[论文解读] A Large-Scale Study of a Sleep Tracking and Improving Device with Closed-loop and Personalized Real-time Acoustic Stimulation

Anh Nguyen, Galen Pogoncheff+9 more|arXiv (Cornell University)|Nov 4, 2022

Sleep and Wakefulness Research被引用 5

一句话总结

本研究提出 Earable，一种可穿戴头带，可实现实时、闭环、个性化的声刺激，以改善入睡时间。通过多信号生理传感和自适应音频提示，其在377名受试者的大型试验中，与临床睡眠评分的符合率达87.8%，并将入睡潜伏期减少了24.1分钟。

ABSTRACT

Various intervention therapies ranging from pharmaceutical to hi-tech tailored solutions have been available to treat difficulty in falling asleep commonly caused by insomnia in modern life. However, current techniques largely remain ill-suited, ineffective, and unreliable due to their lack of precise real-time sleep tracking, in-time feedback on the therapies, an ability to keep people asleep during the night, and a large-scale effectiveness evaluation. Here, we introduce a novel sleep aid system, called Earable, that can continuously sense multiple head-based physiological signals and simultaneously enable closed-loop auditory stimulation to entrain brain activities in time for effective sleep promotion. We develop the system in a lightweight, comfortable, and user-friendly headband with a comprehensive set of algorithms and dedicated own-designed audio stimuli. We conducted multiple protocols from 883 sleep studies on 377 subjects (241 women, 119 men) wearing either a gold-standard device (PSG), Earable, or both concurrently. We demonstrate that our system achieves (1) a strong correlation (0.89 +/- 0.03) between the physiological signals acquired by Earable and those from the gold-standard PSG, (2) an 87.8 +/- 5.3% agreement on sleep scoring using our automatic real-time sleep staging algorithm with the consensus scored by three sleep technicians, and (3) a successful non-pharmacological stimulation alternative to effectively shorten the duration of sleep falling by 24.1 +/- 0.1 minutes. These results show that the efficacy of Earable exceeds existing techniques in intentions to promote fast falling asleep, track sleep state accurately, and achieve high social acceptance for real-time closed-loop personalized neuromodulation-based home sleep care.

研究动机与目标

• 开发一种非药物性、可穿戴的解决方案，以改善失眠患者的入睡时间。
• 基于生理反馈，实现实时、闭环的神经调节，使用个性化的声刺激。
• 在大规模、真实世界家庭睡眠环境中验证系统的准确性和有效性。
• 将 Earable 的生理信号采集和睡眠分期性能与金标准多导睡眠图（PSG）进行对比。

提出的方法

• 设计了一款轻便、用户友好的头带，可连续监测多种头部生理信号（如 EEG、EOG、EMG）。
• 实现了基于生理输入的机器学习算法，实时分类睡眠阶段。
• 开发了专有的音频刺激，根据个体脑电波模式进行定制，实现闭环听觉刺激。
• 通过反馈回路实现实时音频提示，与特定睡眠阶段（如 N1、N2）同步，以促进入睡。
• 集成了个性化刺激方案，可随重复会话中的个体反应自适应调整。
• 在377名参与者中，共进行了883次睡眠研究，同步记录了金标准多导睡眠图（PSG），以验证性能。

实验结果

研究问题

• RQ1 可穿戴的闭环声刺激系统是否能在真实世界家庭环境中有效减少入睡潜伏期？
• RQ2 Eaable 系统在睡眠分期估计方面与临床多导睡眠图（PSG）和专家共识评分相比，准确度如何？
• RQ3 个性化、实时声刺激在无药物干预的情况下，对改善入睡的改善程度如何？
• RQ4 该系统在大规模研究中对多样化用户群体的性能是否具有可扩展性和可靠性？
• RQ5 Eaable 的生理信号与金标准多导睡眠图在睡眠期间的相关性如何？

主要发现

• Eaable 系统的生理信号与金标准多导睡眠图记录的信号之间表现出强相关性（0.89 ± 0.03）。
• 该系统通过其实时自动睡眠分期算法，与三位睡眠技师的共识评分达成87.8%的一致性。
• 使用闭环声刺激后，参与者入睡潜伏期显著减少24.1 ± 0.1分钟。
• 该设备在377名受试者的多样化人群中表现出高度的用户接受度和长期家庭使用的可行性。
• 个性化的声刺激方案有效调节脑活动，促进更快入睡。
• 系统在男性和女性中均保持高性能，且在各人口统计子群体中结果一致。