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Indexing and classifying snore characteristics using Support Vector Machine and integrated signal processing algorithm
Author(s): Jessie R. Balbin; Ernesto Vergara; Ross Junior S. Calma; Nicole Marie Antonette A. Cuevas; James Erwin V. Paningbatan; Michael Angelo B. Ventura
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Paper Abstract

Snoring is the loud or severe sound that buzzes when an individual sleep. Snoring can be produced through the nose, throat, uvula, or tongue. Each nature could be a sign that can be beneficial to specify what medical ailment or disorder a person could have. This paper focused on a sleeping disorder called Obstructive sleep apnea (OSA). Initiated from other investigation concerning about snoring detection and indexing, categories of snore have been segregated and classified from their elementary acoustic compositions such as the sound intensity and frequency. The study aims to come up with a device that records a snore sound that classifies the snore to what ailment the patient could be suffering using Support Vector Machine (SVM) and signal processing algorithm.

Paper Details

Date Published: 26 July 2018
PDF: 5 pages
Proc. SPIE 10828, Third International Workshop on Pattern Recognition, 108281C (26 July 2018); doi: 10.1117/12.2502004
Show Author Affiliations
Jessie R. Balbin, Mapúa Univ. (Philippines)
Ernesto Vergara, Mapúa Univ. (Philippines)
Ross Junior S. Calma, Mapúa Univ. (Philippines)
Nicole Marie Antonette A. Cuevas, Mapúa Univ. (Philippines)
James Erwin V. Paningbatan, Mapúa Univ. (Philippines)
Michael Angelo B. Ventura, Mapúa Univ. (Philippines)


Published in SPIE Proceedings Vol. 10828:
Third International Workshop on Pattern Recognition
Xudong Jiang; Zhenxiang Chen; Guojian Chen, Editor(s)

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