OBSTRUCTIVE SLEEP APNEA CLASSIFICATION USING SNORE SOUND DATASET

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OBSTRUCTIVE SLEEP APNEA CLASSIFICATION USING SNORE SOUND DATASET

E. Jeslin Renjith, Assistant Professor

BS Abdur Rahman Crescent Institute of Science and Technology, Chennai

Jeslin.renjith@gmail.com

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).

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