Characterization of REM/NREM sleep using breath sounds in OSA

Akhter, Shahin, Abeyratne, Udantha R. and Swarnkar, Vinayak (2016) Characterization of REM/NREM sleep using breath sounds in OSA. Biomedical Signal Processing and Control, 25 130-142. doi:10.1016/j.bspc.2015.11.007

Author Akhter, Shahin
Abeyratne, Udantha R.
Swarnkar, Vinayak
Title Characterization of REM/NREM sleep using breath sounds in OSA
Journal name Biomedical Signal Processing and Control   Check publisher's open access policy
ISSN 1746-8094
Publication date 2016-03-01
Year available 2015
Sub-type Article (original research)
DOI 10.1016/j.bspc.2015.11.007
Open Access Status Not Open Access
Volume 25
Start page 130
End page 142
Total pages 13
Place of publication Amsterdam, NX, Netherlands
Publisher Elsevier
Language eng
Subject 2718 Health Informatics
1711 Signal Processing
Abstract Obstructive Sleep Apnea (OSA) is a serious sleep disorder where patient experiences frequent upper airway collapse leading to breathing obstructions and arousals. Severity of OSA is assessed by averaging the number of incidences throughout the sleep. In a routine OSA diagnosis test, overnight sleep is broadly categorized into rapid eye movement (REM) and non-REM (NREM) stages and the number of events are considered accordingly to calculate the severity. A typical respiratory event is mostly accompanied by sounds such as loud breathing or snoring interrupted by choking, gasps for air. However, respiratory controls and ventilations are known to differ with sleep states. In this study, we assumed that the effect of sleep on respiration will alter characteristics of respiratory sounds as well as snoring in OSA patients. Our objective is to investigate whether the characteristics are sufficient to label snores of REM and NREM sleep. For investigation, we collected overnight audio recording from 12 patients undergoing routine OSA diagnostic test. We derived features from snoring sounds and its surrounding audio signal. We computed time series statistics such as mean, variance, inter-quartile-range to capture distinctive pattern from REM and NREM snores. We designed a Naïve Bayes classifier to explore the usability of patterns to predict corresponding sleep states. Our method achieved a sensitivity of 92% (±9%) and specificity of 81% (±9%) in labeling snores into REM/NREM group which indicates the potential of snoring sounds to differentiate sleep states. This may be valuable to develop non-contact snore based technology for OSA diagnosis.
Keyword Non-rapid eye movement
Obstructive sleep apnea
Rapid eye movement
Sleep disorder
Q-Index Code C1
Q-Index Status Provisional Code
Grant ID DP120100141
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2016 Collection
School of Information Technology and Electrical Engineering Publications
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Citation counts: TR Web of Science Citation Count  Cited 3 times in Thomson Reuters Web of Science Article | Citations
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