Department of Physics and Telecommunication Engineering, South China Normal University, Guangzhou 510006, China.
Department of Electronic and Information Engineering, South China Normal University, Foshan 528000, China.
J Healthc Eng. 2022 Jul 8;2022:2016598. doi: 10.1155/2022/2016598. eCollection 2022.
As a physiological phenomenon, sleep takes up approximately 30% of human life and significantly affects people's quality of life. To assess the quality of night sleep, polysomnography (PSG) has been recognized as the gold standard for sleep staging. The drawbacks of such a clinical device, however, are obvious, since PSG limits the patient's mobility during the night, which is inconvenient for in-home monitoring. In this paper, a noncontact vital signs monitoring system using the piezoelectric sensors is deployed. Using the so-designed noncontact sensing system, heartbeat interval (HI), respiratory interval (RI), and body movements (BM) are separated and recorded, from which a new dimension of vital signs, referred to as the coordination of heartbeat interval and respiratory interval (CHR), is obtained. By extracting both the independent features of HI, RI, and BM and the coordinated features of CHR in different timescales, Wake-REM-NREM sleep staging is performed, and a postprocessing of staging fusion algorithm is proposed to refine the accuracy of classification. A total of 17 all-night recordings of noncontact measurement simultaneous with PSG from 10 healthy subjects were examined, and the leave-one-out cross-validation was adopted to assess the performance of Wake-REM-NREM sleep staging. Taking the gold standard of PSG as reference, numerical results show that the proposed sleep staging achieves an averaged accuracy and Cohen's Kappa index of 82.42% and 0.63, respectively, and performs robust to subjects suffering from sleep-disordered breathing.
作为一种生理现象，睡眠占据了人类生命的约 30%，并显著影响着人们的生活质量。为了评估夜间睡眠质量，多导睡眠图（PSG）已被视为睡眠分期的金标准。然而，这种临床设备存在明显的缺点，因为 PSG 限制了患者在夜间的活动，这对于家庭监测来说很不方便。在本文中，我们部署了一种使用压电传感器的非接触式生命体征监测系统。使用所设计的非接触式传感系统，可以分离和记录心跳间隔（HI）、呼吸间隔（RI）和身体运动（BM），从中获得一个新的生命体征维度，称为心跳间隔和呼吸间隔的协调（CHR）。通过提取 HI、RI 和 BM 的独立特征以及 CHR 在不同时间尺度上的协调特征，进行了清醒-快速眼动（REM）-非快速眼动（NREM）睡眠分期，并提出了分期融合算法的后处理来提高分类的准确性。总共对 10 位健康受试者进行了 17 次整夜的非接触式测量与 PSG 同步记录，采用留一法交叉验证评估了清醒-REM-NREM 睡眠分期的性能。以 PSG 的金标准为参考，数值结果表明，所提出的睡眠分期方法的平均准确率和 Cohen's Kappa 指数分别为 82.42%和 0.63，并且对患有睡眠呼吸障碍的受试者具有较强的鲁棒性。
