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.
The COVID-19 outbreak in 2019 is still a pandemic due to its strong contagiousness and viability. In order to prevent the spread of COVID-19, a non-contact monitoring system is needed. COVID-19 patients show symptoms similar to acute viral pneumonia. Because of this, COVID-19 patients are characterized by faster and shallower breathing than normal people. These respiratory status changes affect tissue oxygenation status. In this paper, we develop a system for monitoring changes in tissue oxygenation status in real time using NIRS sensors and classifies breathing patterns using deep learning algorithm.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.