| Summary: | 碩士 === 國立成功大學 === 電腦與通信工程研究所 === 101 === Obstructive Sleep Apnea (OSA) is a respiratory tract obstruction caused by recurrent respiratory tract collapse, then leading to stop breathing disease. About OSA diagnosis, the doctor will ask the patient to the hospital for a sleep examination, using Polysomnpgraphy and patient’s snore and cough for analysis. Because the number of OSA patients has been increased and hospital beds are not enough, resulting in a large number of queued condition that causes severe patient cannot be immediate examination and treatment. Therefore, we propose a mechanism to detect snore and cough, patients can use this mechanism for snore and cough detection in long-term sleep at night. The snore and cough quantitative data help doctors to diagnose diseases, and doctors determine whether the patient need for sleep examination or not. The mechanism can eliminate a lot of patients queuing.
The detection mechanism include three parts. First, the patient all night sound data segments to independent events. Second, we change time domain signal to frequency domain signal by Fourier Transform, and extract features from snore and cough respectively. The last one, we use Support Vector Machine and Hidden Markov Model for establishing the detection mechanism for snoring and coughing sound detection at night.
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