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dc.contributor.supervisorMoussavi, Zahra (Electrical and Computer Engineering)en_US
dc.contributor.authorMontazeripouragha, Amanallah
dc.date.accessioned2012-03-16T17:18:17Z
dc.date.available2012-03-16T17:18:17Z
dc.date.issued2012-03-16
dc.identifier.urihttp://hdl.handle.net/1993/5194
dc.description.abstractObstructive Sleep Apnea (OSA) is a common respiratory disorder during sleep. Apnea is cessation of airflow to the lungs, which lasts for at least 10 seconds accompanied by more than 4% drop of the blood's Oxygen saturation. Polysomnography during the entire night is the Gold Standard diagnostic method of OSA. It's high cost and inconvenience for patients persuaded researchers to seek alternative OSA detection methods. This thesis proposes a technique for assessment of OSA during wakefulness. We recorded tracheal breath sounds of 17 non-apneic individuals and 35 people with various degrees of OSA severity in supine and upright positions during nose and mouth breathing at medium flow rate. We calculated the power spectrum, Kurtosis, and Katz fractal dimensions of the recorded signals. Then, we reduced the number of characteristic features to two. We classified the participant into severe OSA and non-OSA groups as well as non-OSA or mild vs. moderate and severe OSA groups. The results showed more than 91 and 83% accuracy; for the two types of classification. Once veri ed on a larger population, the proposed method may be used as a simple and non-invasive screening tool for assessment of OSA during wakefulness.en_US
dc.language.isoengen_US
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectBiomedicalen_US
dc.subjectSleep Apneaen_US
dc.subjectClassificationen_US
dc.titleAcoustical analysis of respiratory sounds for detection of obstructive sleep apneaen_US
dc.typeinfo:eu-repo/semantics/masterThesis
dc.typemaster thesisen_US
dc.degree.disciplineElectrical and Computer Engineeringen_US
dc.contributor.examiningcommitteeSherif, Sherif (Electrical and Computer Engineering) Paliwal, Jitendra (Biosystems Engineering)en_US
dc.degree.levelMaster of Science (M.Sc.)en_US
dc.description.noteMay 2012en_US
local.subject.manitobayesen_US


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