Normal probability testing of snore signals for diagnosis of obstructive sleep apnea

Obstructive sleep apnea (OSA) is a highly prevalent disease in which upper airways are collapsed during sleep, leading to serious consequences. The standard method of OSA diagnosis is known as polysomnography (PSG), which requires an overnight stay in a specifically equipped facility, connected to o...

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Vydané v:Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Ročník 2009; s. 5551 - 5554
Hlavní autori: Ghaemmaghami, H., Abeyratne, U.R., Hukins, C.
Médium: Konferenčný príspevok.. Journal Article
Jazyk:English
Vydavateľské údaje: United States IEEE 01.01.2009
Predmet:
Algorithms
Auscultation - methods
Australia
Data Interpretation, Statistical
Diagnosis, Computer-Assisted - methods
Diseases
Female
Humans
Information technology
Instruments
Linear predictive coding
Male
Reproducibility of Results
Sensitivity and Specificity
Sleep apnea
Sleep Apnea, Obstructive - complications
Sleep Apnea, Obstructive - diagnosis
Snoring - complications
Snoring - diagnosis
Sound Spectrography - methods
Testing
USA Councils
ISSN:1094-687X, 1557-170X, 2375-7477
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Shrnutí:Obstructive sleep apnea (OSA) is a highly prevalent disease in which upper airways are collapsed during sleep, leading to serious consequences. The standard method of OSA diagnosis is known as polysomnography (PSG), which requires an overnight stay in a specifically equipped facility, connected to over 15 channels of measurements. PSG requires (i) contact instrumentation and, (ii) the expert human scoring of a vast amount of data based on subjective criteria. PSG is expensive, time consuming and is difficult to use in community screening or pediatric assessment. Snoring is the most common symptom of OSA. Despite the vast potential, however, it is not currently used in the clinical diagnosis of OSA. In this paper, we propose a novel method of snore signal analysis for the diagnosis of OSA. The method is based on a novel feature that quantifies the non-Gaussianity of individual episodes of snoring. The proposed method was evaluated using overnight clinical snore sound recordings of 86 subjects. The recordings were made concurrently with routine PSG, which was used to establish the ground truth via standard clinical diagnostic procedures. The results indicated that the developed method has a detectability accuracy of 97.34%.
Bibliografia:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Undefined-1
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content type line 23
ISSN:1094-687X
1557-170X
2375-7477
DOI:10.1109/IEMBS.2009.5333733