心臟疾病在國際上是死亡的主要原因之一，而心電訊號的監測更為判斷是否有患病可能的重要前提。心電訊號中最明顯的波型是R波，因此R波的偵測與判讀是分析心電訊號的第一步驟。我們提出了時域多層級R波偵測演算法，此方法不僅複雜度低，也可以維持波形的完整性，對於後續的疾病偵測有極大的幫助。本研究對其中的係數做最佳化的處理，R波偵測演算法本質上也是個二元分類器，因此在最佳化係數的過程當中，將以分類器性能評估的圖形方法作為主要的工具。本研究利用PhysioNet所提供之MIT-BIH arrhythmia database (MIT-BIH) 資料庫所提供的心電訊號進行最佳化的過程。在演算法效能驗證時，也會用PhysioNet提供的 Apnea-ECG database中長達124小時的心電訊號作評估。最後也會和其他R波偵測演算法做比較，探討效能的差異及造成的原因。

Heart disease is one of the leading causes of death, and the monitoring of the ECG signal is an essential prerequisite for judging whether there is a possibility of heart disease. The most apparent waveform in the ECG signal is the R wave, so the detection and interpretation of the R wave is the first step in analyzing the ECG signal. We proposed a time-domain multi-level R-peak detection algorithm for ECG signal processing. This method not only has low complexity but also maintains the shape of the waveform. The preservation of the ECG waveform is crucial to the following disease detection. This research optimizes the coefficients of the proposed algorithm. The R-peak detection algorithm is essentially a binary classifier. Therefore, in the process of optimizing the coefficients, the graphical method of classifier performance evaluation is used as the primary tool. This research uses the ECG signal from the MIT-BIH arrhythmia database (MIT-BIH) provided by PhysioNet for the optimization process. In the algorithm performance verification, 124 hours of ECG signals from the Apnea-ECG database provided by PhysioNet will also be used as well. Finally, the proposed algorithm will be compared with other R-wave detection algorithms to discuss the difference in performance and the reasons behind it.

致謝 I
中文摘要 II
Abstract III
Contents IV
List of Figures VI
List of Tables VIII
Chapter 1 Introduction 1
1.1 Research Motivation and Purposes 1
1.2 Research Method 2
1.3 Thesis Architecture 2
Chapter 2 General Background Information 3
2.1 Introduction to Electrocardiography 3
2.1.1 Fundamental of Electrocardiography 3
2.1.2 Measurement of ECG 4
2.1.3 Overview of ECG Waveform 7
2.1.4 Abnormal Waveform in ECG 8
2.2 Introduction to Graphical Methods for Classifier Performance Evaluation 11
2.2.1 Precision-Recall Curve 11
2.2.2 Receiver Operating Characteristic 14
2.2.3 Comparison of PRC and ROC 16
2.3 Introduction to PhysioNet and MIT-BIH Database 17
2.3.1 PhysioNet 17
2.3.2 MIT-BIH Arrhythmia Database 18
2.3.3 Open Source Analyzing Tools of PhysioNet 18
2.4 Related Works 18
2.4.1 Pan–Tompkins algorithm 19
2.4.2 QRS Detection Based on an Advanced Multilevel Algorithm 21
2.4.3 R-peak Detection Algorithm for ECG using Double Difference and RR Interval Processing 23
Chapter 3 Proposed Multi-Level QRS Wave Detection Algorithm 26
3.1 Level I: Roughly Filtering Stage 28
3.2 Level I Coefficients Optimization 29
3.2.1 Application of PRC 30
3.2.2 Complexity Reduction 34
3.3 Q and S Waves Detection 36
3.4 Level II: Higher-Standard Filtering Stage 38
3.5 Level II Coefficients Optimization 39
3.5.1 Application of ROC and PRC 40
3.5.2 Different Way of Choosing Optimize Value 46
3.6 Level III: Refinement Stage 48
3.6.1 R Peaks Removal 48
3.6.2 R Peaks Insertion 51
3.7 Level III Coefficients Optimization 53
3.7.1 Optimization of λ4 53
3.7.2 Optimization of λ2' and λ3' 59
Chapter 4 Results and Discussion 63
4.1 Simulation Results 63
4.1.1 The Results of MIT-BIH Arrhythmia Database 63
4.1.2 The Results of Apnea-ECG Database 68
4.2 Level Performance Evaluation 71
4.3 Comparison and Discussion 72
4.3.1 Compare with the Original Work 72
4.3.2 Compare with the Related Works 76
Chapter 5 Conclusion 86
References 87

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