Silence-breathing-snore classification from snore-related sounds

Karunajeewa, Asela S., Abeyratne, Udantha R. and Hukins, Craig (2008) Silence-breathing-snore classification from snore-related sounds. Physiological Measurement, 29 2: 227-243. doi:10.1088/0967-3334/29/2/006


Author Karunajeewa, Asela S.
Abeyratne, Udantha R.
Hukins, Craig
Title Silence-breathing-snore classification from snore-related sounds
Journal name Physiological Measurement   Check publisher's open access policy
ISSN 0967-3334
Publication date 2008-02-01
Sub-type Article (original research)
DOI 10.1088/0967-3334/29/2/006
Volume 29
Issue 2
Start page 227
End page 243
Total pages 17
Editor M. H. Neuman
Place of publication United Kingdom
Publisher Institute of Physics Publishing (IOP)
Language eng
Subject C1
730305 Diagnostic methods
671402 Medical instrumentation
671299 Computer hardware and electronic equipment not elsewhere classified
090399 Biomedical Engineering not elsewhere classified
Abstract Obstructive sleep apnea (OSA) is a highly prevalent disease in which upper airways are collapsed during sleep, leading to serious consequences. Snoring is the earliest symptom of OSA, but its potential in clinical diagnosis is not fully recognized yet. The first task in the automatic analysis of snore-related sounds (SRS) is to segment the SRS data as accurately as possible into three main classes: snoring (voiced non-silence), breathing (unvoiced non-silence) and silence. SRS data are generally contaminated with background noise. In this paper, we present classification performance of a new segmentation algorithm based on pattern recognition. We considered four features derived from SRS to classify samples of SRS into three classes. The features—number of zero crossings, energy of the signal, normalized autocorrelation coefficient at 1 ms delay and the first predictor coefficient of linear predictive coding (LPC) analysis—in combination were able to achieve a classification accuracy of 90.74% in classifying a set of test data. We also investigated the performance of the algorithm when three commonly used noise reduction (NR) techniques in speech processing—amplitude spectral subtraction (ASS), power spectral subtraction (PSS) and short time spectral amplitude (STSA) estimation—are used for noise reduction. We found that noise reduction together with a proper choice of features could improve the classification accuracy to 96.78%, making the automated analysis a possibility.
Keyword obstructive sleep apnea (OSA)
snore related sounds (SRS)
snore segmentation
Q-Index Code C1
Q-Index Status Confirmed Code

Document type: Journal Article
Sub-type: Article (original research)
Collections: 2009 Higher Education Research Data Collection
School of Information Technology and Electrical Engineering Publications
 
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