Indexing and classifying snore characteristics using Support Vector Machine and integrated signal processing algorithm

Jessie R. Balbin; Ernesto Vergara Jr.; Ross Junior S. Calma; Nicole Marie Antonette A. Cuevas; James Erwin V. Paningbatan; Michael Angelo B. Ventura

doi:10.1117/12.2502004

26 July 2018 Indexing and classifying snore characteristics using Support Vector Machine and integrated signal processing algorithm

Jessie R. Balbin, Ernesto Vergara Jr., Ross Junior S. Calma, Nicole Marie Antonette A. Cuevas, James Erwin V. Paningbatan, Michael Angelo B. Ventura

Author Affiliations +

Proceedings Volume 10828, Third International Workshop on Pattern Recognition; 108281C (2018) https://doi.org/10.1117/12.2502004
Event: Third International Workshop on Pattern Recognition, 2018, Jinan, China

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.

Citation Download Citation

Jessie R. Balbin, Ernesto Vergara Jr., Ross Junior S. Calma, Nicole Marie Antonette A. Cuevas, James Erwin V. Paningbatan, and Michael Angelo B. Ventura "Indexing and classifying snore characteristics using Support Vector Machine and integrated signal processing algorithm", Proc. SPIE 10828, Third International Workshop on Pattern Recognition, 108281C (26 July 2018); https://doi.org/10.1117/12.2502004

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

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.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available