Continuous Mobile Audio Monitoring for Sleep Apnea Detection.

Audio-based sleep apnea detection methods hold great potential to improve access to diagnosis, by providing unattended sleep apnea screening at home via sound collected from mobile sensors during sleep. Our research involved a thorough comparison and evaluation of tracheal and ambient microphone recordings for sleep apnea detection with different granularities. Utilising a variety of acoustic representations and sophisticated deep learning architectures, we performed an extensive analysis on the open PSG-Audio dataset, which encompasses over 850 hours of audio data from 194 subjects. For sleep apnea classification, the most effective model showed a 90.8 % accuracy in detecting sleep apnea, 83.3 % accuracy when hypopneic and apneic events were detected separately, and 75.7 % accuracy when apneic events were further divided into three sub-categories. On overnight recordings, the model achieved a sensitivity of 0.93 and a specificity of 1.0 for moderate sleep apnea screening, and a sensitivity of 0.84 and a specificity of 0.97 for severe sleep apnea screening. This research also provided a unique study to compare and combine respiratory sounds from two different types of sensors for sleep apnea detection. The high performance of our model provides a promising avenue for enabling remote diagnosis and monitoring of sleep apnea.