Wang Jia, Xue Jiangtao, Zou Yang, Ma Yuxin, Xu Junhan, Li Yanming, Deng Fei, Wang Yiqian, Xing Kai, Li Zhou, Zou Tong
Department of Cardiology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100730, China.
Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences, Beijing, 101400, China.
Adv Sci (Weinh). 2025 Jun;12(24):e2501750. doi: 10.1002/advs.202501750. Epub 2025 Apr 29.
Despite being a serious health condition that significantly increases cardiovascular and metabolic disease risks, sleep apnea syndrome (SAS) remains largely underdiagnosed. While polysomnography (PSG) remains the gold standard for diagnosis, its clinical application is limited by high costs, complex setup requirements, and sleep quality interference. Although wearable devices using photoplethysmography (PPG) have shown promise in SAS detection, their continuous operation demands substantial power consumption, hindering long-term monitoring capabilities. Here, a dual-modal wearable system is presented integrating a piezoelectric nanogenerator (PENG) and PPG sensor with a biomimetic fingertip structure for SAS detection. A two-stage detection strategy is adopted where the self-powered PENG performs continuous preliminary screening, activating the PPG sensor only when suspicious events are detected. Combined with a Vision Transformer-based deep learning model, the high-accuracy configuration achieves 99.59% accuracy, while the low-power two-stage approach maintained 94.95% accuracy. This dual-modal wearable pulse detection system provides a practical solution for long-term SAS monitoring, overcoming the limitations of traditional PSG while maintaining high detection accuracy. The system's versatility in both home and clinical settings offers the potential for improving early detection rates and treatment outcomes for SAS patients.
尽管睡眠呼吸暂停综合征（SAS）是一种严重的健康状况，会显著增加心血管和代谢疾病风险，但在很大程度上仍未得到充分诊断。虽然多导睡眠图（PSG）仍然是诊断的金标准，但其临床应用受到高成本、复杂的设置要求以及对睡眠质量的干扰的限制。尽管使用光电容积脉搏波描记法（PPG）的可穿戴设备在SAS检测方面显示出前景，但其持续运行需要大量功耗，阻碍了长期监测能力。在此，提出了一种双模式可穿戴系统，该系统将压电纳米发电机（PENG）和PPG传感器集成到具有仿生指尖结构的设备中用于SAS检测。采用了两阶段检测策略，其中自供电的PENG进行连续的初步筛查，仅在检测到可疑事件时才激活PPG传感器。结合基于视觉Transformer的深度学习模型，高精度配置的准确率达到99.59%，而低功耗两阶段方法的准确率保持在94.95%。这种双模式可穿戴脉搏检测系统为长期SAS监测提供了一种实用解决方案，克服了传统PSG的局限性，同时保持了高检测准确率。该系统在家庭和临床环境中的通用性为提高SAS患者的早期检测率和治疗效果提供了潜力。
