Name
Abstract | ||
|---|---|---|
Background: Despite recent significant progress in the development of automatic sleep staging methods, building a good model still remains a big challenge for sleep studies with a small cohort due to the data-variability and data-inefficiency issues. This work presents a deep transfer learning approach to overcome these issues and enable transferring knowledge from a large dataset to a small cohor... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/TBME.2020.3020381 | IEEE Transactions on Biomedical Engineering |
Keywords | DocType | Volume |
Sleep,Databases,Data models,Electroencephalography,Machine learning,Performance evaluation,Electrooculography | Journal | 68 |
Issue | ISSN | Citations |
6 | 0018-9294 | 7 |
PageRank | References | Authors |
0.70 | 42 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Huy Phan | 1 | 133 | 20.88 |
| Oliver Y. Chén | 2 | 59 | 7.29 |
| Koch Philipp | 3 | 7 | 0.70 |
| Zongqing Lu | 4 | 209 | 26.18 |
| Ian Vince McLoughlin | 5 | 233 | 38.08 |
| Alfred Mertins | 6 | 534 | 76.48 |
| Maarten De Vos | 7 | 282 | 33.07 |