基于主体相对微运动与睡眠动力学的近红外视频睡眠分期

中文摘要 AI 辅助

近红外（NIR）视频是一种有前景的非接触式睡眠监测模态，但近期基于视频的睡眠分期方法通常将其用作重建呼吸/心脏代理信号或跨模态生理表征的途径。我们研究了在由多导睡眠监测（PSG）定义的标签下进行纯视频睡眠分期，其中模型仅从NIR视频推断睡眠阶段，无需显式的生理代理重建或辅助生理信号监督。这检验了NIR视频本身是否能提供信息丰富的睡眠阶段证据，而不仅仅是作为恢复生理代理的输入。我们提出了ViNUSS（Video-Native Unmediated Sleep Staging，视频原生无中介睡眠分期）框架，该框架将主体相对微运动学习与整夜睡眠动力学建模相结合。空间锚定的预空间微运动编码保留了局部时间变化及其空间上下文。受试者内阶段对比学习相对于每个受试者夜间特定基线的阶段线索。两尺度睡眠动力学建模捕获了epoch内的运动演化，并将epoch级证据组织成连贯的整夜睡眠阶段轨迹。在475个夜间NIR记录（约3,250小时）上，ViNUSS在四类睡眠分期中实现了0.80的准确率和0.78的宏F1分数。可解释性分析表明，模型关注与觉醒和体位变化相关的胸腹周期性运动及大体运动。这些结果支持NIR视频作为PSG定义睡眠阶段估计的一种独立信息丰富且互补的模态。

英文摘要

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

发表机构

• Seoul National University（首尔大学）
• Seoul National University Hospital（首尔大学医院）

机构由 AI 辅助整理，请以论文原文为准。