用ECG关联表征增强PPG，提升心电相关任务表现
P2E-VQ: ECG-linked representation augmentation for PPG via discrete patch retrieval
- 通过离散块检索，从记忆库中获取ECG相关特征增强PPG表征
- 在五个数据集上六项任务均优于预训练基线模型
- 推理时仅需PPG信号，适合可穿戴设备实时应用
光电容积脉搏波（PPG）因成本低、采集方便被广泛用于消费级可穿戴设备。然而，与心电图（ECG）不同，PPG测量的是外周脉搏动态而非心脏电活动，难以捕捉依赖于ECG形态特征的心脏病预测。现有方法尝试从PPG重建ECG信号，但该逆向映射本质上是不适定的，且波形重建的准确性并不必然带来下游性能提升。为此，我们提出P2E-VQ，一种基于检索增强的框架，将ECG波形重建替换为ECG关联表征检索。具体而言，P2E-VQ将PPG片段转化为离散标记，并从仅由训练数据构建的记忆库中检索出与ECG相关的表征信息，从而增强PPG表征，且推理阶段仅需PPG信号。在涵盖六个下游任务的五个公开数据集上的大量实验表明，在统一的冻结特征线性探测协议下，P2E-VQ始终优于预训练基线模型。
原文摘要 · Abstract (English)
Photoplethysmography (PPG) is widely used in consumer wearables because of its low cost and ease of acquisition. However, unlike electrocardiography (ECG), PPG measures peripheral pulse dynamics rather than cardiac electrical activity, limiting its ability to predict cardiac conditions that rely on ECG-specific morphological cues. Existing methods attempt to bridge this gap by reconstructing ECG signals from PPG signals. However, this inverse mapping is inherently ill-posed, and faithful waveform reconstruction does not necessarily translate into improved downstream performance. To address this challenge, we propose P2E-VQ, a retrieval-augmented framework that replaces ECG waveform reconstruction with ECG-linked representation retrieval. Specifically, P2E-VQ converts PPG patches into discrete tokens and retrieves ECG-linked information from a memory bank constructed exclusively from the training data. This process augments PPG representations while requiring only PPG signals during inference. Extensive experiments on five public datasets covering six downstream tasks, including clinical endpoint prediction and affective state recognition, demonstrate that P2E-VQ consistently outperforms pretrained baselines under a unified frozen-feature linear-probing protocol.
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