Sleep apnea syndrome is a breathing disorder with a prevalence exceeding 20% in the overall population, and it can seriously affect health and well-being. However, this condition usually remains undetected because suitable monitoring solutions are lacking. This contribution presents an approach to facilitate apnea diagnosis using a battery-powered, wireless, miniaturized sensing system embedded in a patient’s mask. It combines a photoacoustic-based carbon dioxide detector with temperature and humidity sensors as well as embedded algorithms to automatically detect apnea episodes. The results show the feasibility of detecting apnea using an easily deployable analysis system.
