用PPG生成可用的ECG，实现可穿戴设备的心脏病筛查
PPGFlowECG: Latent Rectified Flow with Cross-Modal Encoding for PPG-Guided ECG Generation and Cardiovascular Disease Detection
- 通过共享潜空间对齐PPG与ECG信号，提升生成质量
- 在四个数据集上验证，合成信号保真度和诊断效果更优
- 适合缺乏专业设备时的远程心脏疾病初筛
心电图（ECG）是心血管疾病（CVD）评估的临床金标准，但连续监测受限于专用硬件和专业人员。光电容积脉搏波（PPG）广泛存在于可穿戴设备中，易于扩展，但缺乏电生理特异性，限制了诊断可靠性。现有生成方法在将PPG转化为临床可用的ECG信号时，受限于生成模型中生理语义的错位以及高维信号建模的复杂性。为此，我们提出PPGFlowECG，一个两阶段框架：首先使用CardioAlign编码器在共享潜空间中对齐PPG与ECG信号，随后利用潜空间修正流（latent rectified flow）合成ECG。我们进一步提供了该耦合关系的正式分析，表明CardioAlign编码器对于在本框架下实现稳定且语义一致的ECG合成至关重要。在四个数据集上的大量实验表明，该方法显著提升了合成保真度与下游诊断效用。结果表明，当标准ECG获取不可行时，PPGFlowECG支持可扩展的、以可穿戴设备为核心的CVD筛查。
原文摘要 · Abstract (English)
Electrocardiography (ECG) is the clinical gold standard for cardiovascular disease (CVD) assessment, yet continuous monitoring is constrained by the need for dedicated hardware and trained personnel. Photoplethysmography (PPG) is ubiquitous in wearable devices and readily scalable, but it lacks electrophysiological specificity, limiting diagnostic reliability. While generative methods aim to translate PPG into clinically useful ECG signals, existing approaches are limited by the misalignment of physiological semantics in generative models and the complexity of modeling in high-dimensional signals. To address these limitations, we propose PPGFlowECG, a two-stage framework that aligns PPG and ECG in a shared latent space using the CardioAlign Encoder and then synthesizes ECGs with latent rectified flow. We further provide a formal analysis of this coupling, showing that the CardioAlign Encoder is necessary to guarantee stable and semantically consistent ECG synthesis under our formulation. Extensive experiments on four datasets demonstrate improved synthesis fidelity and downstream diagnostic utility. These results indicate that PPGFlowECG supports scalable, wearable-first CVD screening when standard ECG acquisition is unavailable.
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