Um Hyun-Kyung, Noh Eunseo, Yoo Chaehwa, Lee Hyang Woon, Kang Je-Won, Lee Byoung Hoon, Lee Jung-Rok
Department of Mechanical and Biomedical Engineering, Ewha Womans University, Seoul 03760, Republic of Korea.
Graduate Program in Smart Factory, Ewha Womans University, Seoul 03760, Republic of Korea.
ACS Sens. 2025 Jun 27;10(6):4016-4026. doi: 10.1021/acssensors.4c03602. Epub 2025 May 15.
The prevalence of sleep disorders in the aging population and the importance of sleep quality for health have emphasized the need for accurate and accessible sleep monitoring solutions. Polysomnography (PSG) remains the clinical gold standard for diagnosing sleep disorders; however, its discomfort and inconvenience limit its accessibility. To address these issues, a wearable device (WD) integrated with stretchable transparent electrodes (STEs) is developed in this study for multisignal sleep monitoring and artificial intelligence (AI)-driven sleep staging. Utilizing conductive and flexible STEs, the WD records multiple biological signals (electroencephalogram [EEG], electrooculogram [EOG], electromyogram [EMG], photoplethysmography, and motion data) with high precision and low noise, comparable to PSG (<4 μV). It achieves a 73.2% accuracy and a macro F1 score of 0.72 in sleep staging using an AI model trained on multisignal inputs. Notably, accuracy marginally improves when using only the EEG, EOG, and EMG channels, which may simplify future device designs. This WD offers a compact, multisignal solution for at-home sleep monitoring, with the potential for use as an evaluation tool for personalized sleep therapies.
老年人群中睡眠障碍的患病率以及睡眠质量对健康的重要性凸显了对准确且便捷的睡眠监测解决方案的需求。多导睡眠图（PSG）仍然是诊断睡眠障碍的临床金标准；然而，其带来的不适和不便限制了其可及性。为解决这些问题，本研究开发了一种集成了可拉伸透明电极（STE）的可穿戴设备（WD），用于多信号睡眠监测和人工智能（AI）驱动的睡眠分期。利用导电且灵活的STE，WD能够高精度、低噪声地记录多种生物信号（脑电图[EEG]、眼电图[EOG]、肌电图[EMG]、光电容积脉搏波描记法和运动数据），与PSG相当（<4 μV）。在使用基于多信号输入训练的AI模型进行睡眠分期时，它实现了73.2%的准确率和0.72的宏F1分数。值得注意的是，仅使用EEG、EOG和EMG通道时准确率略有提高，这可能会简化未来的设备设计。这种WD为家庭睡眠监测提供了一种紧凑的多信号解决方案，具有用作个性化睡眠疗法评估工具的潜力。
