Towards an Intelligent Monitoring System for Patients with Obstrusive Sleep Apnea

Due to the growing incidence of chronic diseases and aging populations, the pressure to control costs and the expectations of continuous improvements in the quality of service have increased the need to understand how healthcare is provided and to determine whether cost-effective improvements to care practices can be made. In the case of people suffering Obstructive Sleep Apnea, patients using self-administer nasal Continuous Positive Airway Pressure (CPAP) may receive information on the treatment only once they go to a visit with the lung specialist. In this paper, we propose an IoT-based Intelligent Monitoring System that relies on machine learning to achieve a threefold goal: (1) it is aimed at early detecting compliance in order to predict CPAP usage; (2) it monitors the actual adherence degree to the treatment to keep informed both the patient and the lung specialists; and (3) it sends recommendations to the patient to empower her/him and to better follow up.