Document Details

Document Type : Thesis 
Document Title :
APNEA DETECTION DURING SLEEP BASED ON VIDEO ANALYSIS
الكشف عن توقف التنفس أثناء النوم باعتماد تحليل الفيديو
 
Subject : Faculty of Economics and Administration 
Document Language : Arabic 
Abstract : Individuals who suffer from different sleep breathing disorders are subject to a wide range of serious health problems. Unfortunately, the rate of diagnosis is very low and the existing breathing monitoring techniques are expensive, uncomfortable and time and labor intensive. The gold standard Polysomnography (PSG) is invasive, costly, technically complex and time-consuming. Towards developing a non-contact sleep breathing monitoring system, this study presents two main contributions. First, a new waveform is proposed that facilitates the identification of each type of breathing event: normal breathing, obstructive sleep apnea, central sleep apnea, and hypopnoea. Second, a statistical approach is proposed for detecting obstructive sleep apnea events as it allows more objectivity and saves more time. The major advantage of the proposed approach is that it does not require any manual adjustments and does not depend on the patient sleeping pose. Moreover, the proposed waveform illustrates that each type of breathing events has a specific pattern and hence can be easily distinguished. In addition to the possibility of recognizing a body movement. This facilitates identifying only suspicious periods during which physiological signals will be scored, instead of analyzing the whole signals of $8$ hours of sleep. Moreover, in the automatic analysis of the motion patterns, the motion magnitudes during apnea events are considered as having atypical values relative to those obtained during normal breathing and are detected automatically thanks to the use of a statistical test for outliers detection. The experimental evaluation on real infrared videos recordings for both detection techniques show high performances. 
Supervisor : Dr. Heyfa Esmaeel Ammar 
Thesis Type : Master Thesis 
Publishing Year : 1440 AH
2019 AD 
Added Date : Tuesday, February 26, 2019 

Researchers

Researcher Name (Arabic)Researcher Name (English)Researcher TypeDr GradeEmail
سماهر عبدالمجيد لشكرLashkar, Samaher AbdulmajeedResearcherMaster 

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