Dang Thi Hang, Kim Seong-Mun, Choi Min-Seong, Hwan Sung-Nam, Min Hyung-Ki, Bien Franklin
Department of Electrical Engineering, Ulsan National Institute of Science and Technology, 50, UNIST-gil, Ulsan 44919, Republic of Korea.
SB Solutions Inc., Ulsan National Institute of Science and Technology, Ulsan 44919, Republic of Korea.
Sensors (Basel). 2025 Apr 10;25(8):2412. doi: 10.3390/s25082412.
Obstructive sleep apnea (OSA) is common among older populations and individuals with cardiovascular diseases. OSA diagnosis is primarily conducted using polysomnography or recommended home sleep apnea test (HSAT) devices. Wireless wearable devices have emerged as promising tools for OSA screening and follow-up. This study introduces a novel automated algorithm for detecting OSA using abdominal movement signals and acceleration data collected by a wireless abdomen-worn sensor (Soomirang). Thirty-seven subjects underwent overnight monitoring using an HSAT device and the Soomirang system simultaneously. Normal and apnea events were classified using an MLP-Mixer deep learning model based on Soomirang data, which was also used to estimate total sleep time (ST). Pearson correlation and Bland-Altman analyses were conducted to evaluate the agreement of ST and the apnea-hypopnea index (AHI) calculated by the HSAT device and Soomirang. ST demonstrated a correlation of 0.9 with an average time difference of 7.5 min, while AHI showed a correlation of 0.95 with an average AHI difference of 3. The accuracy, sensitivity, and specificity of the Soomirang for detecting OSA were 97.14%, 100%, and 95.45% at AHI ≥ 15, respectively. The proposed algorithm, utilizing data from a wireless abdomen-worn device exhibited excellent performance in detecting moderate to severe OSA. The findings underscored the potential of a simple device as an accessible and effective tool for OSA screening and follow-up.
阻塞性睡眠呼吸暂停（OSA）在老年人群和患有心血管疾病的个体中很常见。OSA诊断主要通过多导睡眠图或推荐的家庭睡眠呼吸暂停测试（HSAT）设备进行。无线可穿戴设备已成为OSA筛查和随访的有前景的工具。本研究介绍了一种使用无线腹部佩戴传感器（Soomirang）收集的腹部运动信号和加速度数据检测OSA的新型自动化算法。37名受试者同时使用HSAT设备和Soomirang系统进行了夜间监测。基于Soomirang数据，使用MLP-Mixer深度学习模型对正常和呼吸暂停事件进行分类，该数据还用于估计总睡眠时间（ST）。进行Pearson相关性分析和Bland-Altman分析，以评估HSAT设备和Soomirang计算的ST和呼吸暂停低通气指数（AHI）的一致性。ST的相关性为0.9，平均时间差为7.5分钟，而AHI的相关性为0.95，平均AHI差为3。当AHI≥15时，Soomirang检测OSA的准确性、敏感性和特异性分别为97.14%、100%和95.45%。所提出的算法利用来自无线腹部佩戴设备的数据，在检测中度至重度OSA方面表现出优异的性能。研究结果强调了这种简单设备作为OSA筛查和随访的便捷有效工具的潜力。
