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研究生:呂忠憲
研究生(外文):Chung-Hsien Lu
論文名稱:使用心電圖衍生呼吸訊號檢測睡眠呼吸中止及低通氣症候群
論文名稱(外文):Detection of Sleep Apnea and Hypopnea Syndrome Using Electrocardiogram Derived Respiratory Signal
指導教授:簡福榮簡福榮引用關係
口試委員:曾傳蘆譚旦旭黃永發
口試日期:2016-07-19
學位類別:碩士
校院名稱:國立臺北科技大學
系所名稱:電機工程研究所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
畢業學年度:104
語文別:中文
中文關鍵詞:決策合併心電圖多導睡眠生理儀睡眠呼吸中止症
外文關鍵詞:Machine LearningElectrocardiogramPolysomnographyHypopneaSleep Apnea
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近年來睡眠呼吸中止症(Sleep Apnea and Hypopnea Syndrome, SAHS)逐漸受到重視。臨床研究顯示睡眠呼吸中止症對人體精神及心血管方面都會造成不良影響,其中85 %到90 %為阻塞性睡眠呼吸中止症(Obstructive Sleep Apnea, OSA)。因此檢測與治療OSA的方式廣為學界以及醫界重視。目前要準確判定是否患有睡眠呼吸中止症,必須在睡眠中心睡一個晚上,透過多導睡眠生理儀的多項生理訊號記錄,離線判讀受測者的呼吸暫停指數(Apnea and Hypopnea Index, AHI),以做為進一步醫療的依據。此方法費時且代價昂貴,因此發展出更簡便的檢測方法可以提升治療的普及性。
本論文使用心電圖(Electrocardiogram)生理訊號來判定是否發生阻塞性睡眠呼吸中止症。共提取95種統計、時域及頻域特徵。並且以11種分類器訓練、測試。接著將其中3種效果不錯分類器做決策合併,分別為:縮減誤差修剪決策樹的拔靴集成法(Bootstrap Aggregating Reduced-Error Pruning Decision Tree, or Bagging REPTree),單層決策樹的自適應增強演算法(Adaptive Boosting Decision Stump, or AdaBoost Decision Stump),和交替決策樹的拔靴集成法(Bootstrap Aggregating Alternating Decision Trees, or Bagging ADTree)。其中最好的為多數決,可得到68.92%的準確率、67.39%的靈敏度和69.38%的特異度。為了使結果更好,最後再加入血氧飽和度(oxygen saturation)35種特徵,可以將結果提高為78.14%的準確率、84.35%的靈敏度和77.57%的特異度。
Sleep Apnea and Hypopnea Syndrome (SAHS) have become an increasingly important public-health problem in recent years. Clinical studies have shown that SAHS can adversely affect patient’s cardiovascular system and cause behavior disorder. Moreover, up to 85%-90% of these cases are of obstructive sleep apnea (OSA). Therefore, the study of how to diagnose, detect and deal with OSA is becoming a significant issue. Polysomnography (PSG) is practically considered as the standard means for SAHS diagnosis. However, PSG is expensive and time-consuming since it requires SAHS patients to spend one night in a sleep laboratory with professional technicians and doctors. Accordingly, with the purpose of improving such inconvenience, there exists a huge demand to develop much simplified methods for diagnosing the OSA.
In this paper, we exploit electrocardiogram (ECG) derived respiratory signals to determine whether the occurrence of OSA. At first, 95 kinds of statistics, time domain, and frequency domain features are extracted. Then 11 machine-learning algorithms are applied to categorize SAHS events. Among them, the best three algorithms are picked out for further decision combination: they are AdaBoost with Decision Stump, Bagging with REPTree, and Bagging with ADTree. The experimental results show that the Majority Vote performs good behavior of sensitivity and specificity. It is able to achieve sensitivity at 67.39%, specificity at 69.38%, and accuracy at 68.92%, respectively. In case of including additional 35 types of oxygen saturation features, the performance can be further improved. It achieves accuracy at 78.14%, sensitivity at 84.35%, and specificity at 77.57%, respectively.
目錄
摘 要 i
ABSTRACT iii
誌 謝 v
目錄 vi
表目錄 viii
圖目錄 ix
第一章 緒論 1
1.1 研究動機與背景 1
1.2 相關研究 1
1.3 研究方法 3
1.4 論文架構 4
第二章 睡眠呼吸中止症 5
2.1睡眠呼吸中止症簡介 5
2.1.1 病症分類及併發症 7
2.2 多導睡眠生理儀簡介 7
2.3睡眠呼吸中止症診斷及治療 8
第三章 分類器 10
3.1 常用分類器 10
3.1.1 支持向量機(Support Vector Machine) 10
3.1.2 決策樹(Decision Trees) 15
3.2 集成學習 21
3.2.1 AdaBoost 21
3.2.2 Bagging 24
第四章 實驗流程與結果 26
4.1 實驗環境 26
4.1.1硬體及軟體 26
4.2 實驗流程 28
4.3特徵擷取 30
4.3.1 心電圖學 30
4.3.2 心率變異(Heart Rate Variability,HRV) 30
4.3.3 EDR訊號 31
4.3.4 特徵選取 31
4.4 實驗結果與討論 33
4.4.1 評斷標準 33
第五章 結論及未來展望 44
5.1 結論 44
5.2 未來展望 44
參考文獻 45
參考文獻

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