Detecting nonlinearity in prediction residuals of snoring sounds

Author

Mikami, Tsuyoshi

Author_Institution

Tomakomai Coll. of Technol., Tomakomai, Japan

fYear

2009

fDate

18-21 Aug. 2009

Firstpage

5256

Lastpage

5259

Abstract

This paper focuses on the nonlinear properties of snoring sounds for the purpose of obstructive sleep apnea diagnosis. Snoring sounds are convolutional sounds caused by wheezing of airway obstruction and oscillation of soft palate. Namely, it should be natural that the snoring sounds are generated from a nonlinear dynamics, but the nonlinear properties of them have not yet been studied so far. In this paper, the nonlinearity is defined as the predictability using a linear AR prediction model, and the prediction residuals are analyzed by portmanteau test.

Keywords

acoustic signal detection; autoregressive processes; bioacoustics; medical disorders; medical signal detection; medical signal processing; patient diagnosis; pneumodynamics; sleep; statistical testing; OSA syndrome; acoustic signal acquisition; airway obstruction wheezing; convolutional sound; linear AR prediction model; nonlinear dynamics detection; obstructive sleep apnea syndrome diagnosis; portmanteau test; snoring sound prediction residual; soft palate oscillation; Acoustic noise; Acoustic testing; Convolution; Convolutional codes; Educational institutions; Frequency domain analysis; Predictive models; Sleep apnea; Tongue; White noise; Linear Prediction Model; Nonlinearity; Portmanteau Test; Snoring Sounds;

fLanguage

English

Publisher

ieee

Conference_Titel

ICCAS-SICE, 2009

Conference_Location

Fukuoka

Print_ISBN

978-4-907764-34-0

Electronic_ISBN

978-4-907764-33-3

Type

conf

Filename

5333345