睡眠呼吸中止症是一種在睡眠時停止呼吸的疾病，病人在睡眠時會發生反覆呼吸 停止的情況，嚴重時甚至危及生命安全。一次睡眠呼吸中止症事件定義為病患睡 眠時發生停止呼吸超過十秒以上。睡眠呼吸中止事件可分為兩種類型 : 阻塞型 睡眠呼吸中止症(OSA)事件及中樞型睡眠呼吸中止症(CSA)事件。除了以上兩種類 型外，尚有一種病症稱為睡眠淺呼吸(Hypopnea)。淺呼吸為呼吸氣流下降超過 50%以上。在醫學上使用睡眠呼吸\淺呼吸指標(AHI)來判定病人的嚴重程度。罹 患睡眠呼吸中止症的病人會有專注力下降、日間嗜睡、情緒暴躁及夜間覺醒等症 狀使得生活品質下降。由於發病時處睡眠時間，常容易讓患者忽略甚至未查覺。 目前醫院透過睡眠多項生理檢查(Polysomnography, PSG)診斷睡眠呼吸中止症。但 由於該檢測花費昂貴、配戴多條感測器使患者感到不舒適以及環境限制，本研究 使用低成本的三軸加速度感測器分別黏貼在左胸以及左腹取得之胸腹起伏訊號 搭配 PSG 中的血氧濃度訊號以簡化睡眠呼吸中止症的檢查。
本研究中，取得三軸加速度感測器的胸腹訊號及血氧濃度訊號後，先利用三軸選 擇法(TAA selection method)將胸腹訊號做處理，接著萃取胸腹訊號的振幅比例、 頻率比例與共變異數以及血氧濃度中的最小值、最大值、中位數、一次微分後的 變異數、中位數與最小值的差異與平均值共十項特徵訊號。最後，利用以上十項 特徵訊號放進支持向量機(SVM)做訓練後所得出之分類器配合狀態機(state machine)以分類不同病症，達到所要之目標，並且透過 SpO2 訊號建立血氧濃度 偵測的演算法，透過找到血氧濃度下降百分之三的區間，輔助狀態機做事件的分 類。透過醫師專業經驗的指引，本研究的支持向量機訓練所取得的資料，是透過 病人生理表現型資訊來做篩選，以得到更準確的結果。
在 SVM 模擬比較中，使用三軸加速度感測器所取得之胸腹訊號分析模擬結果的 準確度為 82.26%。

Sleep apnea syndrome (SAS) is a well-known sleep disorder nowadays. People suffering SAS cease breathing during sleeping because 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, uncomfortable 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 separately to sense the thoracic movement (THO) and abdominal movement (ABD) signals.
This thesis proposed a sleep apnea / hypopnea event detection algorithm by THO, ABD movement signals and blood oxygen saturation levels (SpO2) signal during sleep. The proposed algorithm first combines two three-dimensional signals for two tri-axial accelerometers (TAA) into two one-dimensional signals, TAA-ABD and TAA-THO, respectively, and segments the overnight recorded TAA-ABD and TAA-THO signals into 10-second windows to extract two features from both TAA-ABD and TAA-THO, fundamental frequency ratio and 95% quantile amplitude ratio. The SpO2 signal is segmented into 20-second windows with six features, minimum, maximum, median, mean, variance of the first derivative, and difference between median and minimum. The proposed algorithm uses a SpO2 desaturation detector to detect SpO2 desaturation, and a support vector machine (SVM) with ten features to construct classifiers and a SVM state machine to identify the apnea and hypopnea events.
In this study, inspired by the physiological knowledge, we propose a phenotype-based approach. It is well known that the gender, body-mass index (BMI), and age are intimately related to the sleep apnea pattern and severity. With the phenotypical information we can reduce the training subject from 63 to 15 to improve our training speed. And achieve 82:26% accuracy of AHI classification. The results indicates that the proposed algorithm has great potential to classify the severity of patients in clinical examinations for both the screening and the homecare purposes.

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