臺灣博碩士論文加值系統

睡眠呼吸中止症候群被認為是二十世紀所發現最重要的睡眠障礙，也是睡眠醫學重要的研究方向，患者因睡眠呼吸中止造成睡眠期間反覆缺氧的結果而容易導致心肺功能受損、白天嗜睡、疲勞駕駛等併發症與後遺症，嚴重者甚至造成睡眠中猝死，不但耗費醫療資源，也將嚴重影響患者生活品質。一般來說患者並無立即病痛與不適且大多患者都不自覺，容易造成延誤就醫且睡眠多項生理檢查需耗費相當昂貴的成本與人力，目前國內的醫療資源並無法做全面性的篩選，就預防醫學的角度而言，建立一套可以優先準確篩選出中度與重度睡眠呼吸中止症候群的預測模型有其必要性。

本研究將以病患就醫過程中產生的相關問卷資料為基礎，再運用基因演算法來解決複雜的問卷變項問題，建立一套可以準確預測出中度與重度睡眠呼吸中止症候群診斷系統，提供高危險群簡便、準確的預測模型，讓醫療人員及早針對不同嚴重程度的患者提供適當的治療與建議，預防其併發症與後遺症的發生，進而達到預防性醫學的目標。從研究成果顯示，運用基因演算法模型明顯優於普遍被流行病學者用來建立疾病預測模型的Logistic Regression方法，其診斷預測績效也更為優異與準確。

Sleep apnea syndrome is regarded as the most important sleep disorder discovered in the 20th century as well as the significant research direction for sleep medicine. Sleeping breath-ceasing results in a symptom of repeated arterial anoxemia in sleeping, which can easily cause harm to cardio- pulmonary and causes sequela and complicating disease such as excessive day-time sleepiness and drowsy driving, even sudden death in sleeping. In such situations, the disease not only consume a lot of medical resources but also has a bad impact on the patient’ living quality. Generally speaking, most of patients are unaware of sleep apnea syndrome for its painlessness and none- discomfort, which will delay treatment. Moreover, it requires for high costs and personnel expense on polysomnograph（PSG）examination. We could not do overall examination according to the contemporary medical resources. In the aspect of preventive medicine, it is necessary to establish a set of prediction model to accurately give preference to moderate serious sleep apnea syndrome.

In this study, by means of computerized analyzing the laboratory data of the patients during medical visits we use Genetic Algorithms（GA）to solve complicated problems from changeable items of questionnaire to establish a system that can accurately predict moderate serious sleep apnea syndrome. The system can provide high-risk populations with convenient and precise predictive model so as to help medical personnel that they could propose the proper treatments and suggestions for patients according to various degrees of disease. The result of the research clearly shows that GA model is better than Logistic Regression that is widely used for establishing Disease Predictive model by scholars of epidemic diseases, and it brings more accurate effects than that of the later.

第壹章 緒 論
1.1 研究背景 1
1.2 研究動機 2
1.3 研究目的 4
1.4 研究方法與步驟 5
第貳章 文獻探討
2.1 國際睡眠疾病分類 8
2.2 睡眠呼吸中止症候群的臨床症狀與治療 14
2.3 睡眠呼吸中止症的危險因子 21
2.4 基因演算法文獻 25
第參章 研究方法
3.1 研究方法與架構 36
3.2 變數選擇與操作型定義 40
3.3 基因演算法架構 41
3.4 實驗架構與評估方式 44
3.5 睡眠多項生理檢查 48
第肆章 實驗結果
4.1 綜合評估問卷結果說明 50
4.2 系統軟硬體環境說明 54
4.3 實驗訓練結果 55
4.4 實驗結果綜合分析 64
第伍章 結論與建議
5.1 結論 69
5.2 研究發現與貢獻 70
5.3 研究限制與建議 72
參考資料
中文文獻 74
英文文獻 75
電子資料 79
附錄
附錄一：柏林氏問卷表 80
附錄二：綜合睡眠評估問卷表 81

表目錄
表一：全國睡眠檢查施行統計表 3
表二：阻塞型睡眠呼吸中止症候群併發症 16
表三：預測模型之特性分析表 26
表四：變數列表 40
表五：基因演算法模型母體排列表 42
表六：混亂矩陣圖 43
表七：綜合睡眠評估問卷人口特徵統計表 51
表八：睡眠多項生理檢查RDI統計表 51
表九：綜合睡眠評估問卷資料統計表 53
表十：硬體設備規格 54
表十一：軟體設備規格 54
表十二：參數設定值 55
表十三：訓練樣本與測試樣本分配表 56
表十四：基因演算法模型訓練結果（訓練樣本）65
表十五：基因演算法模型訓練結果（測試樣本65
表十六：基因演算法模型訓練結果（總樣本數）65
表十七：基因演算法模型評估績效總表 66
表十八：Logit模型訓練結果 66
表十九：Logit模型評估績效表 67
表二十：GA模型與Logit模型績效指標值比較表 67
表二十一：GA、Logit與ESS預測績效指標值比較表 68

圖目錄
圖一：研究流程圖 7
圖二：正常睡眠呼吸氣道圖 10
圖三：阻塞性睡眠呼吸中止氣道圖 11
圖四：睡眠呼吸中止症候群腦波變化圖（S1~S4）17
圖五：睡眠呼吸中止症候群腦波變化圖（MVT、Snoring、REM）18
圖六：睡眠呼吸中止症候群呼吸道阻塞圖 20
圖七：使用連續正壓呼吸輔助器(CPAP) 20
圖八：打鼾頻率與(EDS)統計圖 23
圖九：睡眠打鼾與性別、年齡關係圖 24
圖十：基因演算法架構基礎 27
圖十一：基因演算法基本架構圖 28
圖十二：基因演算法流程圖 29
圖十三：單點交配圖 32
圖十四：雙點交配圖 33
圖十五：多點交配圖 33
圖十六：均勻交配圖 34
圖十七：綜合睡眠評估問卷表結構圖 37
圖十八：研究架構圖 39
圖十九：睡眠多項生理檢查偵測部位圖 48
圖二十：GA模型－變數值域畫面 56
圖二十一：GA模型－訓練模型（Trainset）畫面 57
圖二十二：GA模型－測試模型（Testset）畫面 58
圖二十三：GA模型－演化母體畫面 58
圖二十四：GA模型－好的解輸出畫面 59
圖二十五：Logit模型輸出系數圖 63
圖二十六：Logit模型實驗結果圖 64
圖二十七：GA模型與Logit模型評估績效圖 68

中文文獻

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蘇木春、張孝德，《機器學習-類神經網路、模糊理論以及基因演算法則》，台北，全華科技，民89。
鄭智銘，《睡眠監測相關研究簡介》元智大學，民91.7。
李佩玲，《肝病防治會刊第三十期》，睡眠障礙與肝臟及疾病，台大醫院胸腔科主治醫師、台灣睡眠醫學學會秘書長。
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江宏志，《運用基因演算法建構疾病預測模型之研究-以尿路結石疾病預測為例》，臺灣大學商學研究所，博士論文，民92。
李文瑞，《運用基因演算法建構疾病早期診斷模型之研究-以糖尿病前期之診斷為例》，輔仁大學資訊管理學系在職專班，碩士論文，民92。
行政院衛生署身心障礙鑑定表 中華民國九十一年二月七日行政院衛生署醫字第0910014811號公告修正。

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電子資料

台灣睡眠醫學學會Taiwan Society of Sleep Medicine（TSSM）
網站：http://www.tssm.org.tw . Accessed May 8, 2006.
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