发表机构
Singapore Management University; University of Cambridge（新加坡管理大学; 剑桥大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
PulseFlow 提出源条件反事实生成框架，结合条件表示学习与可逆潜在传输，在保留源信息的同时编辑心脏节律，在两个临床队列上实现有效节律转换并改进有限标签下的 AF 分类。
AI 中文摘要
光电容积脉搏波（PPG）已成为连续心血管监测的重要模态，包括心房颤动（AF）检测。然而，在许多临床环境中，标记的 AF 记录仍然有限，当只有有限的目标数据可用时，这使得模型适应变得困难。生成建模提供了一种自然的方式来缓解这种稀缺性，通过合成额外的 AF 信号。然而，现有方法主要生成与目标条件匹配的样本，而没有显式建模如何转换观察到的源记录，这使得难以利用来自特定人群或队列的丰富源记录进行有针对性的增强。我们引入了 PulseFlow，一个源条件反事实生成框架，它结合了条件表示学习与可逆潜在传输，在保留源信息的同时编辑心脏节律。在两个临床队列上的实验证明了有效的节律转换、可测量的源对应性以及在有限标签下改进的 AF 分类。
英文摘要
Photoplethysmography (PPG) has become an important modality for continuous cardiovascular monitoring, including atrial fibrillation (AF) detection. However, labeled AF recordings remain limited in many clinical settings, making model adaptation difficult when only limited target data are available. Generative modeling offers a natural way to alleviate this scarcity by synthesizing additional AF signals. Existing approaches, however, mainly generate samples that match the target condition without explicitly modeling how an observed source recording should be transformed, making it difficult to leverage abundant source recordings from a specific population or cohort for targeted augmentation. We introduce PulseFlow, a source-conditioned counterfactual generation framework that combines conditional representation learning with invertible latent transport to edit cardiac rhythm while retaining information from the source. Experiments across two clinical cohorts demonstrate effective rhythm transformation, measurable source correspondence, and improved AF classification under limited labels.
