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An Enhanced QRS Detection Algorithm in the Multiple Leads Environments

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There have been many algorithms to remove noise and detect QRS complexes of Electrocardiograph (ECG) signal. However all process of these works are done at just one lead. Through the influence of medical necessity, ECG analysis uses 10 lead sensor to observe 12 leads which show different angles of heart. Thus, we make an enhanced QRS detection algorithm in the multiple leads environment. In this environment, when some leads show noisy result, there could be a fine lead to detect QRS complexes. From this fact, we split signals with few seconds and discriminate the cleanest lead in each interval. After that working, we use the information about R positions from the finest lead at noisy signals to find accurate QRS complexes. This algorithm could gather information that each lead gives.
There have been many algorithms to remove noise and detect QRS complexes of Electrocardiograph (ECG) signal. However all process of these works are done at just one lead. Through the influence of medical necessity, ECG analysis uses 10 lead sensor to observe 12 leads which show different angles of heart. Thus, we make an enhanced QRS detection algorithm in the multiple leads environment. In this environment, when some leads show noisy result, there could be a fine lead to detect QRS complexes. From this fact, we split signals with few seconds and discriminate the cleanest lead in each interval. After that working, we use the information about R positions from the finest lead at noisy signals to find accurate QRS complexes. This algorithm could gather information that each lead gives.
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Contents
Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . i
Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii
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Contents
Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . i
Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii
List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iv
List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v
1 Introduction 1
1.1 The Electrocariogram (ECG) . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Algorithms for QRS Complexes Detection . . . . . . . . . . . . . . . . . . . . 2
2 Algorithm Overview 4
3 Methodology 7
3.1 Partitioning the ECG signal . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
3.2 Filtering the noisy components . . . . . . . . . . . . . . . . . . . . . . . . . . 11
3.2.1 Three Linear Filtering . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
3.2.2 DC Removing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
3.3 Finding R . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
3.3.1 Making R Candidates . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
3.3.2 Discriminating Cleaner Lead . . . . . . . . . . . . . . . . . . . . . . . 17
3.3.3 Catching Exact R At Each Lead . . . . . . . . . . . . . . . . . . . . . 18
3.4 Finding Q and S . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
4 Experiments 20
5 Conclusion 24
Summary (in Korean) 25
References 26