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研究生:顏紹雍
研究生(外文):Shao-Yung Yen
論文名稱:心電訊號特徵擷取與心律不整判斷系統之研究
論文名稱(外文):The ECG features detection and arrhythmia classification system
指導教授:江行全江行全引用關係
學位類別:碩士
校院名稱:元智大學
系所名稱:工業工程與管理學系
學門:工程學門
學類:工業工程學類
論文種類:學術論文
論文出版年:2007
畢業學年度:95
語文別:中文
論文頁數:98
中文關鍵詞:心電圖 (ECG)QRS複合波型MIT-BIH資料庫心律不整分類
外文關鍵詞:Electrocardiogram (ECG)QRS complexforward-backward algorithmMIT-BIH databasecardiac arrhythmia classification
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醫師與醫護人員通常使用心電圖(Electrocardiogram, ECG)來判讀心臟異常訊息,此工作之執行經常耗費大量的人力資源與時間。為了能協助專業醫療人員,即時判斷病患是否患有心律不整之可能性。本研究發展一演算系統來擷取心電訊號中的QRS複合波型,結合臨床病理的心律不整判斷準則,完成一個能夠將儀器所蒐集得之心電訊號資料作為輸入,將病患是否有心律不整之異常作為輸出之監測診斷系統。對於一般較規律且雜訊較少的心電圖而言,以斜率理論為基礎的演算方法有不錯的偵測效果,但對於雜訊影響較大的心電圖,仍然有不少誤判的情形。因此本研究針對以斜率為基礎之演算法提出修正,增加向前及向後搜尋資料並刪除振幅過小波型之規則以改進其缺失。以MIT-BIH心律不整資料庫做為訓練資料以設定參數,同時使用實務上之安心卡資料做驗證及測試。將演算法偵測所獲得之結果透過文獻所提出之心律不整判斷規則,進行心律不整異常之判讀。由於受到特徵擷取誤判的影響,後續分類結果所產生之誤判為將正常患者誤判成有心律不整之情形。此類誤判在MIT-BIH資料庫的27筆資料中有1筆,分類正確率為96.3%;在安心卡的52筆資料中有5筆,分類正確率為90.38%。
Automated diagnostic system has become an established component of medical technology. The main concept of the medical technology is an inductive engine that learns the decision characteristics of the diseases and then can be used to diagnose future patients with uncertain diseases states. Electrocardiogram (ECG) is an important tool in diagnosing the condition of the heart. It provides valuable information about the functional aspects of the heart and cardiovascular system. This research develops an algorithm to extract the QRS complex wave and combines the clinical judgment criterion of the cardiac arrhythmia to model a classification system. The system input data is ECG signal and output is the classification of cardiac arrhythmia. The method is forward-backward algorithm that is revised So and Chan method and augments forward and backward searching rules and also deletes lower R amplitude to improve the detection performance. The ECG signals are taken form MIT-BIH arrhythmia database, which are used to classify 4 different arrhythmias for training. There are normal, premature ventricular contraction, ventricular flutter/fibrillation and 2o heart block (Tsipouras, 2005). Regard MIT-BIH arrhythmia database as training data in order to set up the parameters, verifying and testing by real medical ECG data. According to QRS complex extraction and clinical judgment criterion of the cardiac arrhythmia, the proposed approach provided 96.3% accuracy of classification on MIT-BIH database and 90.38% accuracy of classification on empirical medical ECG data.
摘要 i
Abstract ii
目錄 ii
圖目錄 v
表目錄 vii
第一章 緒論 1
1.1 研究背景 1
1.2 研究動機 1
1.3 研究目的 3
1.4 研究方法與流程 3
1.5 研究架構 5
第二章 文獻探討 6
2.1 心電圖簡介 6
2.2 訓練與測試資料介紹 8
2.2.1 MIT-BIH心律不整資料庫 8
2.2.2 亞東醫院安心卡資料 10
2.3 QRS波偵測演算法 11
2.3.1 類神經網路 11
2.3.2 小波轉換 14
2.3.3 傳統偵測演算法 17
2.3.4 So and Chan演算法 22
2.4 心跳分類方法 23
2.4.1 心跳區間特徵分類法 23
第三章 研究方法 28
3.1 研究流程 28
3.2 Forward-backward演算法 29
3.3 心律不整分類規則 38
3.4 評估指標 42
第四章 實驗結果與討論 44
4.1 參數設定 44
4.2 Forward-backward演算法偵測結果 58
4.2.1 MIT-BIH資料庫偵測結果 58
4.2.2 安心卡資料偵測結果 64
4.3 RR-interval心律不整分類結果 71
4.3.1 MIT-BIH資料庫分類結果 71
4.3.2 安心卡資料分類結果 73
第五章 結論與貢獻 74
5.1 結論 74
5.2 貢獻與未來研究 75
參考文獻 76
附錄 79
附錄A MIT-BIH資料-偵測結果圖形 79
附錄B MIT-BIH資料庫訓練參數k之偵測結果 82
附錄C MIT-BIH資料庫偵測結果 84
附錄D MIT-BIH資料庫分類結果 87
附錄E 安心卡資料-偵測測試結果圖形 88
附錄F 安心卡資料特徵擷取結果 92
附錄G 安心卡資料-分類結果 97
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