PPG instead of APW remains an open challenge, further complicated by the scarcity of annotated PPG measurements. As a solution, we propose a hybrid approach that uses hemodynamic simulations and unlabeled clinical data to estimate cardiovascular biomarkers directly from PPG signals. Our hybrid model combines a conditional variational autoencoder trained on paired PPG-APW data with a conditional density estimator of cardiac biomarkers trained on labeled simulated APW segments. As a key result, our experiments demonstrate that the proposed approach can detect fluctuations of cardiac output and stroke volume and outperform a supervised baseline in monitoring temporal changes in these biomarkers.
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