A snoring classifier based on Heart Rate Variability analysis

Author

Ieong, Chio-In ; Dong, Cheng ; Nan, Wenya ; Rosa, Agostinho ; Guimarães, Ronaldo ; Vai, Mang-I ; Mak, Pui-In ; Wan, Feng ; Mak, Peng-Un

Author_Institution

Dept. of Electr. & Comput. Eng., Univ. of Macau, Macao, China

fYear

2011

fDate

18-21 Sept. 2011

Firstpage

345

Lastpage

348

Abstract

The effect of snoring on the cardiovascular system is not well-known. In this study we analyzed the Heart Rate Variability (HRV) differences between light and heavy snorers. The experiments are done on the full-whole-night polysomnography (PSG) with ECG and audio channels from patient group (heavy snorer) and control group (light snorer), which are gender- and age-paired, totally 30 subjects. A feature Snoring Density (SND) of audio signal as classification criterion and HRV features are computed. Mann-Whitney statistical test and Support Vector Machine (SVM) classification are done to see the correlation. The result of this study shows that snoring has close impact on the HRV features. This result can provide a deeper insight into the physiological understand of snoring.

Keywords

audio signal processing; cardiovascular system; medical signal processing; signal classification; support vector machines; Mann-Whitney statistical test; audio channel; audio signal; cardiovascular system; classification criterion; feature snoring density; heart rate variability analysis; snoring classifier; support vector machine classification; Correlation; Hafnium; Heart rate variability; Silicon; Sleep apnea; Support vector machines; Time domain analysis;

fLanguage

English

Publisher

ieee

Conference_Titel

Computing in Cardiology, 2011

Conference_Location

Hangzhou

ISSN

0276-6547

Print_ISBN

978-1-4577-0612-7

Type

conf

Filename

6164573