NDLI: A novel method for obstructive sleep apnea severity estimation using speech signals

Content Provider	IEEE Xplore Digital Library
Author	Kriboy, M. Tarasiuk, A. Zigel, Y.
Copyright Year	2014
Description	Author affiliation: Dept. of Biomed. Eng., Ben-Gurion Univ. of the Negev, Beer-Sheva, Israel (Kriboy, M.; Zigel, Y.) \|\| Sleep-Wake Disorders Unit, Soroka Med. Center, Beer-Sheva, Israel (Tarasiuk, A.)
Abstract	Obstructive sleep apnea (OSA) is a prevalent sleep disorder associated with anatomical abnormalities of the upper airway. It is known that anatomic changes in the vocal tract affect the acoustic parameters of speech. We hypothesize that the speech signal contains valuable information that can be utilized for the assessment of OSA severity. We prospectively included 131 men with a variety of OSA severities; subjects were recorded immediately prior to polysomnography study while reading a one-minute speech protocol. Features from time and spectra domains were extracted, and a feature selection procedure was applied. Using a support vector regression (SVR), the proposed system estimates OSA severity, which is defined by the apnea-hypopnea index (AHI: the average number of apneic events per hour of sleep). Correlation of R=0.67, AHI error of 10.17 events/hr, and diagnostic agreement of 66.7% were achieved. This study provides the proof of concept that it is possible to estimate OSA severity by analyzing speech signals.
Sponsorship	IEEE Signal Process. Soc.
Starting Page	3606
Ending Page	3610
File Size	229823
Page Count	5
File Format	PDF
ISBN	9781479928934
DOI	10.1109/ICASSP.2014.6854273
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-05-04
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Speech Sleep apnea Feature extraction Estimation Speech processing Support vector machines Adaptation models SVR OSA speech signal processing
Content Type	Text
Resource Type	Article

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