Artificial neural networks for breathing and snoring episode detection in sleep sounds

Emoto, Takahiro, Abeyratne, Udantha R., Chen, Yongjian, Kawata, Ikuji, Akutagawa, Masatake and Kinouchi, Yohsuke (2012) Artificial neural networks for breathing and snoring episode detection in sleep sounds. Physiological Measurement, 33 10: 1675-1689.


Author Emoto, Takahiro
Abeyratne, Udantha R.
Chen, Yongjian
Kawata, Ikuji
Akutagawa, Masatake
Kinouchi, Yohsuke
Title Artificial neural networks for breathing and snoring episode detection in sleep sounds
Journal name Physiological Measurement   Check publisher's open access policy
ISSN 0967-3334
1361-6579
Publication date 2012
Sub-type Article (original research)
DOI 10.1088/0967-3334/33/10/1675
Volume 33
Issue 10
Start page 1675
End page 1689
Total pages 15
Place of publication Bristol, United Kingdom
Publisher Institute of Physics
Collection year 2013
Language eng
Abstract Obstructive sleep apnea (OSA) is a serious disorder characterized by intermittent events of upper airway collapse during sleep. Snoring is the most common nocturnal symptom of OSA. Almost all OSA patients snore, but not all snorers have the disease. Recently, researchers have attempted to develop automated snore analysis technology for the purpose of OSA diagnosis. These technologies commonly require, as the first step, the automated identification of snore/breathing episodes (SBE) in sleep sound recordings. Snore intensity may occupy a wide dynamic range (>95dB) spanning from the barely audible to loud sounds. Low-intensity SBE sounds are sometimes seen buried within the background noise floor, even in high-fidelity sound recordings made within a sleep laboratory. The complexity of SBE sounds makes it a challenging task to develop automated snore segmentation algorithms, especially in the presence of background noise. In this paper, we propose a fundamentally novel approach based on artificial neural network (ANN) technology to detect SBEs. Working on clinical data, we show that the proposed method can detect SBE at a sensitivity and specificity exceeding 0.892 and 0.874 respectively, even when the signal is completely buried in background noise (SNR <0dB). We compare the performance of the proposed technology with those of the existing methods (short-term energy, zero-crossing rates) and illustrate that the proposed method vastly outperforms conventional techniques.
Keyword Neural networks
Obstructive sleep apnea
Snoring/breathing episodes
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2013 Collection
School of Information Technology and Electrical Engineering Publications
 
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Created: Wed, 14 Nov 2012, 12:26:13 EST by Dr Udantha Abeyratne on behalf of School of Information Technol and Elec Engineering