以圖形辨識方法做心電圖訊號分析｜國立清華大學博碩士論文庫

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研究生：	吳鳳甄 Wu, Feng-Jen
論文名稱：	以圖形辨識方法做心電圖訊號分析 Pattern Recognition for ECG Signal Analysis
指導教授：	陳朝欽 Chen, Chaur-Chin
口試委員:
學位類別：	碩士 Master
系所名稱：	電機資訊學院 - 資訊系統與應用研究所 Institute of Information Systems and Applications
論文出版年：	2009
畢業學年度：	97
語文別：	英文
論文頁數：	41
中文關鍵詞：	心電圖、辨識、偵測
外文關鍵詞：	ECG, pattern recognition, detection
相關次數：	點閱：2 下載：0
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心臟疾病是近幾年來台灣十大死亡原因的前三名，由於心臟疾病所引發的心血管疾病是無法預期的，因此發展一套偵測心臟疾病的系統是很重要的
在本篇論文，我們實作一套心電圖訊號分類系統，可以分成這些步驟:訊號前處理、小波轉換以及訊號分類。在第一個步驟中，我們使用中值濾波器來減少雜訊以及解決訊號飄移的問題，接著使用小波轉換來偵測QRS波，然後對每一個QRS波擷取特徵，最後使用nearest neighbor method將偵測到的QRS分類，並且評估分類結果。在這個實驗中，我們將心跳分為六大類: normal beat (N)、left bundle branch block beat (L)、right bundle branch block beat (R)、premature ventricular contraction (V)、atrial premature beat (A)、ventricular escape wave (E)以及 ventricular flatter wave (I)。
我們將這個心電圖訊號分類系統測試在MIT-BIH Arrhythmia Database和 European ST-T Database這二個心電圖資料庫。實驗結果可以分成二個主要部分QRS波偵測以及訊號分類。在QRS波偵測中，可以有效的偵測到QRS波，其準確率高達99%。而在訊號分類系統中，其辨識率此二個心電圖資料庫分別達到97%以及99%。

Heart disease is the third of the top ten causes of deaths in Taiwan these years. The heart disease cause of the cardiovascular disease is unexpected hence developing a real-time heart disease detector system is important.
In this thesis we implement an ECG signal classification system with these stages: signal pre-processing, wavelet transform, and signal classification. In the first stage, median filter is used to reduce the noise and baseline wander on the ECG signal. Then we detect the QRS complex and extract the features of QRS complex by wavelet transform. Finally, we use nearest neighbor method to classify each pattern of QRS complex. A QRS complex is classified into one of the seven types: normal beat (N), left bundle branch block beat (L), right bundle branch block beat (R), premature ventricular contraction (V), atrial premature beat (A), ventricular escape wave (E), and ventricular flatter wave (I).
The QRS complex classification is tested on two databases: MIT-BIH Arrhythmia Database and the European ST-T Database. The experimental results can be divided into two parts: QRS complex detection and ECG signal classification. In QRS complex detection, the accurate detection rates are above 99% in these two databases. The recognition rates are about 97% and 99% in the MIT-BIH Arrhythmia Database and the European ST-T Database, respectively.

Chapter 1 Introduction....................................................................................1
Chapter 2 Architecture of the Classification System......................................3
Chapter 3 QRS Complex Detection................................................................7
3.1 Signal Pre-processing.......................................................................7
3.2 Wavelet Transform...........................................................................8
3.2.1 5/3 Wavelet Transform..........................................................9
3.2.2 9/7 Wavelet Transform..........................................................12
3.3 R-peak Detection.............................................................................15
Chapter 4 QRS Complex Classification........................................................18
4.1 Feature Extraction............................................................................18
4.2 Signal Classification.........................................................................19
4.2.1 A Training Phase....................................................................19
4.2.2 A Test Phase............................................................................22
Chapter 5 ECG Signal Database for Experiments...........................................23
5.1 The MIT-BIH Arrhythmia Database..................................................24
5.2 The European ST-T Database............................................................25
Chapter 6 Experimental Results......................................................................26
6.1 Experiments on MIT-BIH Arrhythmia Database..............................26
6.1.1 Experimental Results of QRS Complex Detection................26
6.1.2 Experimental Results of QRS complex Classification...........30
6.2 Experiments on European ST-T database..........................................33
6.2.1 Experimental Results of QRS Complex Detection.................33
6.2.2 Experimental Results of QRS complex Classification............35
Chapter 7 Conclusion and Future Work...........................................................37
References........................................................................................................38

                                

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[web01] http://ecg.mit.edu/
[web02] http://library.med.utah.edu/kw/ecg/
[web03] http://www.physionet.org/
[web04] http://www.jonbarron.org/heart-health-program/07-02-2007.php
[web05] http://www.americanheart.org/
[web06] http://en.wikipedia.org/wiki/Electrocardiogram

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