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研究生: 沈家平
Shen Chia Ping
論文名稱: 心電圖訊號分析演算法與硬體架構設計
The Algorithm and Hardware Design for Electrocardiogram Signal Analysis
指導教授: 高文忠
Kao, Wen-Chung
學位類別: 碩士
Master
系所名稱: 工業教育學系
Department of Industrial Education
論文出版年: 2007
畢業學年度: 95
語文別: 中文
論文頁數: 103
中文關鍵詞: 小波轉換心電圖支持向量機
英文關鍵詞: wavelet transform, electrocardiogram, support vector machine
論文種類: 學術論文
相關次數: 點閱:287下載:46
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  • 心電圖是心臟搏動相關的電位變化圖,而且對於醫師診斷出不同的心臟疾病和來監測、評估病人的病情是非常重要的。從兩段心跳之間所取出的心電圖樣本形狀通常用來辨識心臟方面的疾病。本篇論文中我們提出一個高效能的心電圖辨識系統,此系統可以適應性地從446個參數中選擇最重要的特徵出來,再利用支持向量機來辨識這些心電圖。而我們測試MIT-BIH Arrhythmia database中的心電圖辨識率可達98.09%,在已發表的論文中是效果最好的方法之一。

    另外,我們也設計一個硬體的工具來完成抽取小波轉換的係數以及支持向量機的辨識器。此硬體可以增加辨識過程的速度以及嵌入成可攜式的元件。

    Electrocardiogram (ECG) is a representation of the electrical activity of heartbeats and it is quite an important signal for doctors to diagnose cardiac disease and monitor patient conditions. The shape of each ECG beat cycle as well as the interval time between two successive beats is commonly used for identifying the types of heart diseases. In this thesis, we propose a high performance ECG recognition system which adaptively selects the most important features from 446 candidate parameters and identifies the heart condition based on modified support vector machines (SVM). With tested by MIT-BIH Arrhythmia database, the final classification result can achieve 98.09% which is believed to be the best one in the published literatures.

    On the other hand, we also design a hardware engine dedicated for extracting wavelet transform based features and classification by SVM. The engine may help to speed up the recognition process and integrated into a portable device.

    第一章 緒論 6 1.1研究動機 6 1.2研究背景 9 1.3問題描述 11 1.4本論文所提出的方法 13 1.5論文架構 15 第二章 系統架構 16 2.1軟體簡介 17 2.2硬體整合系統 21 第三章 相關研究 23 第四章 心電圖診斷理論基礎與系統 30 4.1心電圖病症簡介 30 4.2 K -MEANS分群法 43 4.3小波轉換簡介 46 4.4支持向量機簡介 50 第五章 心電圖辨識軟體系統設計 59 5.1特徵值抽取與統計分析 59 5.2特徵抽取改進與特性分析 67 第六章 特徵抽取與辨識硬體架構設計 71 6.1特徵抽取電路 71 6.2 SVM電路 75 第七章 實驗結果 78 7.1軟體實驗結果 78 7.2硬體實驗結果 84 第八章 結論與未來展望 92 參 考 文 獻 93 作者自傳 97 著作 圖目錄 表目錄

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