Name
Abstract | ||
|---|---|---|
ECG classification is important to the diagnosis of cardiovascular disease. This paper develops a robust and accurate algorithm for automatic detection of heart arrhythmias from ECG signals recorded with one lead. A novel model based on the convolutional neural network is proposed to extract low-level and high-level features of short term ECG. In addition, Information-Theoretic Metric Learning is utilized as a final classification model to boost the discrimination abilities of the network trained features. The experimental results over the MIT-BIH arrhythmia database show that the model achieves a comparable performance with most of the state-of-the-art methods and Information-Theoretic Metric Learning further improves the performance. Besides the good accuracy achieved, the proposed method balances different criteria. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1007/s11042-020-09035-w | MULTIMEDIA TOOLS AND APPLICATIONS |
Keywords | DocType | Volume |
Short term ECG classification,Feature representation,Residual-concatenate network,Information-Theoretic Metric Learning | Journal | 79.0 |
Issue | ISSN | Citations |
31-32 | 1380-7501 | 1 |
PageRank | References | Authors |
0.36 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xinjing Song | 1 | 1 | 0.36 |
| Gongping Yang | 2 | 414 | 42.17 |
| Kuikui Wang | 3 | 11 | 6.92 |
| Yuwen Huang | 4 | 5 | 5.83 |
| Yuan Feng | 5 | 17 | 5.55 |
| Yilong Yin | 6 | 966 | 135.80 |