RAE-PPG：面向PPG基础模型的持续时间锚定保留与扩展预训练
RAE-PPG: Duration-Grounded Retain-and-Extend Pretraining for PPG Foundation Models
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中文总结 AI 辅助
RAE-PPG提出持续时间锚定的保留与扩展预训练方法，通过逐步增加输入时长并复用早期参数与目标，使单一PPG编码器渐进获取特征，在18项任务中12项超越现有模型。
中文摘要 AI 辅助
由光电容积描记（PPG）衍生的信号特征需要不同的信号持续时间来表征。现有的PPG基础模型将持续时间视为预训练或评估条件，而非利用PPG特征所需的不同持续时间来组织自监督。我们假设自监督应随信号持续时间而扩展，使得单一编码器能够逐步获取额外特征，同时保留并复用早期学习成果。我们提出保留与扩展PPG（RAE-PPG），该方法在10秒、30秒和240秒的输入上依次训练单一Transformer编码器，为每次更长观察所支持的信号特征增加监督。编码器被划分为持续时间特定的参数组，使得后续阶段能够复用早期组，同时仅更新分配给当前阶段的组。选定的早期目标被复用以监督后续阶段，促使相应特征在更长输入表示中保持可访问性。从最终编码器直接解码表明，早期特征在更长输入表示中仍可恢复，而后期特征在其引入持续时间下表现出更高的平均解码性能。受控比较进一步表明，先前阶段的学习为两个转换点处新引入特征的学习提供了更好的基础。在来自八个数据集的18项任务中，最终冻结编码器在12项任务上取得了最佳观测得分，优于五种现有PPG基础模型。
英文摘要
Signal features derived from photoplethysmography (PPG) require different signal durations to characterize. Existing PPG foundation models treat duration as a pretraining or evaluation condition rather than using the different durations required by PPG features to organize self-supervision. We hypothesize that self-supervision should expand with signal duration, allowing a single encoder to progressively acquire additional features while preserving and reusing earlier learning. We introduce Retain-and-Extend PPG (RAE-PPG), which trains a single Transformer encoder successively on 10 s, 30 s, and 240 s inputs, adding supervision for signal features supported by each longer observation. The encoder is partitioned into duration-specific parameter groups, allowing later stages to reuse earlier groups while updating only the group assigned to the current stage. Selected earlier targets are reused to supervise later stages, encouraging the corresponding features to remain accessible in longer-input representations. Direct decoding from the final encoder shows that earlier features remain recoverable from longer-input representations, while later-stage features show higher mean decoding performance at their introduction durations. Controlled comparisons further show that prior-stage learning provides a better basis for learning newly introduced features at both transitions. Across 18 tasks from eight datasets, the final frozen encoder achieves the best observed score on 12 tasks compared with five existing PPG foundation models.
发表机构
- Korea University（高丽大学）
机构由 AI 辅助整理，请以论文原文为准。
