Classification of vibratory patterns of the upper airway during sleep

Author

Alshaer, H. ; Rudzicz, Frank ; Falk, Tiago H. ; Wen-Hou Tseng ; Bradley, T.D.

Author_Institution

Sleep Res. Lab., Univ. Health Network, Toronto, ON, Canada

fYear

2013

fDate

3-7 July 2013

Firstpage

2080

Lastpage

2083

Abstract

Upper airway (UA) narrowing and collapse during sleep results in obstructive sleep apnea (OSA). We hypothesize that vibratory patterns of snoring can distinguish simple snorers from those with OSA. Samples of breath sounds were collected from 7 snorers without OSA and 5 with OSA. Snoring pitch (F₀) contours were found using the robust algorithm for pitch tracking (RAPT). The OSA snoring contours showed fluctuating patterns as compared to the smoother patterns of simple snorers. This suggests that snoring reveals the underlying instabilities of UA tissue in OSA. Conditional random fields, a statistical sequence classifier, gave 75% accuracy in distinguishing the 2 groups.

Keywords

biological tissues; medical disorders; medical signal processing; neurophysiology; pattern classification; pneumodynamics; signal classification; sleep; statistical analysis; OSA snoring contours; UA tissue; breath sounds; conditional random fields; obstructive sleep apnea; pitch tracking; robust algorithm; snoring pitch contours; statistical sequence classifier; upper airway; vibratory pattern classification; Accuracy; Atmospheric modeling; Biomechanics; Biomedical measurement; Hidden Markov models; Sleep apnea;

fLanguage

English

Publisher

ieee

Conference_Titel

Engineering in Medicine and Biology Society (EMBC), 2013 35th Annual International Conference of the IEEE

Conference_Location

Osaka

ISSN

1557-170X

Type

conf

DOI

10.1109/EMBC.2013.6609942

Filename

6609942