Bianchi M T, Lipoma T, Darling C, Alameddine Y, Westover M B
1. Neurology Department, Sleep Division, Massachusetts General Hospital, Boston MA, USA ; 2. Division of Sleep Medicine, Harvard Medical School, Boston, MA, USA.
3. Rest Devices, Boston, MA, USA.
Int J Med Sci. 2014 May 30;11(8):796-802. doi: 10.7150/ijms.9303. eCollection 2014.
Obstructive sleep apnea (OSA) is a prevalent and treatable disorder of neurological and medical importance that is traditionally diagnosed through multi-channel laboratory polysomnography(PSG). However, OSA testing is increasingly performed with portable home devices using limited physiological channels. We tested the hypothesis that single channel respiratory effort alone could support automated quantification of apnea and hypopnea events. We developed a respiratory event detection algorithm applied to thoracic strain-belt data from patients with variable degrees of sleep apnea. We optimized parameters on a training set (n=57) and then tested performance on a validation set (n=59). The optimized algorithm correlated significantly with manual scoring in the validation set (R2=0.73 for training set, R2=0.55 for validation set; p<0.05). For dichotomous classification, the AUC was >0.92 and >0.85 using apnea-hypopnea index cutoff values of 5 and 15, respectively. Our findings demonstrate that manually scored AHI values can be approximated from thoracic movements alone. This finding has potential applications for automating laboratory PSG analysis as well as improving the performance of limited channel home monitors.
阻塞性睡眠呼吸暂停（OSA）是一种普遍且可治疗的具有神经学和医学重要性的疾病，传统上通过多通道实验室多导睡眠图（PSG）进行诊断。然而，OSA检测越来越多地使用生理通道有限的便携式家用设备进行。我们检验了仅通过单通道呼吸努力就能支持对呼吸暂停和低通气事件进行自动量化的假设。我们开发了一种呼吸事件检测算法，应用于来自不同程度睡眠呼吸暂停患者的胸段应变带数据。我们在一个训练集（n = 57）上优化参数，然后在一个验证集（n = 59）上测试性能。优化后的算法在验证集中与人工评分显著相关（训练集R2 = 0.73，验证集R2 = 0.55；p < 0.05）。对于二分分类，使用呼吸暂停低通气指数临界值5和15时，曲线下面积（AUC）分别>0.92和>0.85。我们的研究结果表明，仅通过胸段运动就可以近似得出人工评分的呼吸暂停低通气指数（AHI）值。这一发现对于自动化实验室PSG分析以及提高有限通道家用监测仪的性能具有潜在应用价值。
