TY - JOUR AU - akbararian, Sina AU - Ghahjaverestan, Nasim Montazeri AU - Yadollahi, Azadeh AU - Taati, Babak PY - 2021 DA - 2021/11/1 TI -基于红外视频数据的非接触式睡眠监测评估睡眠呼吸暂停严重程度及区分体位和非体位睡眠呼吸暂停:模型开发和实验验证JO - J Med Internet Res SP - e26524 VL - 23 IS - 11 KW -睡眠呼吸暂停KW -深度学习KW -非接触监测KW -计算机视觉KW -体位睡眠呼吸暂停KW - 3D卷积神经网络KW - 3D- cnn AB -背景:睡眠呼吸暂停是一种以睡眠期间频繁呼吸停止为特征的呼吸系统疾病。睡眠呼吸暂停的严重程度是由呼吸暂停低通气指数(AHI)决定的，AHI是每小时呼吸事件的发生率。在体位睡眠呼吸暂停中，仰卧睡姿的AHI高于其他睡姿。体位疗法是一种治疗体位性呼吸暂停的行为策略(例如，穿着一件鼓励朝侧卧位睡觉的物品)。诊断睡眠呼吸暂停和是否是体位性的金标准是多导睡眠图;然而，这种测试不方便，昂贵，并且有很长的等待名单。目的:本研究的目的是发展和评估一种非接触方法来估计睡眠呼吸暂停的严重程度，并区分体位和非体位睡眠呼吸暂停。方法:采用非接触深度学习算法分析睡眠红外视频，估计AHI，并区分体位与非体位睡眠呼吸暂停患者。具体而言，采用三维卷积神经网络(CNN)架构对光流提取的运动进行处理，以检测呼吸事件。 Positional sleep apnea patients were subsequently identified by combining the AHI information provided by the 3D-CNN model with the sleeping position (supine vs lateral) detected via a previously developed CNN model. Results: The algorithm was validated on data of 41 participants, including 26 men and 15 women with a mean age of 53 (SD 13) years, BMI of 30 (SD 7), AHI of 27 (SD 31) events/hour, and sleep duration of 5 (SD 1) hours; 20 participants had positional sleep apnea, 15 participants had nonpositional sleep apnea, and the positional status could not be discriminated for the remaining 6 participants. AHI values estimated by the 3D-CNN model correlated strongly and significantly with the gold standard (Spearman correlation coefficient 0.79, P<.001). Individuals with positional sleep apnea (based on an AHI threshold of 15) were identified with 83% accuracy and an F1-score of 86%. Conclusions: This study demonstrates the possibility of using a camera-based method for developing an accessible and easy-to-use device for screening sleep apnea at home, which can be provided in the form of a tablet or smartphone app. SN - 1438-8871 UR - //www.mybigtv.com/2021/11/e26524 UR - https://doi.org/10.2196/26524 UR - http://www.ncbi.nlm.nih.gov/pubmed/34723817 DO - 10.2196/26524 ID - info:doi/10.2196/26524 ER -