An SVM-based classification of oral and nasal snoring sounds with Kullback-Leibler kernel

Author

Mikami, Tsuyoshi ; Kojima, Yohichiro ; Yonezawa, Kazuya ; Yamamoto, Masahito ; Furukawa, Masashi

Author_Institution

Tomakomai Coll. of Technol., Tomakomai, Japan

fYear

2012

fDate

20-23 Aug. 2012

Firstpage

1795

Lastpage

1797

Abstract

Recently, numerous investigations have shown that loud habitual snoring is due to nasal obstruction and loud oral snoring is found in many sleep apnea patients. So, it is important to detect oral snoring in the earlier stage, but unfortunately we cannot know our own sleep condition. For such purpose, we adopt a Support Vector Machine (SVM) classifier with Kullback-Leibler (KL) kernel so as to classify oral and nasal snoring sounds based on the spectral properties, and compare it with the other kernel functions.

Keywords

acoustic signal detection; medical disorders; pattern classification; signal classification; sleep; spectral analysis; support vector machines; KL kernel; Kullback-Leibler kernel; SVM classifier; SVM-based classification; kernel functions; loud habitual snoring; loud oral snoring; nasal obstruction; nasal snoring sound classification; nasal snoring sounds; oral snoring detection; oral snoring sound classification; oral snoring sounds; sleep apnea patients; sleep condition; spectral property; support vector machine classifier; Accuracy; Acoustics; Educational institutions; Kernel; Mouth; Sleep apnea; Support vector machines; Kullback-Leibler divergence; pattern classification; snoring sounds; support vector machines;

fLanguage

English

Publisher

ieee

Conference_Titel

SICE Annual Conference (SICE), 2012 Proceedings of

Conference_Location

Akita

ISSN

pending

Print_ISBN

978-1-4673-2259-1

Type

conf

Filename

6318745