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.

翻译：心电图（ECG）是评估心血管疾病（CVD）的临床金标准，但连续监测受限于专用硬件和专业人员的需求。光电容积脉搏波（PPG）在可穿戴设备中普遍存在且易于扩展，但缺乏电生理特异性，限制了诊断的可靠性。尽管生成方法旨在将PPG转换为具有临床价值的心电信号，但现有方法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。为解决这些限制，我们提出了PPGFlowECG，这是一个两阶段框架：首先使用CardioAlign编码器将PPG与ECG在共享潜在空间中对齐，然后通过潜在修正流合成ECG。我们进一步对该耦合进行了形式化分析，表明CardioAlign编码器对于保证我们框架下稳定且语义一致的ECG合成是必要的。在四个数据集上的大量实验证明了合成保真度和下游诊断效用的提升。这些结果表明，当标准ECG采集不可行时，PPGFlowECG能够支持可扩展的、以可穿戴设备为先导的心血管疾病筛查。