臺灣博碩士論文加值系統

長久以來心律不整檢測和分類都是心電圖訊號處理的主要議題。在此研究中，我們開發出一種針對多類型心律不整的辨識系統，包含心房顫動、心房撲動、以及具有生命危險的心室過速、心室撲動與心室顫動。此心電圖訊號辨識系統可分為不同的特徵提取技術與分類器的應用。在Alpha穩定分佈中的高斯分佈與心律不整有相似的性質，像是尖銳的峰值與脈衝特性，相較於一般的訊號分佈有較好的預期結果。此外帶有心律不整的心電圖訊號在時域上看起來混亂且難以解讀，利用機率密度函數與累加分佈函數進行統計分析，將會是一個更具有潛力的辨識特徵方法，因此本論文提出一種基於Alpha穩定分佈估計心律不整訊號參數的新方法。由於心律不整具有多種不同的形式，我們基於多種機器學習演算法中的分類器來驗證此方法的可行性。本論文提出的系統架構能夠分類不同的心律不整疾病，並證明使用Alpha穩定分佈足以區分心電圖訊號的主要特徵。

Electrocardiogram (ECG) has been used as the media for detecting and recognizing cardiac arrhythmias. In this research, we develop a recognition system for a variety of arrhythmias such as atrial fibrillation (AFIB), atrial flutter (AFL), and life threatening ventricular tachycardia (VT), ventricular flutter (VFL), and ventricular fibrillation (VFIB). This ECG recognition system is developed based on several feature extraction techniques followed by neural network classifiers. Alpha-stable distribution, with a general form of Gaussian distribution, shares the same properties with various arrhythmias such as sharp spikes or occasional impulses of outlying observations than one would expect from normally distributed signals. Since some electrocardiograms with arrhythmias are chaotic and difficult to be modeled in the time domain, interpreting the syndromes with their statistical information would be an alternative for exploring underlying characteristics. In addressing this problem, a novel method of accurate parameter estimation of cardiac signal is proposed. In addition, based on various arrhythmia types, various classifiers with deep learning algorithms are designed to verify the usefulness of our approaches. The developed system has shown the potential for alpha stable distribution to distinguish the main features of the ECG signal and thereby enhance the classification scheme.

摘要
Abstract
目錄
圖片索引
表索引
專有名詞縮寫對照表
第一章 導論
1.1 前言
1.2 心電圖訊號處理相關文獻探討
1.3 本文架構
第二章 心電圖與心律不整資料簡介
2.1 心電圖
2.1.1 心臟的電性傳導系統
2.1.2 心電圖介紹
2.2 心律不整疾病介紹
2.3 機器學習
2.3.1 支持向量機
2.3.2 最近鄰居法
2.4 深度神經網路
2.4.1 多層感知器
2.4.2 卷積神經網路
2.4.3 遞歸神經網絡
2.4.4 堆疊自編碼器
第三章 Alpha穩定分佈
3.1 Alpha穩定分佈
3.1.1 Alpha穩定分佈參數
3.1.2 Alpha穩定分佈特性
3.2 Alpha穩定分佈參數估計
3.2.1 基於經驗特徵函數法
3.2.2 基於樣本分位數法
3.2.3 參數估計演算法精確度比較
3.3 機率密度函數
3.4 累加分佈函數
第四章 實驗結果
4.1 資料來源介紹
4.2 ECG訊號預處理
4.2.1 高通濾波器
4.2.2 均值濾波器
4.3 ECG訊號分段擷取
4.3.1 基於N秒資料分段方法
4.3.2 基於R波偵測資料分段方法
4.4 特徵資料提取
4.4.1 Alpha穩定分佈參數特徵
4.4.2 機率密度函數與累積分佈函數
4.4.3 離散小波轉換
4.5 K-Fold交叉驗證
4.6 ECG資料分類結果
4.6.1 SVM演算法分類結果
4.6.2 KNN演算法分類結果
4.6.3 深度學習演算法分類結果
4.7 分類器辨識率比較
4.8 敏感度與變異度的性能評估
第五章 結論與未來展望
5.1 結論
5.2 未來展望
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