近年來睡眠呼吸中止症（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的方式廣為學界以及醫界所重視。然而睡眠診斷的研究常需要藉助睡眠檢查室進行整夜甚至多次整夜性的睡眠檢查與評估，有代價昂貴且耗時等缺失。因此，發展一項更簡便的OSA檢測技術，來降低軟體、硬體需求，甚或是大大減少檢測的時間及成本，將能大幅的改善OSA診斷以及治療的有效性及普及率，且更符合經濟效益。
基於台灣目前尚無此技術之開發研究基礎，因此本研究將承襲近來以ECG訊號檢測OSA技術的趨勢經驗，發展一套軟體演算技術，以記錄心電圖訊號為基礎，借助訊號處理、特徵選取技術，配合類神經網路人工智慧的演算規則，建構線上即時OSA檢測系統，期望能夠提高診斷精確度，提升OSA診斷技術之醫療效率與品質。

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. Moreover, 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, both academically and medically. Polysomnography can monitor the OSA with relatively fewer invasive techniques. However, polysomnography-based sleep studies are expensive and time-consuming because they require overnight evaluation in sleep laboratories with dedicated systems and attending personnel. Therefore, to improve such inconveniences, one needs to develop a simplified method to diagnose the OSA, so that the OSA can be detected with less time and reduced financial costs.

Since currently there seems to be no OSA detection technique available in Taiwan, the goal of this work is to develop a reliable OSA diagnostic algorithm. In particular, via signal processing, feature extraction and artificial intelligence, this thesis describes an on-line ECG-based OSA diagnostic system. It is hoped that with such a system the OSA can be detected efficiently and accurately.

論 文 目 錄

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 8
1.4 論文架構 11
第二章 心電圖學與訊號處理 12
2.1 心電圖學簡介 12
2.2 P波、QRS複合波、T波與RR-Interval 13
2.3 十二導極心電圖 14
2.3.1 六個肢導極 14
2.3.2 六個心前導極 15
2.4 心率變異(Heart Rate Variability，HRV) 16
2.5 小波理論與QRS Detection 17
2.5.1 第一階段濾波 18
2.5.2 多重解析度 19
2.6 EDR的訊號處理與EDR值 22
第三章 特徵變數與特徵選取技術 24
3.1 從ECG訊號得到的特徵變數介紹 24
3.1.1 由RR-Interval衍生的特徵變數 24
3.1.2 由EDR訊號衍生的特徵變數 27
3.1.3 改良與新開發的特徵變數 28
3.2 特徵變數選取方法 31
3.2.1 Sequential Backward Selection 33
3.2.2 Sequential Forward Selection 35
3.2.3 Sequential Floating Search 38
第四章 分類器機制 43
4.1 類神經網路 43
4.1.1 MLP網路架構 44
4.1.2 MLP網路在型態鑑別上的設定相關 46
4.2 委員會機器 47
4.3 Averaging Committee 48
4.4 Bagging Ensemble 49
4.5 AdaBoost Algorithm 50
4.6 兩階段決策委員會機器 51
第五章 實驗過程與結果 54
5.1 特徵變數選取實驗與結果 54
5.2 35-Fold Cross Validation 60
5.3 使用委員會機器的ANN分類器 66
第六章 結論與未來期許 73
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Malcolm ST 原著, 呂嘉陞 編譯, 1999, 心電圖學必備, 合記圖書出版社