用流匹配模型从PPG信号中高精度重建多种生命体征波形。
PENGUIN: General Vital Sign Reconstruction from PPG with Flow Matching State Space Model
- 基于流匹配的时序生成框架，实现对PPG信号的细粒度建模。
- 在6个真实数据集上优于单任务与通用基线方法，跨任务表现稳定。
- 适合临床连续心率监测、呼吸分析等多场景生命体征解码应用。
光电容积脉搏波（PPG）作为非侵入式且低成本的心血管健康监测手段至关重要。然而，PPG信号易受运动伪影和噪声干扰，导致动脉血压（ABP）等生命体征的准确估计困难。现有方法通常局限于单一任务或特定环境，泛化能力受限；近期通用方法多依赖数秒级预测，忽略了生命体征的形态特征。为此，我们提出PENGUIN，一种扩展深度状态空间模型的生成式流匹配框架，可对PPG进行细粒度条件建模，实现多生命体征的连续波形重建。我们在三个不同任务（心电图重建、呼吸监测、ABP监测）的六个真实世界PPG数据集上评估该方法，结果表明其持续优于各类任务专用与通用基线，验证了PENGUIN作为鲁棒生命体征重建通用框架的有效性。
原文摘要 · Abstract (English)
Photoplethysmography (PPG) plays a crucial role in continuous cardiovascular health monitoring as a non-invasive and cost-effective modality. However, PPG signals are susceptible to motion artifacts and noise, making accurate estimation of vital signs such as arterial blood pressure (ABP) challenging. Existing estimation methods are often restricted to a single-task or environment, limiting their generalizability across diverse PPG decoding scenarios. Moreover, recent general-purpose approaches typically rely on predictions over multi-second intervals, discarding the morphological characteristics of vital signs. To address these challenges, we propose PENGUIN, a generative flow-matching framework that extends deep state space models, enabling fine-grained conditioning on PPG for reconstructing multiple vital signs as continuous waveforms. We evaluate PENGUIN using six real-world PPG datasets across three distinct vital sign reconstruction tasks (electrocardiogram reconstruction, respiratory monitoring, and ABP monitoring). Our method consistently outperformed both task-specific and general-purpose baselines, demonstrating PENGUIN as a general framework for robust vital sign reconstruction from PPG.
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