Abstract
Sleep apnea syndrome (SAS) is a well-known sleep disorder nowadays. People suffering SAS cease breathing during sleeping beacuse airway are partially or completely blocked caused by upper respiratory collapse repeatedly. SAS reduces quality of life by daytime sleepiness, memory declination, sexual dysfunction, and even myocardial infarction or cerebral vascular accident during sleeping. In general, people hardly notice SAS and need to do overnight sleep test, which is called polysomnography (PSG) in the sleep center of the hospital. PSG test is a limited environment, unconfortable and expensive examination. In order to simplify the complex diagnosis, this thesis uses two tri-axial accelerometers (TAA), which are low-cost and small sensors, stick on the left side of thorax and abdomen seperately to sense the thoracic (THO) and abdominal (ABD) movement signals. This thesis proposed a sleep apnea / hypopnea event detection algorithm to detect the sleep apnea/ hypopnea event by THO, ABD moving signals and oxygen saturation SpO2 signal during sleep. In the algorithm, using TAA selection method proposed in the thesis to transfer the three-dimensional data captured by TAA into one-dimentional signals seperately called TAA-ABD and TAA-THO and segments the two moving signals into 10-second window to extract features, which are cross-correlation, fundamental frequency and maximum amplitude values. The oxygen saturation signal is segmented into 15-second window and the six features, minimum, maximum, median, variance of the first derivative, difference from the median to the minimum, and the area under dip level of 3\% of oxygen saturation are extracted. Then, putting the ten features into Support Vector Machine (SVM) to construct classifiers and using the classifiers to design a state machine to calculate the number of sleep apnea/ hypopnea events.