Processing of snore related sounds for the diagnosis of obstructive sleep apnoea (OSA)

Wakwella, Ajith S. (Ajith Susantha) (2005). Processing of snore related sounds for the diagnosis of obstructive sleep apnoea (OSA) PhD Thesis, School of Information Technol and Elec Engineering, The University of Queensland.

Attached Files (Some files may be inaccessible until you login with your UQ eSpace credentials)
Name Description MIMEType Size Downloads
THE21035.pdf Full text application/pdf 4.50MB 6
Author Wakwella, Ajith S. (Ajith Susantha)
Thesis Title Processing of snore related sounds for the diagnosis of obstructive sleep apnoea (OSA)
School, Centre or Institute School of Information Technol and Elec Engineering
Institution The University of Queensland
Publication date 2005
Thesis type PhD Thesis
Supervisor Udantha R. Abeyratne
Total pages 138
Language eng
Subjects 1109 Neurosciences
Formatted abstract

Obstructive Sleep Apnoea (OSA) is a sleep related disorder, which causes repetitive collapse of upper airways during sleep leading to serious consequences. Aging populations and rising number of obese people are making OSA an increasingly important public health problem. At present, over 90% of the patients remain undiagnosed. Gold standard of OSA diagnosis, Polysomnography (PSG), requires the patient to spend full night at the sleep clinic attached to a multitude of measurement leads. Therefore PSG is inconvenient and expensive, making it unsuited for community screening.


Snoring, which is a common symptom of OSA, carries vital information about the status of the upper airways, though it is rarely being used for diagnosis. Several advantages of using snoring sound are non-invasive analysis, cost effectiveness, possibility of automated scoring and unattended study. This can be developed as a single measurement, which is potentially superior to PSG and other portable monitoring devices for screening OSA. In this research project we develop a clinical database of full night snoring sounds. We propose algorithms to segment and categorise recorded snoring signal into silence and voiced/unvoiced snoring sounds. These algorithms allow us to extract all the snores from the full night recording of sounds around 99% accuracy. Then we analyse the extracted snores from the subjects to track the development of apnoea events starting from the time a subject goes to sleep, using pitch-jitter information for mild OSA patients.


We introduce a novel feature, called intra snore pitch jumps (ISPJ), in a view to diagnose OSA. We tested the feature using a clinical database of full night snoring from 29 subjects. Sensitivity values of 83-1OO% (at specificity of 50-80%) were obtained at several severity levels of OSA. Achieved sensitivity levels indicate that feature ISPJ can be used for screening OSA at home setting using a simple monitor.

Keyword Sleep apnea syndromes -- Diagnosis
Sleep disorders -- Diagnosis
Additional Notes
Variant title:
Processing of snorig [sic] for the diagnosis of OSA

Document type: Thesis
Collection: UQ Theses (RHD) - UQ staff and students only
Citation counts: Google Scholar Search Google Scholar
Created: Wed, 12 Jun 2013, 16:52:28 EST by Mr Lachlan Wong on behalf of Scholarly Communication and Digitisation Service