Name
Title | ||
|---|---|---|
Study on the use of standard 12-lead ECG data for rhythm-type ECG classification problems |
Abstract | ||
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Background and objectives: Most deep-learning-related methodologies for electrocardiogram (ECG) classification are focused on finding an optimal deep-learning architecture to improve classification performance. However, in this study, we proposed a methodology for fusion of various single-lead ECG data as training data in the single-lead ECG classification problem. Methods: We used a squeeze-and-excitation residual network (SE-ResNet) with 152 layers as the baseline model. We compared the performance of a 152-layer SE-ResNet trained on ECG signals from various leads of a standard 12-lead ECG system to that of a 152-layer SE-ResNet trained on only single-lead ECG data with the same lead information as the test set. The experiments were performed using five different types of rhythm-type single-lead ECG data obtained from Konkuk University Hospital in South Korea. Results: Experiment results based on the combination from the relationship experiments of the leads showed that lead-aVR or II revealed the best classification performance. In case of -aVR, this model achieved a high F1 score for normal (98.7%), AF (98.2%), APC (95.1%), and VPC (97.4%), indicating its potential for practical use in the medical field. Conclusion: We concluded that the 152-layer SE-ResNet trained by fusion of single-lead ECGs had better classification performance than the 152-layer SE-ResNet trained on only single-lead ECG data, regardless of the single-lead ECG signal type. We also found that the best performance directions for single-lead ECG classification are Lead-aVR and II. (C) 2021 The Author(s). Published by Elsevier B.V. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1016/j.cmpb.2021.106521 | COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE |
Keywords | DocType | Volume |
Single-lead ECG classification, Deep learning, Convolutional neural network, SE-ResNet, Heterogeneous single-lead ECG, Standard 12-lead ECG, 12 Single-lead ECG | Journal | 214 |
ISSN | Citations | PageRank |
0169-2607 | 1 | 0.38 |
References | Authors | |
0 | 8 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Junsang Park | 1 | 1 | 0.38 |
| Junho An | 2 | 1 | 0.38 |
| Jinkook Kim | 3 | 1 | 0.38 |
| Sunghoon Jung | 4 | 1 | 0.38 |
| Yeongjoon Gil | 5 | 1 | 0.38 |
| Yoojin Jang | 6 | 1 | 0.38 |
| Kwanglo Lee | 7 | 1 | 0.38 |
| Il-Young Oh | 8 | 1 | 0.38 |