Snore Signal Enhancement and Activity Detection via Translation-Invariant Wavelet Transform

• Published in: IEEE transactions on biomedical engineering 2008-10, Vol.55 (10), p.2332-2342
• Main Authors: Ng, Andrew Keong, San Koh, Tong, Puvanendran, Kathiravelu, Ranjith Abeyratne, Udantha
• Format: Article
• Language: English
• Subjects: Acoustic noise; Algorithms; Artificial Intelligence; Background noise; Detectors; Discrete wavelet transforms; Enhancement and detection; Humans; level-correlation-dependent (LCD) threshold; Medical Laboratory Science; Noise reduction; obstructive sleep apnea (OSA); Pattern Recognition, Automated - methods; Reference Values; Signal analysis; Signal Processing, Computer-Assisted; Signal to noise ratio; Sleep apnea; Sleep Apnea, Obstructive - diagnosis; Sleep Apnea, Obstructive - physiopathology; Sleep disorders; snore activity (SA) detector; snore signals; snoring; Snoring - diagnosis; Snoring - physiopathology; Sound; Sound Spectrography - methods; translation-invariant discrete wavelet transform (TIDWT); wavelet thresholding; Wavelet transforms; Working environment noise
• ISSN: 0018-9294; 1558-2531
• DOI: 10.1109/TBME.2008.925682

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.