Deep-learning-based breathing pattern classification method for real-time monitoring of patients with infectious respiratory disease

The global outbreak of novel Coronavirus Disease (COVID-19) in 2019 required a method for detecting and continuously monitoring patients with an infectious respiratory disease. Patients infected with acute respiratory disease show symptoms of shallow, rapid breathing and dyspnea due to hypoxia-hypercapnia. In this paper, we develop a system for monitoring of patients with infectious respiratory disease in real time using NIRS sensors and classifies breathing patterns using deep learning algorithm.