Abstract
Obstructive Sleep Apnea (OSA) is a potentially fatal chronic condition that increases the risk of cardiovascular illnesses. One of the most common symptoms of OSA is snoring. This paper aims to present and test a highly efficient Classification method as well as a sensitive whole-night snore sound detector that relies on non-contact knowledge. The goal of this ensemble approach is to combine several classifiers, such as the Quaternions Firefly Algorithm and Particle Swarm Optimization (PSO), to create a new classification method. Progressively enhance the optimization-based classifiers' accuracy. Index Terms - Obstructive Sleep Apnea (OSA), Particle Swarm Optimization (PSO), Quaternions Firefly Algorithm (QFA).
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
