Continuous monitoring of vital signs in Pediatric Intensive Care Units (PICUs) is essential for early detection of clinical deterioration and effective clinical decision-making. However, contact-based sensors such as pulse oximeters may cause skin irritation, increase infection risk, and lead to patient discomfort. Remote photoplethysmography (rPPG) offers a contactless alternative to monitor heart rate using facial video, but remains underutilized in PICUs due to motion artifacts, occlusions, variable lighting, and domain shifts between laboratory and clinical data. We introduce a self-supervised pretraining framework for rPPG estimation in the PICU setting, based on a progressive curriculum strategy. The approach leverages the VisionMamba architecture and integrates an adaptive masking mechanism, where a lightweight Mamba-based controller assigns spatiotemporal importance scores to guide probabilistic patch sampling. This strategy dynamically increases reconstruction difficulty while preserving physiological relevance. To address the lack of labeled clinical data, we adopt a teacher-student distillation setup. A supervised expert model, trained on public datasets, provides latent physiological guidance to the student. The curriculum progresses through three stages: clean public videos, synthetic occlusion scenarios, and unlabeled videos from 500 pediatric patients. Our framework achieves a 42% reduction in mean absolute error relative to standard masked autoencoders and outperforms PhysFormer by 31%, reaching a final MAE of 3.2 bpm. Without explicit region-of-interest extraction, the model consistently attends to pulse-rich areas and demonstrates robustness under clinical occlusions and noise.

翻译：儿科重症监护室（PICU）中生命体征的持续监测对于早期发现临床病情恶化及有效临床决策至关重要。然而，基于接触的传感器（如脉搏血氧仪）可能引起皮肤刺激、增加感染风险并导致患者不适。远程光电容积描记术（rPPG）提供了一种利用面部视频监测心率的非接触式替代方案，但由于运动伪影、遮挡、光照变化以及实验室数据与临床数据之间的领域偏移，该技术在PICU中仍未得到充分利用。本文提出一种基于渐进式课程策略的自监督预训练框架，用于PICU环境下的rPPG估计。该方法采用VisionMamba架构，并集成自适应掩码机制——通过轻量级Mamba控制器分配时空重要性分数以指导概率性图像块采样。该策略在保持生理相关性的同时动态提升重建难度。针对临床标注数据匮乏的问题，我们采用师生蒸馏架构：在公开数据集上训练的监督式专家模型为学生模型提供潜在生理指导。课程设置包含三个阶段：洁净公开视频、合成遮挡场景以及来自500名儿科患者的未标注视频。相较于标准掩码自编码器，本框架将平均绝对误差降低42%，并以31%的优势超越PhysFormer，最终达到3.2 bpm的MAE。在无需显式提取感兴趣区域的情况下，该模型能持续关注脉搏信号富集区域，并在临床遮挡与噪声干扰下表现出鲁棒性。