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研究生:尤俊國
研究生(外文):Chun-Kuo Yu
論文名稱:以適應特徵選擇與支持向量機實現心電圖辨識系統
論文名稱(外文):lectrocardiogram Analysis with Adaptive Feature Selection and Support Vector Machines
指導教授:高文忠黃奇武
指導教授(外文):Wen-Chung KaoChi-Wu Huang
學位類別:碩士
校院名稱:國立臺灣師範大學
系所名稱:應用電子科技研究所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2006
畢業學年度:94
語文別:中文
論文頁數:107
中文關鍵詞:心電圖小波轉換支持向量機
外文關鍵詞:ECGWavelet TransformSVM
相關次數:
  • 被引用被引用:0
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心電圖提供了診斷心臟病病和心血管病症的功能,為了能夠及時的監控病人的生理狀態,有時候必須持續長時間且連續的記錄病患所產生的心電圖資料,採用更多的心電圖資訊來判斷波形的物理變化,藉此可以較正確的評估病患目前生理情況,但是通常所得到的心電圖資料必須由專業的醫護人員來解析判讀。

本研究所提出了一個新的心電圖分析演算法,使用小波轉換分析頻帶來擷取心電圖特徵值,包含了改善特徵選擇和分類系統的設計,所擷取出的特徵向量作為心電圖辨識系統中最重要的特徵。而在心電圖辨識系統中較特別的特徵為QRS複合波組,這是含有極高頻的成份且能量較大的峰值波形。在辨識系統中採用支持向量機作為辨別不同種類心臟疾病的分類器。
Electrocardiogram signal (ECG) provides the functional aspects of the heart and cardiovascular system. In order to monitor the real-time evolution of the patients, the ECG signal is sometimes recorded continuously for one or more days. The availability of more and more information on the physical status and evolution of the patient is always desirable, but usually the information needs to be assimilated and evaluated by doctors or nurses.

We propose a new wavelet transform based ECG analysis algorithm with improving the feature extraction and classifier design. Inherited from the properties of WT, the extracted vectors can represent the most important features for ECG signals. It is particularly true for the QRS complex the can be recognized as the high frequency and high energy components. The system adopts support vector machines (SVM) to differentiate the types of heart diseases.
中文摘要 i
英文摘要 ii
誌  謝 iii
目  錄 iv
圖 目 錄 vi
表 目 錄 viii
第一章 緒論 1
1.1 研究動機 1
1.2 相關研究 5
1.3 目前問題的檢討 13
1.4 本論文提出之方法 14
1.5 論文架構 17
第二章 心電圖原理和病症資料 18
2.1 心電圖原理 18
2.1.1 心電圖簡介 18
2.1.2 心臟的結構 19
2.1.3 心臟循環系統 22
2.1.4 心臟傳導系統 23
2.1.5 十二導程心電圖 28
2.2 MIT-BIH心律不整資料庫介紹 30
2.3 心電圖病症簡介 33
第三章 系統架構 45
3.1 系統簡介 45
3.2 辨識流程 46
第四章 心電圖診斷理論基礎 50
4.1 K-MEAN分群法 50
4.2 小波轉換簡介 53
4.3 支持向量機簡介 57
第五章 心電圖診斷系統 64
5.1 心電圖辨識相關研究 64
5.2 峰值(R波)偵測 70
5.3 K-MEAN分類 72
5.4 特徵值抽取 74
5.5 特徵值的統計分析 77
5.6 病症辨識 83
第六章 實驗結果 87
6.1 心電圖辨識結果 87
6.2 相關文獻與本研究之比較 88
第七章 結論與未來展望 91
7.1 結論 91
7.2 未來展望 92
參考文獻 93
作者簡介 97
參 考 文 獻
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