Title | ||
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Automated arrhythmia detection with homeomorphically irreducible tree technique using more than 10,000 individual subject ECG records |
Abstract | ||
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Background and objective: Arrhythmia constitute a common clinical problem in cardiology. The diagnosis is often made using electrocardiographic (ECG) signals but manual ECG interpretation by experts is expensive and time-consuming. In this work, we developed and validated an arrhythmia classification model based on handcrafted features, which was more computationally efficient than traditional deep learning models.Material and method: The classification model comprised (i) a specific feature extraction function based on the homeomorphically irreducible tree (HIT) graph pattern, (ii) multi-level feature generation based on maximum absolute pooling, (iii) Chi2 feature selector, and (iv) standard support vector machine classifier. We trained and validated the model on a large dataset comprising 12-leads ECGs acquired from more than 10,000 subjects. Performance metrics were reported for seven- (Case 1) and four-class (Case 2) arrhythmia diagnosis.Results: High classification accuracy rates of 92.95% and 97.18% were attained for Case 1 and Case 2, respectively, that were comparable with those of deep learning on the same ECG dataset.Conclusion: The model achieved excellent classification results at low computational cost, which underscores the potential for real world application of the proposed HIT-based ECG classification model. (C) 2021 Elsevier Inc. All rights reserved. |
Year | DOI | Venue |
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2021 | 10.1016/j.ins.2021.06.022 | Information Sciences |
Keywords | DocType | Volume |
Homeomorphically irreducible tree pattern,Maximum absolute pooling,Chi2 feature selection,Automated arrhythmia detection,ECG | Journal | 575 |
ISSN | Citations | PageRank |
0020-0255 | 1 | 0.35 |
References | Authors | |
0 | 5 |
Name | Order | Citations | PageRank |
---|---|---|---|
Mehmet Baygin | 1 | 2 | 2.41 |
Turker Tuncer | 2 | 49 | 12.16 |
Sengul Dogan | 3 | 39 | 10.96 |
Ru-San Tan | 4 | 239 | 22.37 |
Rajendra Acharya U | 5 | 4666 | 296.34 |