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研究生:林俊杰
研究生(外文):Chun-Chieh Lin
論文名稱:應用智慧型計算在12導程心電圖於心臟疾病之辨識
論文名稱(外文):Cardiac Disease Classification with Clinical 12-Lead ECG by Using Computational Intelligence Approach
指導教授:張百棧張百棧引用關係
指導教授(外文):Pei-Chann Chang
學位類別:博士
校院名稱:元智大學
系所名稱:資訊管理學系
學門:電算機學門
學類:電算機一般學類
論文種類:學術論文
論文出版年:2011
畢業學年度:99
語文別:英文
論文頁數:62
中文關鍵詞:12導程心電圖心臟疾病心房顫動心肌梗塞盲訊號分離隱藏馬可夫模型主成分分析多項式近似法特徵選取
外文關鍵詞:12-Lead ECGCardiac DiseaseAtrial FibrillationMyocardial InfarctionBlind Source SeparationHidden Markov ModelsPrincipal Component AnalysisPolynomial ApproximationFeature Extraction
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在臨床病例醫學中,心電圖在心肺相關疾病上為一最為廣泛應用的非侵入式診斷工具。心電圖監測病人的心跳狀況,並對於心房與心室的活動給予精準且重要的資訊。在處理並瞭解心電圖波形的過程,關鍵在於其在時間上形態轉換之處理。臨床上,12導程心電圖在多數的醫院中均已有所應用,其對於心臟疾病提供了更多詳盡的波形資料。本研究將著重於利用不同的智慧型技術,透過分析12導程心電圖之形態,從中擷取心電訊號之特徵,並藉以判定該病例所可能之疾病。本研究中,使用心房顫動及心肌梗塞做為本研究之判定病例。於心房顫動病例中,本研究使用盲訊號分離技術,從12導程心電圖中分離出可能之顫動訊號,並將其視為用以判別病例之特徵;於心肌梗塞病例中,本研究則使用隱藏式馬可夫模型計算在不同導程中,該心跳波形可能之發生機率,本研究並使用主成份分析及多項式近似法擷取ST片段中之特徵資訊。此三類數學模型均使用於12 導程心電圖中計算出可用於判斷心肌梗塞之特徵選取,其中主成分分析對於單一心跳之診斷率較高而多項式近似法則對於病例之診斷較為有效。

In clinical medicine, Electrocardiogram (ECG) is one of the most widely used non-invasive diagnostic tools for cardiopulmonary diseases. ECG monitors the patients’ heart-beat and clinically gives accurate and important information about the activities of atrium and ventricle. The key in treating ECG complex is using the morphology in time detection. Clinical 12-lead ECG data is now available in most hospitals and it includes more detailed information about cardiac disease. This research focuses on how to extract the features in ECG data by analyzing the morphological characteristics through different approaches. Atrial Fibrillation (AF) and Myocardial Infarction (MI) are used to verify the applied computational intelligence approaches. For AF classification, Blind Source Separation (BSS) is adopted to separate the estimated fibrillation sources from 12-lead ECG. The extracted sources can be regarded as the features of AF and used to classify the input cases. Second, for MI classification, three mathematical models are applied to calculate the feature vector of MI cases. Hidden Markov Models (HMMs) are used to calculate the likelihood value for each lead; Principal Component Analysis (PCA) is adopted to find the main components of ST segment and polynomial approximation is applied to calculate the coefficients of fitted polynomial formula of ST segment. The calculated coefficients can be used as input feature vector for classifier. In experimental result with SVM classifier, PCA is better for beat classification while polynomial approximation can give the higher accuracy for case classification.

書名頁 i
論文口試委員審定書 ii
授權書 iv
中文摘要 v
英文摘要 vi
誌謝 viii
Table of Contents ix
List of Tables xi
List of Figures xii
1. Introduction 1
1.1. Atrial Fibrillation (AF) 4
1.2. Myocardial Infarction (MI) 5
1.3. Performance Measurement 7
2. Related Research 8
2.1. Wavelet Transformation (WT) to Analysis ECG Characteristics 8
2.2. Morphological Transform to Extract P-wave in ECG Complex 9
2.3. ECG Feature Extraction by Principle Component Analysis (PCA) 9
2.4. ST Shape Change Classification by Polynomial Approximation 10
2.5. Independent Component Analysis (ICA) for 12-Lead ECG Sources Separation 11
2.6. ECG Complex Segmentation by Hidden Markov Models (HMMs) 12
2.7. Empirical Mode Decomposition (EMD) for Atrial Fibrillation Classification 13
2.8. Non-Linear Classifier for ECG Analysis 15
3. Methodology 22
3.1. Atrial Fibrillation Classification in 12-Lead ECG 22
3.1.1. Independent Component Analysis (ICA) 23
3.1.2. Second-Order Blind Identification (SOBI) 26
3.1.3. Power Spectral Density (PSD) Based Classification 27
3.1.4. Ensemble Empirical Mode Decomposition (EEMD) 29
3.2. Myocardial Infarction Classification in 12-Lead ECG with Whole Complex 32
3.2.1. ECG Beat Sampling 33
3.2.2. Feature Extraction: Using HMMs for Each Lead V1 to V4 34
3.2.3. Data Classification 37
3.3. Myocardial Infarction Classification in 12-Lead ECG with ST-segment 39
3.3.1. ECG signal pre-processing 40
3.3.1.1 QRS-wave location 40
3.3.1.2 ST-Segmentation 41
3.3.2. Feature Extraction Stage 42
3.3.2.1 Principal Component Analysis 42
3.3.2.2 Polynomial Approximation 43
4. Experimental Results 45
4.1. Performance Measurement for AF Classification 45
4.2. Performance Measurement for MI Classification with Whole ECG by HMM 46
4.3. Performance Measurement for MI Classification with Principal Component Analysis and Polynomial Approximation 47
5. Discussion 50
6. Conclusion 54
Reference 55
Vita 62

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