在新一代的穿戴式動態心電圖監測系統研發上，受限於電池容量，裝置內的低運算複雜度訊號處理技術具有高度需求。例如，可運用在裝置端進行初級訊號判讀，便於在發現異常心律時才進行訊號儲存，以減少資料寫入時的電力消耗；或是在偵測到危險心律時傳輸心電訊號，由雲端進行更精確之判讀與後續處理，以避免持續的無線傳輸所造成的電力消耗。本文提出一種新的即時心跳偵測及其分類方法，兼具低運算複雜度與高準確率並可應用在裝置之中。首先，本方法透過訊號轉換，增強QRS區段並抑制P波與T波，同時也消除動態雜訊所產生的基線漂移；其次，QRS波的基準點就可以由轉換後訊號所偵測出之波峰與波谷之間的關係來決定；然後，再基於4種事先定義的QRS波模板，進行R點與其它特徵識別，同時也達成初級心跳分類；最後，偵測出之心跳可用識別出的特徵點及其相關決策規則來進行心跳分類。本文提出之方法最終採用MIT-BIH心律不整資料庫來驗證效能，其QRS偵測靈敏度與正確預測率分別為99.82%與99.81%，而VEB偵測靈敏度與正確預測率分別為89.34%與84.97%，QRS偵測與VEB分類都達到國際水準。此外，本文提出之方法已經在智慧手機與嵌入式系統上實現，並實際應用在醫院臨床資料的收集與測試上，也同時驗證了本文提出之方法的低運算複雜度與實際可用性。

In the new-generation wearable Electrocardiogram (ECG) devices, signal processing with low power consumption is required to transmit data when detecting dangerous rhythms and to record signals when detecting abnormal rhythms. This study proposes a real-time QRS detection and heartbeat classification method with low computational complexity while maintaining a high accuracy. The enhancement of QRS segments and restraining of P and T waves are carried out by the proposed ECG signal transformation, which also leads to the elimination of baseline wandering. In this study, the QRS fiducial point is determined based on the detected crests and troughs of the transformed signal. Subsequently, the R point can be recognized based on four QRS waveform templates and preliminary heartbeat classification can be also achieved at the same time. At last, the detected heartbeat can be classified using decision rules according to the detected features. The performance of the proposed approach is demonstrated using the benchmark of the MIT-BIH Arrhythmia Database, where the QRS sensitivity (Se) is 99.82%, positive prediction (+P) is 99.81%, VEB (ventricular ectopic beat) Se is 89.34%, VEB +P is 84.97%, respectively. The result reveals the approach’s advantage of low computational complexity, as well as the feasibility of the real-time application on a mobile phone and an embedded system.

ABSTRACT I
ABSTRATCT IN CHINESE II
ACKOWLEGEMENT III
CONTENTS IV
LIST OF TABLES VII
LIST OF FIGURES IX
SYMBOLS AND ABBREVIATIONS XIII
CHAPTER 1 INTRODUCTION 1
1.1 Motivation 1
1.2 Literature Review 3
1.3 Structure of this Dissertation 6
CHAPTER 2 ELECTROGARDIAGRAM BASICS 7
2.1 The Normal Electrocardiogram 7
2.2 New-Generation Wearable ECG Devices 12
2.3 MIT-BIH Arrhythmia Database 15
2.4 ANSI/AAMI EC57 17
2.5 Challenges in ECG Signal Processing 20
CHAPTER 3 MOTHODOLOGY 23
3.1 Method Overview 23
3.2 Refreshment of the ECG signal 24
3.3 Signal Transformation 24
3.4 The QRS Fiducial Point Detection 29
3.5 Features Recognition 33
3.5.1 S Point Detection 34
3.5.2 Ra Point Detection 35
3.5.3 Q point Detection 35
3.5.4 R Point Detection 36
3.5.5 T Point Detection 36
3.5.6 P Point Detection 37
3.6 Heartbeat Classification 37
CHAPTER 4 BENCHMARKING STUDY 42
4.1 QRS Detection and R Point Recognition Result 42
4.2 Heartbeat Classification Result 45
4.3 Benchmarking Study Using MIT-BIH Arrhythmia Database 48
CHAPTER 5 IMPLEMENTATION 54
5.1 Implementation for a Mobile Phone 54
5.2 Implementation in an Embedded System 55
5.3 A Study Case of Walking 57
5.4 VEB Detection in the Mobile ECG Application 60
CHAPTER 6 CONCLUSIONS 62
REFERENCES 65
APPENDIX 1 The latest three products of FDA cleared ECG Patch 71
APPENDIX 2 A bone chart of ECG relative research topics 73
PUBLICATIONS 74
Journal Paper: 74
Conference Papers: 74
Patents: 75

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