使用Sleepal AI灯进行准确睡眠分期检测的非接触式方法
A Contactless Approach for Accurate Sleep Stage Detection Using the Sleepal AI Lamp.
文献信息
| PMID | 42838096 |
|---|---|
| 原文 | 在 PubMed 查看原文 ↗ |
| 发表日期 | 2026 |
| 作者 | Zhuo Diao |
| 作者单位 | Hong Kong Xsmart Century Technology Co., Ltd, Flat 08A, 19/F, China Shipbuilding Tower, 650 Cheung Sha Wan Road, Kowloon, Kowloon, 999077, Hong Kong. |
| 期刊 | Physiological measurement |
| SCI 分区 | Q3 |
| IF | 2.6 |
| 研究类型 | AI/ML · 临床 |
| 所属专科 | 耳科 |
中文摘要
目的： 验证一种非接触式雷达系统（Sleepal AI灯）在评估睡眠结构和分期方面的准确性和临床可行性。尽管非接触式雷达传感器无需身体接触即可捕捉呼吸和运动力学，但在大规模、病理多样化的队列中仍缺乏稳健的验证。
方法： 使用调频连续波（FMCW）床边雷达与金标准PSG同步测量，对1,022名成年人的夜间呼吸和身体运动信号进行评估。该队列包括睡眠特征正常的个体和患有不同严重程度阻塞性睡眠呼吸暂停（OSA）的患者。一种频率增强的深度学习架构解读了这些呼吸和运动信号。
主要结果： 在独立验证集中，二分类睡眠-觉醒分类达到92.7%的准确率、Cohen's kappa为0.792、睡眠敏感性96.4%、睡眠特异性80.8%、宏平均F1分数0.896。四阶段分类（觉醒、浅睡、深睡和快速眼动（REM）睡眠）达到77.2%的准确率、Cohen's kappa为0.677、宏平均F1分数0.768、平衡准确率/宏召回率77.4%，觉醒、浅睡、深睡和REM的特定阶段F1分数分别为0.839、0.740、0.691和0.802。
意义： 研究结果支持基于60 GHz雷达的系统用于自动评估睡眠宏观结构的有效性。通过无需身体接触捕捉特定阶段的呼吸和运动表型，该技术可能为家庭筛查、纵向恢复跟踪和人群水平睡眠流行病学提供一种无干扰的工具。
英文摘要
To validate the accuracy and clinical feasibility of a noncontact radar system (Sleepal AI Lamp) for assessing sleep architecture and staging. Although noncontact radar sensors can capture respiratory and motor mechanics without physical attachment, robust validation in large-scale, pathologically diverse cohorts remains scarce.

Approach: Nocturnal respiratory and body movement signals were assessed in 1,022 adults using simultaneous measurements from a frequency-modulated continuous-wave (FMCW) bedside radar and gold-standard PSG. The cohort included individuals with normal sleep profiles and patients with varying severities of obstructive sleep apnea (OSA). A frequency-augmented deep learning architecture interpreted these respiratory and movement signals.

Main results: In the independent validation set, binary sleep-wake classification achieved 92.7% accuracy, a Cohen's kappa of 0.792, 96.4% sleep sensitivity, 80.8% sleep specificity, and a macro-averaged F1 score of 0.896. Four-stage classification (Wake, Light, Deep, and rapid eye movement (REM) sleep) achieved 77.2% accuracy, a Cohen's kappa of 0.677, a macro-averaged F1 score of 0.768, and balanced accuracy/macro-recall of 77.4%, with stage-specific F1 scores of 0.839, 0.740, 0.691, and 0.802 for Wake, Light, Deep, and REM, respectively.

Significance: The findings support the validity of the 60 GHz radar-based system for automated assessment of sleep macrostructure. By capturing stage-specific respiratory and movement phenotypes without physical attachment, this technology may provide an unobtrusive tool for home-based screening, longitudinal recovery tracking, and population-level sleep epidemiology.