Name
Abstract | ||
|---|---|---|
ECG classification is a key technology in intelligent electrocardiogram (ECG) monitoring. In the past, traditional machine learning methods such as support vector machine (SVM) and K-nearest neighbor (KNN) have been used for ECG classification, but with limited classification accuracy. Recently, the end-to-end neural network has been used for ECG classification and shows high classification accura... |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/JBHI.2021.3090421 | IEEE Journal of Biomedical and Health Informatics |
Keywords | DocType | Volume |
Electrocardiography,Neural networks,Feature extraction,Heart beat,Monitoring,Real-time systems,Power demand | Journal | 26 |
Issue | ISSN | Citations |
1 | 2168-2194 | 0 |
PageRank | References | Authors |
0.34 | 0 | 11 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jianbiao Xiao | 1 | 17 | 2.02 |
| Qingsong Liu | 2 | 4 | 2.44 |
| Huanqi Yang | 3 | 0 | 0.34 |
| jiahao liu | 4 | 23 | 11.31 |
| Ning Wang | 5 | 0 | 0.34 |
| Zhen Zhu | 6 | 8 | 1.51 |
| Yulong Chen | 7 | 0 | 0.34 |
| Yu Long | 8 | 0 | 0.34 |
| Liang Chang | 9 | 22 | 3.80 |
| Liang Zhou | 10 | 0 | 0.34 |
| Jun Zhou | 11 | 0 | 0.34 |