A skin-interfaced wireless wearable device and data analytics approach for sleep-stage and disorder detection

• 成果类型: Article
• 署名作者: Du, Yayun; Gu, Jianyu; Duan, Shiyuan; Trueb, Jacob; Tzavelis, Andreas; Shin, Hee-Sup; Arafa, Hany; Li, Xiuyuan; Huang, Yonggang; Carr, Andrew N.; Davies, Charles R.; Rogers, John A.
• 署名单位: Northwestern University; Northwestern University; Northwestern University; Feinberg School of Medicine; Northwestern University; Procter & Gamble; Northwestern University; Northwestern University; Northwestern University
• 刊物名称: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• ISSN/ISSBN: 0027-14285
• 发表日期: 2025-06-10
• 关键词: heart-rate-variability; poincare plot; validation; time

摘要

Accurate identification of sleep stages and disorders is crucial for maintaining health, preventing chronic conditions, and improving diagnosis and treatment. Direct respiratory measurements, as key biomarkers, are missing in traditional wrist-or finger-worn wearables, which thus limit their precision in detection of sleep stages and sleep disorders. By contrast, this work introduces a simple, multimodal, skin-integrated, energy-efficient mechanoacoustic sensor capable of synchronized cardiac and respiratory measurements. The mechanical design enhances sensitivity and durability, enabling continuous, wireless monitoring of essential vital signs (respiration rate, heart rate and corresponding variability, temperature) and various physical activities. Systematic physiology-based analytics involving explainable machine learning allows both precise sleep characterization and transparent tracking of each factor's contribution, demonstrating the dominance of respiration, as validated through a diverse range of human subjects, both healthy and with sleep disorders. This methodology enables cost-effective, clinical-quality sleep tracking with minimal user effort, suitable for home and clinical use.