| Summary: | 碩士 === 國立臺中科技大學 === 資訊管理系碩士班 === 104 === The pressures of modern working life have caused deterioration in the sleep quality of people. Among sleep disorders, sleep apnea affects our everyday life the most. It not only disturbs physiological regulatory functions, but also often leads to poor focus and lack of efficiency at work. It may even lead to drowsiness during the working day, resulting in irreversible tragedy caused by inattention.
This study focused mainly on the early detection of sleep apnea and assisting patients in early prevention, to avoid worsening of their condition. Polysomnogram (PSG) is commonly used as an assessment tool of sleep apnea patients. However, using PSG requires sleep study specialists to conduct the assessment and diagnosis. PSG is also time-consuming and the equipment is fairly expensive. Furthermore, due to limited hospital beds in sleep centers, patients usually need to wait a long time to be scheduled for a sleep study, and consequently cannot undergo the testing promptly. Therefore, this study developed a fast, objective, and cost-effective sleep apnea screening and prediction model to predict the possibility of the occurrence and severity of illness. This model also provides early assessment and treatment to the patients, thereby reducing the incidence and preventing the deterioration of the disease. The study used a variety of physiological characteristics in combination with machine learning to establish a screening model for the severity of sleep apnea, and to evaluate the effectiveness of different machine learning methods in detecting this disorder. Fuzzy C mean (FCM) algorithm was used to construct the prevention model for sleep apnea, which would assist doctors in achieving the goal of early identification of symptoms. In addition, during the experiment, the researchers found that patients with severe sleep apnea would have apnea that resulted in self-rescuing awakening. Thus, relying solely on peripheral capillary oxygen saturation (SpO2) and apnea-hypopnea index (AHI) would result in false diagnosis of patients with apnea-induced awakening as having mild symptoms. This study showed that altered brainwave activity can accurately reflect the features of sleep apnea and is not affected by apnea-induced awakening. It also found new brainwave features associated with sleep apnea.
|
|---|
