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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:10.55041/IJSREM34095

OBSTRUCTIVE SLEEP APNEA CLASSIFICATION USING SNORE SOUND DATASET

Stream/Area:

-

Subject:

-

Paper ID:

10.55041/IJSREM34095

Volume:

8 |

5 |

May 2024

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM34095

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

EJR

E. Jeslin Renjith

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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.

KEYWORDS

Obstructive Sleep Apnea (OSA) Particle Swarm Optimization (PSO) Quaternions Firefly Algorithm (QFA).

Cite This Article

E. Jeslin Renjith, "OBSTRUCTIVE SLEEP APNEA CLASSIFICATION USING SNORE SOUND DATASET," International Journal of Scientific Research in Engineering and Management, vol. 8, no. 5, May 2024. https://doi.org/10.55041/IJSREM34095

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12/31/45430

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