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Author: 廖偉杰
Wei-Chieh Liu
Thesis Title: 基於逆轉換演算法與支援向量機的心電圖分類器
ECG Classifier Based on Inverse Transformation Algorithm and Support Vector Machines
Advisor: 洪西進
Shi-Jinn Horng
Committee: 洪西進
林韋宏
顏成安
Degree: 碩士
Master
Department: 電資學院 - 資訊工程系
Department of Computer Science and Information Engineering
Thesis Publication Year: 2022
Graduation Academic Year: 110
Language: 中文
Pages: 83
Keywords (in Chinese): 支援向量機心電圖心電圖轉換穿戴裝置
Keywords (in other languages): Support Vector Machine, ECG, ECG transform, Wearable device
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本篇論文主要是透過機器學習(Machine Learning)中支援向量機(Support Vector Machine)演算法對人體心電訊號圖(Electrocardiograph, ECG 以下簡稱心電圖)進行分類。採用穿戴裝置(Wearable device)以便利性與非侵入性檢測(Non-invasive Procedures)的方式來取得人體心電圖,將心電圖數值化後,再將其轉換成數值向量,透過支援向量機演算法找出心電圖之特徵,並對其進行分類。然而,數值化後的心電圖大部分的特徵是不必要的,期望找出必要的特徵後,進而將心電圖作精準的分類。在本研究中,開發出一種逆轉換演算法將心電圖數值化,數值化後經由反覆實驗,發現每一種類別皆有不同的必要特徵,且這些必要特徵能對人體心電圖作出精準的分類。採用非專業醫療儀器,卻成功地對穿戴裝置的心電圖進行分類,達到便利性與非侵入性檢測的目的。


In this thesis, we develop a classifier based on Support Vector Machine for classifying electrocardiograph (ECG) of wearable devices. The wearable device is used to obtain the ECG of the human body in a convenient and non-invasive way. After the ECG is digitized, it is converted into a numerical vector, and the features of the ECG are found and classified through the support vector machine algorithm. However, most of the features of the digitized ECG are unnecessary, and it is expected that ECG can be accurately classified after finding the necessary features. We also develop an inverse transformation algorithm to digitize the ECG in this study. Repeated experiments with digitized ECGs, it was found that each category has different necessary feature which can accurately classify the human ECG. The development of an algorithm to convert the ECG into digitized form and the accurate classification of the ECG are the focus of this paper. Instead of using the professional medical instruments, the purposes of convenience and non-invasive detection are achieved as the ECG measured by the wearable device is successfully classified.

摘要 I Abstract II 致謝 III 目錄 IV 圖片目錄 VII 表格目錄 IX 1 緒論 1 1.1 研究動機 1 1.2 研究目的 1 1.3 研究貢獻 2 1.4 本論文之架構 2 2 系統架構與相關設備規格 3 2.1 系統架構 3 2.2 相關設備規格 3 3 心電訊號圖介紹 4 3.1 心電訊號的傳導構造 4 3.2 心臟的細胞 5 3.3 心電圖的時間與電壓 7 3.4 心電圖成圖原理 8 4 支援向量機 12 4.1 符號說明 12 4.2 資料分散情況 13 4.3 測試階段 16 4.4 資料不分散情況 16 4.5 支援向量機的核函數 21 5 資料集與驗證標準 22 5.1 資料集 22 5.2 驗證標準 24 6 研究方法 27 6.1 資料的預處理 27 6.2 逆轉換演算法 29 6.3 特徵擷取 34 6.4 特徵選擇 35 7 實驗過程 36 7.1 實驗設計 36 7.2 解析度的影響 37 7.3 性別分類 39 7.4 有無身孕分類 47 7.5 胎兒性別分類 55 8 實驗結果 63 8.1 性別分類 63 8.2 有無身孕分類 65 8.3 胎兒性別分類 67 9 結論與未來展望 69 9.1 結論 69 9.2 未來展望 69 參考資料 70

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