Arsenali Bruno, van Dijk Johannes, Ouweltjes Okke, den Brinker Bert, Pevernagie Dirk, Krijn Roy, van Gilst Merel, Overeem Sebastiaan
Annu Int Conf IEEE Eng Med Biol Soc. 2018 Jul;2018:328-331. doi: 10.1109/EMBC.2018.8512251.
Obstructive sleep apnea (OSA) is a disorder that affects up to 38% of the western population. It is characterized by repetitive episodes of partial or complete collapse of the upper airway during sleep. These episodes are almost always accompanied by loud snoring. Questionnaires such as STOP-BANG exploit snoring to screen for OSA. However, they are not quantitative and thus do not exploit its full potential. A method for automatic detection of snoring in whole-night recordings is required to enable its quantitative evaluation. In this study, we propose such a method. The centerpiece of the proposed method is a recurrent neural network for modeling of sequential data with variable length. Mel-frequency cepstral coefficients, which were extracted from snoring and non-snoring sound events, were used as inputs to the proposed network. A total of 20 subjects referred to clinical sleep recording were also recorded by a microphone that was placed 70 cm from the top end of the bed. These recordings were used to assess the performance of the proposed method. When it comes to the detection of snoring events, our results show that the proposed method has an accuracy of 95%, sensitivity of 92%, and specificity of 98%. In conclusion, our results suggest that the proposed method may improve the process of snoring detection and with that the process of OSA screening. Follow-up clinical studies are required to confirm this potential.
阻塞性睡眠呼吸暂停（OSA）是一种影响高达38%西方人口的疾病。其特征是睡眠期间上呼吸道反复出现部分或完全塌陷。这些发作几乎总是伴有大声打鼾。诸如STOP-BANG之类的问卷利用打鼾来筛查OSA。然而，它们不是定量的，因此没有充分发挥其潜力。需要一种在整夜记录中自动检测打鼾的方法，以实现对其进行定量评估。在本研究中，我们提出了这样一种方法。所提出方法的核心是一个用于对可变长度序列数据进行建模的递归神经网络。从打鼾和非打鼾声音事件中提取的梅尔频率倒谱系数被用作该网络的输入。另外还使用一个距离床头顶端70厘米处的麦克风记录了20名转诊进行临床睡眠记录患者的情况。这些记录用于评估所提出方法的性能。在打鼾事件检测方面，我们的结果表明，所提出的方法准确率为95%，灵敏度为92%，特异性为98%。总之，我们的结果表明，所提出的方法可能会改善打鼾检测过程，进而改善OSA筛查过程。需要后续的临床研究来证实这一潜力。
