Name
Title | ||
|---|---|---|
Towards Interpretable Arrhythmia Classification with Human-machine Collaborative Knowledge Representation. |
Abstract | ||
|---|---|---|
Arrhythmia detection and classification is a crucial step for diagnosing cardiovascular diseases. However, deep learning models that are commonly used and trained in end-to-end fashion are not able to provide good interpretability. In this paper, we address this deficiency by proposing the first novel interpretable arrhythmia classification approach based on a human-machine collaborative knowledge... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/TBME.2020.3024970 | IEEE Transactions on Biomedical Engineering |
Keywords | DocType | Volume |
Electrocardiography,Knowledge representation,Feature extraction,Man-machine systems,Collaboration,Machine learning,Neural networks | Journal | 68 |
Issue | ISSN | Citations |
7 | 0018-9294 | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jilong Wang | 1 | 57 | 19.88 |
| Rui Li | 2 | 0 | 0.34 |
| Renfa Li | 3 | 647 | 97.10 |
| Bin Fu | 4 | 0 | 0.34 |
| Chunxia Xiao | 5 | 6 | 2.54 |
| Danny Ziyi Chen | 6 | 0 | 0.34 |