Xu Huajun, Song Wei, Yi Hongliang, Hou Limin, Zhang Changheng, Chen Bin, Chen Yuqin, Yin Shankai
Department of Otolaryngology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, 600 Yishan Road, Shanghai, 200233, China.
Sleep Breath. 2015 May;19(2):599-605. doi: 10.1007/s11325-014-1055-0. Epub 2014 Sep 9.
Loud snoring is one of the principle symptoms of obstructive sleep apnea (OSA). Snoring sound analysis is a potentially cost-effective, reliable alternative for the diagnosis of OSA. However, no investigation has determined the accuracy of snoring signal analysis for the diagnosis of OSA in the Chinese Han population. Therefore, we investigated whether whole-night snoring detection and analysis aids the diagnosis of OSA using a new snore analysis technique.
Snoring sounds were recorded using a non-contact microphone and polysomnography (PSG) was performed simultaneously throughout the night. We randomly selected 30 subjects each from four groups based on the severity of OSA. The rhythm and frequency domain of the snoring signal were analyzed based on frequency energy endpoint detection (FEP) and the Earth mover's distance (EMD), for each subject to harvest the EMD-calculated Apnea-Hypopnea Index (AHIEMD). Finally, we compared the AHIEMD with the PSG-monitored AHI (AHIPSG).
The accuracy of the AHIEMD compared with the AHIPSG was 96.7, 86.7, 86.7, and 96.7% in non-, mild, moderate, and severe OSA patients, respectively. AHIEMD was correlated with AHIPSG (r(2) = 0.950, p < 0.001). The area under the receiver operating characteristic curve values for OSA detection was 0.974, 0.957, and 0.997 for AHIEMD thresholds of 5, 15, and 30 events/h, respectively. Bland-Altman analysis revealed 91.7% agreement of AHIEMD with AHIPSG.
This new method for identifying OSA by analyzing snoring is feasible and reliable in the Han population. The snoring sound-based technique appears to be a promising tool for OSA screening and diagnosis.
大声打鼾是阻塞性睡眠呼吸暂停（OSA）的主要症状之一。鼾声分析是一种潜在的具有成本效益且可靠的OSA诊断替代方法。然而，尚无研究确定鼾声信号分析在中国汉族人群中诊断OSA的准确性。因此，我们使用一种新的鼾声分析技术来研究全夜鼾声检测和分析是否有助于OSA的诊断。
使用非接触式麦克风记录鼾声，并在整个夜间同时进行多导睡眠图（PSG）监测。我们根据OSA的严重程度从四组中每组随机选择30名受试者。基于频率能量端点检测（FEP）和推土机距离（EMD）分析鼾声信号的节律和频域，为每个受试者获取经EMD计算的呼吸暂停低通气指数（AHIEMD）。最后，我们将AHIEMD与PSG监测的AHI（AHIPSG）进行比较。
在无、轻度、中度和重度OSA患者中，与AHIPSG相比，AHIEMD的准确率分别为96.7%、86.7%、86.7%和96.7%。AHIEMD与AHIPSG相关（r(2) = 0.950，p < 0.001）。对于AHIEMD阈值为5、15和30次/小时，OSA检测的受试者工作特征曲线下面积值分别为0.974、0.957和0.997。Bland-Altman分析显示AHIEMD与AHIPSG的一致性为91.7%。
这种通过分析鼾声来识别OSA的新方法在汉族人群中是可行且可靠的。基于鼾声的技术似乎是一种有前途的OSA筛查和诊断工具。
