Sleep apnea-hypopnea syndrome (SAHS), as a widespread respiratory sleep disorder, if left untreated, can lead to a series of pathological changes. By using Polysomnography (PSG), traditional SAHS diagnosis tends to be complex and costly. Nasal airflow (NA) is the most direct reflection of the severity of SAHS. Therefore, we try to take advantage of NA signals that can be easily recorded by wearable devices. In this paper, we present an automatic detection approach of SAH events based on single-channel signal. Through this approach, an enhanced frequency extraction network is designed, which factorizes the mixed feature maps by their frequencies. And the spatial resolution of low-frequency components is reduced so as to save spending. Besides, in our research, the vanilla convolution block of the high-frequency components are replaced by residual blocks and smaller groups of filters with bigger size kernels. And we use the spatial attention module to facilitate feature extraction. Compared with state-of-the-art networks in this field, the promising results reveal that the proposed network for SAH events multiclass classification shows outstanding performance with accuracy of 91.23%, sensitivity of 90.81% and specificity of 90.59%. Thus, we believe that our approach, as a low-cost and high-efficiency solution, shows a great potential for detecting SAH events.

睡眠呼吸暂停低通气综合征（SAHS）作为一种广泛的呼吸睡眠障碍，如果不加以治疗，会导致一系列病理变化。通过使用多导睡眠图（PSG），传统的SAHS诊断趋于复杂且成本高昂。鼻气流（NA）是SAHS严重程度的最直接反映。因此，我们尝试利用可穿戴设备可以轻松记录的NA信号。在本文中，我们提出了一种基于单通道信号的SAH事件自动检测方法。通过这种方法，设计了一种增强的频率提取网络，该网络将混合特征图的频率分解。并且降低了低频分量的空间分辨率，从而节省了开支。此外，在我们的研究中 高频分量的原始卷积块被残差块和具有较大内核的较小滤波器组取代。并且我们使用空间注意模块来促进特征提取。与该领域的最新网络相比，有希望的结果表明，拟议的SAH事件多分类网络具有出色的性能，准确度为91.23％，灵敏度为90.81％，特异性为90.59％。因此，我们认为，作为一种低成本，高效率的解决方案，我们的方法显示出检测SAH事件的巨大潜力。令人鼓舞的结果表明，拟议的SAH事件多分类网络具有出色的性能，准确度为91.23％，灵敏度为90.81％，特异性为90.59％。因此，我们认为，作为一种低成本，高效率的解决方案，我们的方法显示出检测SAH事件的巨大潜力。令人鼓舞的结果表明，拟议的SAH事件多分类网络具有出色的性能，准确度为91.23％，灵敏度为90.81％，特异性为90.59％。因此，我们认为，作为一种低成本，高效率的解决方案，我们的方法显示出检测SAH事件的巨大潜力。