无需生理信号，仅用近红外视频就能准确判断睡眠阶段。
Subject-Relative Micro-Motion and Sleep Dynamics for Near-Infrared Video Sleep Staging
- 基于个体微动与整夜动态建模，直接从视频推断睡眠分期。
- 在475个记录上达到0.80准确率和0.78宏F1值。
- 适合无接触睡眠监测场景，可解释性强。
近红外（NIR）视频是无接触睡眠监测的有前景方式，但现有视频睡眠分期方法常将其作为重建呼吸/心率代理或跨模态生理表征的途径。本文研究在多导睡眠图（PSG）标签定义下的纯视频睡眠分期，模型仅从NIR视频中推断睡眠阶段，不显式重构生理代理或依赖辅助生理信号监督。这验证了NIR视频本身是否具备可判别的睡眠分期信息，而非仅用于恢复生理信号。我们提出ViNUSS（Video-Native Unmediated Sleep Staging）框架，结合个体相对微动学习与全夜睡眠动态建模：空间锚定的预空间微动编码保留局部时间变化及其空间上下文；个体内部阶段对比学习以个体夜间基线为参照提取阶段线索；双尺度睡眠动态建模捕捉段内运动演化，并将段级证据整合为连贯的整夜睡眠分期轨迹。在475例夜间NIR记录（约3,250小时）上，ViNUSS实现0.80准确率与0.78宏F1值（四类睡眠分期）。可解释性分析表明注意力集中在胸腹部周期性运动及与觉醒、体位变化相关的整体动作。结果支持NIR视频作为独立且互补的模态，用于基于PSG定义的睡眠分期估计。
原文摘要 · Abstract (English)
Near-infrared (NIR) video is a promising modality for contactless sleep monitoring, but recent video-based sleep staging methods often use it as a route to reconstructed respiratory/cardiac proxies or cross-modal physiological representations. We study video-only sleep staging under labels defined by polysomnography (PSG), where the model infers sleep stages from NIR video alone without explicit physiological proxy reconstruction or auxiliary physiological signal supervision. This tests whether NIR video itself can provide informative sleep-stage evidence, rather than only serving as an input for recovering physiological proxies. We propose ViNUSS (Video-Native Unmediated Sleep Staging), a framework that combines subject-relative micro-motion learning with full-night sleep dynamics modeling. Spatially anchored pre-spatial micro-motion encoding preserves localized temporal variation together with its spatial context. Within-subject stage contrast learns stage cues with respect to each subject's night-specific baseline. Two-scale sleep dynamics modeling captures within-epoch motion evolution and organizes epoch-level evidence into a coherent full-night sleep-stage trajectory. On 475 overnight NIR recordings (~3,250 hours), ViNUSS achieves 0.80 accuracy and 0.78 macro-F1 for four-class sleep staging. Interpretability analysis suggests attention to thoraco-abdominal periodic motion and gross body movements associated with arousals and position changes. These results support NIR video as an independently informative and complementary modality for PSG-defined sleep-stage estimation
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