Snore signal enhancement and activity detection via translation-invariant wavelet transform

Ng, A. K., Koh, T. S., Puvanendran, K. and Abeyratne, U. R. (2008) Snore signal enhancement and activity detection via translation-invariant wavelet transform. IEEE Transactions on Biomedical Engineering, 55 10: 2332-2342. doi:10.1109/TBME.2008.925682

Author Ng, A. K.
Koh, T. S.
Puvanendran, K.
Abeyratne, U. R.
Title Snore signal enhancement and activity detection via translation-invariant wavelet transform
Journal name IEEE Transactions on Biomedical Engineering   Check publisher's open access policy
ISSN 0018-9294
Publication date 2008-10-01
Year available 2008
Sub-type Article (original research)
DOI 10.1109/TBME.2008.925682
Open Access Status Not yet assessed
Volume 55
Issue 10
Start page 2332
End page 2342
Total pages 11
Editor Principe, J. C.
Place of publication USA
Publisher IEEE
Language eng
Subject C1
671402 Medical instrumentation
671299 Computer hardware and electronic equipment not elsewhere classified
730305 Diagnostic methods
090303 Biomedical Instrumentation
Abstract Acoustical properties of snores have been widely studied as a potentially cost-effective and reliable alternative to diagnosing obstructive sleep apnea (OSA), with a common recognition that the diagnostic accuracy depends heavily on the snore signal quality and intelligibility. This paper proposes a novel preprocessing system that performs two critical tasks concurrently in a translation-invariant wavelet transform domain. These tasks include enhancement of snore signals via a level-correlation-dependent (LCD) threshold, and identification of snore presence through a snore activity (SA) detector. Various experiments were conducted to warrant the robustness of the system in terms of theoretical statistics quality, signal-to-noise ratio, mean opinion score, and clinical usefulness in detecting OSA. Results indicate that the proposed LCD threshold and SA detector are highly comparable to the existing denoising methodologies using level-dependent threshold and segmentation approaches using short-time energy and zero-crossing rate, yielding the best results in all the experiments. Given the strong initial performance of the proposed preprocessing system for snore signals, continued exploration in this direction could potentially lead to additional improvement in signal integrity, thereby increasing the diagnostic accuracy for OSA.
Keyword enhancement and detection
level-correlation-dependent (LCD) threshold
obstructive sleep apnea (OSA)
snore activity detector
snore signals
translation-invariant discrete wavelt transformation
wavelet thresholding
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

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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Citation counts: TR Web of Science Citation Count  Cited 17 times in Thomson Reuters Web of Science Article | Citations
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