近年來睡眠呼吸暫止症(SAS)逐漸受到重視，其後遺症會對人體造成精神及心血管方面不良的影響，其中又有高達90%以上為阻塞性睡眠呼吸中止症(OSA)，因此檢測與治療OSA就顯得格外重要。然而睡眠診斷的研究長需要藉助睡眠檢查室進行一次甚至多次整夜性的睡眠檢查與評估，有代價昂貴且複雜等缺失。
本論文將發展僅以呼吸氣流訊號來偵測睡眠呼吸阻塞的方法，借由向量量化編碼技術、訊號處理、特徵選取技術，最後再以類神經網路做分類處理，建構一套呼吸事件檢測系統，來降低軟硬體需求，甚至減少大量檢測的時間與成本，將能大幅改善OSA診斷以及治療的有效性及普及率，更能符合經濟效益。

SAS has become an increasingly important public-health problem in recent years. It can adversely affect neurocognitive, cardiovascular, respiratory diseases and can also cause behavior disorder. Since up to 90% of these cases are obstructive sleep apnea (OSA), therefore, the study of how to diagnose, detect and treat OSA is becoming a significant issue, academically and medically. Polysomnography (PSG) can monitor the OSA with relatively fewer invasive techniques. However, PSG-based sleep studies are expansive and time-consuming because they require overnight evaluation in sleep laboratories with dedicated systems and attending personnel.
This work develops a flow rate based detection method for apneas. In particular, via signal processing, feature extraction and neural network, this thesis introduces a flow rate based detective system. The goal is to detect OSA with less time and reduced financial costs.

論文目錄
論文摘要（中文）...................................................................I
論文摘要（英文）.................................................................II
論文目錄..............................................................................III
圖目錄...................................................................................VII
表目錄.................................................................................VIII
第一章 緒論..........................................................................1
1.1 前言........................................................................................1
1.2 研究動機與背景....................................................................4
1.3 研究方法與步驟概述............................................................7
1.4 論文架構................................................................................9
第二章 向量量化編碼方法................................................10
2.1 最近鄰居分類器.................................................................10
2.2.1 原理...........................................................................10
2.2 向量量化原理....................................................................12
2.3 傳統LBG演算法...............................................................14
2.3.1 向量量化過程中的失真...........................................14
2.3.2 傳統LBG增加代碼的方法.....................................15
2.4 修正型LBG演算法..........................................................15
2.4.1 主軸方向...................................................................16
2.4.2 修正型LBG演算法.................................................17
第三章 分類器機制............................................................21
3.1 類神經網路................................................................................21
3.1.1 MLP網路架構………………………………………..…22
3.1.2 MLP網路在型態鑑別上的設定相關....................................24
3.2 委員會機器..........................................................................25
3.3 Average Committee………………………………………..26
第四章 呼吸氣流訊號分類方法........................................27
4.1 呼吸氣流訊號之前處理.......................................................27
4.2 應用VQ編碼方法於呼吸氣流訊號……………………….28
4.3 波形特徵................................................................................33
4.3.1 原始特徵…………………………………………….34
4.3.2 機率差大小重新排序………………...……………..35
4.3.3 機率差絕對值大小重新排序……………………….40
4.3.4 刪減維度…………………………………………….44
4.4 波形特徵使用次數................................................................47
4.4.1 原始特徵……………………………………...……..48
4.4.2 機率差大小重新排序……………………………….50
4.4.3 機率差絕對值大小重新排序……………………….51
4.4.4 刪減維度………………………………………….…52
第五章 實驗方法與結果....................................................57
5.1 波形特徵及其相關特徵變數實驗方法與結果………….58
5.2 直方圖特徵及其相關特徵變數實驗方法與結果…….….64
第六章 結論與未來展望....................................................70
參考文獻................................................................................72

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