臺灣博碩士論文加值系統

本論文提出了一個基於心電圖的心室頻脈(Ventricular Tachycardia, VT)及心室顫動(Ventricular Fibrillation, VF)的辨識方法並探討運用於心因性猝死(Sudden Cardiac Death, SCD)預測的研究。VT、VF的辨識與SCD的預測分別使用5秒及60秒的心電圖(ECG)作為訊號來源，其中SCD預測是以VF發病前固定區段的時間進行研究及分析。
本研究主要分為兩部分，第一部分為VT、VF之心律不整辨識，訊號來源為MIT-BIH Malignant Ventricular Arrhythmia Database (VFDB)，第二部分為SCD預測，訊號來源為MIT-BIH Sudden Cardiac Death Holter Database (SDDB)。在系統架構上可分為前處理、特徵擷取、特徵正規化、特徵挑選與分類。首先，先將原始訊號經過前處理去除不必要的雜訊，然後進行特徵擷取及特徵正規化來取得可用以分類的特徵，接著探討傳統基因演算法(GA)、改良型基因演算法(MGA)、多目標基因演算法(NSGA-II)及P值法(P-value)進行特徵挑選，以降低特徵維度時，提升辨識及預測正確率的效能。論文中並探討如何讓挑選出的特徵同時適用於訓練集與測試集的方法，其中分類使用支持向量機(SVM)，並藉由五折(five-fold)的方式來進行交叉驗證。
結果顯示，第一部分嚴重心律不整辨識在訓練集得到的最佳靈敏度、特異度及正確率分別為93.05%、96.84%及95.6%。而在測試集則為92.55%、96.43%及95.52%。第二部分心因性猝死預測，於發病前第10分鐘的訊號在訓練集得到的最佳靈敏度(Sensitivity, Se)、特異度(Specificity, Sp)及正確率(Accuracy, Ac)則分別為76.25%、82.5%及79.38%。而在測試集則為72.5%、82.5%及77.5%。
本系統對於VT、VF的辨識以及SCD預測上皆有不錯的正確率，研究成果可應用在醫療救護上，作為醫護人員掌握病患病情的一項重要輔助。

This thesis proposes a Ventricular Tachycardia (VT) and Ventricular Fibrillation (VF) identification method and its application to Sudden Cardiac Death (SCD) prediction based on electrocardiogram (ECG). These two systems used ECG of 5 seconds and 60 seconds to separately identify VT, VF and predict SCD. The research of SCD prediction were based on signals in specific period of time before the onset of VF.
This study is divided into two parts. The first part is arrhythmia (VT and VF) identification. The ECG signals used in the experiment were obtained from the MIT-BIH Malignant Ventricular Arrhythmia Database (VFDB). The second part is SCD prediction. The ECG signals were obtained from the MIT-BIH Sudden Cardiac Death Holter Database (SDDB). The system architecture was divided into pre-processing, feature extraction, feature normalization, feature selection and classification. Firstly, the original signals were preprocessed to remove unnecessary noise. Secondly, feature extraction and feature normalization were used to obtain the features for classification. Thirdly, traditional genetic algorithm (GA), modified genetic algorithm (MGA), multi-objective genetic algorithm (NSGA-II) and P value were used for feature selection to reduce the feature dimensions. Their performance in improving the identification and prediction accuracy rate was compared. Strategies for selecting features that are suitable for using both in the training set and test set were discussed. Finally, support vector machine (SVM) was used to classify these heart rhythms, and the five-fold scheme was used for cross-validation.
In the first part, a sensitivity (Se) of 93.05%, a specificity (Sp) of 96.84%, and an accuracy (Ac) of 95.6% were obtained on the training dataset. And, an Se of 92.55%, an Sp of 96.43%, and an Ac of 95.52% were obtained on the test dataset.
In the second part, this study can predict the SCD ten minutes before its onset with an Se of 76.25%, an Sp of 82.5%, and an Ac of 79.38% on the training dataset, and an Se of 72.5%, an Sp of 82.5%, and an Ac of 77.5% on the test dataset. According to the results, the systems for arrhythmia identification and SCD prediction proposed in this study imposing accuracy rates, and demonstrate their capability to be used in medical care to assist health care workers handle their patient’s condition.

致謝辭
摘要
Abstract
目錄
圖目錄
表目錄
第一章 緒論
1.1研究動機
1.2相關研究
1.3研究目的
1.4論文架構
第二章 研究背景
2.1.心電圖原理
2.1.1心臟與心搏
2.1.2心電圖波型
2.1.3心電圖導極
2.2心律不整
2.3心因性猝死
2.4小波轉換
2.5支持向量機
第三章 研究方法
3.1實驗架構
3.2資料庫介紹
3.3訊號前處理
3.4特徵擷取
3.5特徵正規化
3.6特徵選取
3.6.1基因演算法
3.6.2改良型基因演算法
3.6.3適應值計算
3.6.4多目標基因演算法
3.6.5 P值法
3.7交叉驗證
第四章 研究結果與討論
4.1嚴重心律不整辨識結果與討論
4.2心因性猝死預測結果與討論
4.3研究結果評估與分析
4.4相關文獻比較
第五章 結論與未來展望
5.1結論
5.2未來展望
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