Name
Title | ||
|---|---|---|
A hybrid self-attention deep learning framework for multivariate sleep stage classification |
Abstract | ||
|---|---|---|
Sleep is a complex and dynamic biological process characterized by different sleep patterns. Comprehensive sleep monitoring and analysis using multivariate polysomnography (PSG) records has achieved significant efforts to prevent sleep-related disorders. To alleviate the time consumption caused by manual visual inspection of PSG, automatic multivariate sleep stage classification has become an important research topic in medical and bioinformatics. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1186/s12859-019-3075-z | BMC Bioinformatics |
Keywords | Field | DocType |
Attention mechanism, Deep learning, Sleep stage classification, Polysomnography, Multivariate time series | Visual inspection,Biology,Multivariate statistics,Sleep monitoring,Artificial intelligence,Deep learning,Bioinformatics,Machine learning,Polysomnography | Journal |
Volume | Issue | ISSN |
20 | 16 | 1471-2105 |
Citations | PageRank | References |
2 | 0.35 | 9 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ye Yuan | 1 | 16 | 2.63 |
| Kebin Jia | 2 | 22 | 4.05 |
| Fenglong Ma | 3 | 374 | 33.08 |
| Guangxu Xun | 4 | 19 | 4.65 |
| Yaqing Wang | 5 | 95 | 9.71 |
| lu su | 6 | 1118 | 66.61 |
| Aidong Zhang | 7 | 2970 | 405.63 |