A Contactless Approach for Accurate Sleep Stage Detection Using the Sleepal AI Lamp.

Authors

Zhuo Diao, Yueting Li, Jianpeng Wang, Shengyu Guan, Xinwei Wang, Wenxiong Cui, Xin Shi, Tong Liu, Kailai Sun, Jingyu Wang, Dian Fan, Thomas Penzel

Published in

Physiological measurement. Oct 06, 2026. Epub Oct 06, 2026.

Abstract

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.

PMID:
42838096
Bibliographic data and abstract were imported from PubMed on 07 Oct 2026.

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