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研究生: 楊岳穎
Yueh-Yiing Yang
論文名稱: 以適應性特徵擷取及改進支持向量機檢測心電圖心律不整
Detection of cardiac arrhythmia in electrocardiograms using adaptive feature extraction and modified support vector machines
指導教授: 高文忠
Kao, Wen-Chung
學位類別: 碩士
Master
系所名稱: 電機工程學系
Department of Electrical Engineering
論文出版年: 2010
畢業學年度: 98
語文別: 中文
論文頁數: 79
中文關鍵詞: 心電圖適應性特徵選取支持向量機k-means分群法
英文關鍵詞: electrocardiogram (ECG), adaptive feature extraction, support vector machines (SVMs), k-means clustering
論文種類: 學術論文
相關次數: 點閱:156下載:9
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  • 心電圖(ECG)分析是檢測心律不整最好的方法之ㄧ,雖然已經有許多相關的演算法已經被提出,但是可靠性低的訊號特徵提取分析或歸納能力較低的辨識器使得系統的辨識率仍然不能達到要求。本論文提出適應性特徵擷取與改良的支持向量機(SVMs)的心電圖心律不整檢測系統。首先利用小波轉換係數及訊號之振幅或週期等參數作為系統的候選人,針對每一個分類器適應性的擷取出少數特定的特徵;而改良式支持向量機結合k-means分群法與一對一支持向量機,並且修改其投票機制,進一步提高了相似類別之間的辨識率。此心電圖心律不整檢測系統使用了超過100,000筆的MIT-BIH心律不整資料庫樣本進行測試,平均辨識率高達97.96%。

    The electrocardiogram (ECG) analysis is one of the most important approaches to cardiac arrhythmia detection. Many algorithms have been proposed, however, the recognition rate is still unsatisfactory due to unreliable feature extraction in signal characteristic analysis or poor generalization capability of the classifier. In this paper, we propose a system for cardiac arrhythmia detection in ECGs with adaptive feature selection and modified support vector machines (SVMs). Wavelet transform-based coefficients and signal amplitude/interval parameters are first enumerated as candidates, but only a few specific ones are adaptively selected for the classification of each class pair. A new classifier, which integrates k-means clustering, one-against-one SVMs, and a modified majority voting mechanism, is proposed to further improve the recognition rate for extremely similar classes. By testing the system with more than 100,000 samples in MIT-BIH arrhythmia database, the average recognition rate is 97.72%.

    目錄 摘要 i ABSTRACT ii 誌謝 iii 目錄 iv 圖目錄 vi 表目錄 viii 第一章 緒論 1 1.1 前言 1 1.2 研究背景 3 1.3 問題描述 5 1.4 論文架構 6 第二章 心電圖簡介及相關研究探討 7 2.1 心電圖簡介 7 2.2 PQRST型態與判讀 10 2.3 MIT-BIH心律不整資料庫簡介 14 2.3.1 MIT-BIH病症介紹 15 2.4 相關研究概述 16 第三章 系統架構 20 3.1 系統簡介 20 3.2 系統辨識流程 21 第四章 心律不整之心電圖辨識演算法 25 4.1 心搏偵測 25 4.2 次級分類演算法k-means分群演算法 28 4.2.1 k-means分群演算法 28 4.2.2 次級分類演算法 29 4.3 心電圖訊號候選特徵擷取 33 4.3.1 小波轉換(Wavelet Transform)簡介 33 4.3.2 以小波為基礎之特徵擷取演算法 35 4.3.3 心電圖之生理特徵統計 39 4.4 適應性特徵擷取系統 40 4.5 支持向量機介紹 43 4.5.1 改良支持向量機 46 4.6 心電圖之心臟疾病辨識 48 第五章 對心律不整之心電圖辨識實驗結果 50 5.1 實驗樣本製備 50 5.2 辨識結果 50 5.3 文獻比較 69 第六章 結論與未來展望 71 6.1 結論 71 6.2 未來展望 71 參考文獻 73 自傳 78 學術成就 79

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