Convolutional neural network for screening of obstructive sleep apnea using snoring sounds

Screening and assessment for obstructive sleep apnea hypopnea syndrome (OSAHS) has attracted growing attention to improve the life of patients with sleep apnea. The gold standard for diagnosing OSAHS is an overnight polysomnography (PSG) in a dedicated sleep laboratory. Yet, the PSG test is professional, expensive and unsuitable for mass screening of the population. OSAH affects about 10 % of population in the world, and there are about 80 % suffers remaining undiagnosed. The automatic and cheap OSAHS patients screening methods are urgently needed. As a major sign of undiagnosed OSAHS, snoring has been used for diagnosis of OSAHS. An automated snore detection method would allow a faster diagnosis and more patients to be analyzed. In this paper, we build a database including more than 80 thousand of snoring sound episodes from 124 subjects. These sounds are recorded by non-contact microphone in the subject's private room, and labeled by trained sleep clinicians. And then the visibility graph method is modified as a novel framework for encoding these snoring time series into images to fully take advantage of two-dimension convolutional neural networks (CNN) in computer vision tasks. At last, a CNN model based on visibility graph method (VG) is applied to automatically extract features and recognize severity of OSAHS based on these snoring sounds. The experimental results show that our approach achieves an accuracy of 92.5 %, sensitivity of 93.9 %, and specificity of 91.2 % for OSAHS recognition. Our research provides an alternative method for rapid and massive screening and diagnosis of OSAHS.