Sleep Stage Classification on Sleep-EDF ST
83MF1Our Pretrained Model
Evaluation results
| Method | Links | |||
|---|---|---|---|---|
| Our Pretrained Modelmodel_type=Pretrained2026.02 | 83 | — | — | |
| RobustSleepNetYear=2021, Technique=RNN, LP=FT, Signals=EEG + EOG2022.07 | 81 | — | — | |
| Classical ML PipelineYear=2022, Technique=Catboost, LP=LFS, Signals=EEG + EOG + EMG2022.07 | 79.5 | 0.836 | 0.765 | |
| Classical ML PipelineYear=2022, Technique=Logistic regr., LP=LFS, Signals=EEG + EOG + EMG2022.07 | 79.2 | 0.829 | 0.759 | |
| RobustSleepNetYear=2021, Technique=RNN, LP=DT, Signals=EEG + EOG2022.07 | 79.1 | — | — | |
| Classical ML PipelineYear=2022, Technique=Catboost, LP=LFS, Signals=EEG + EOG2022.07 | 78.9 | 0.832 | 0.758 | |
| Classical ML PipelineYear=2022, Technique=Logistic regr., LP=LFS, Signals=EEG + EOG2022.07 | 78.8 | 0.825 | 0.754 | |
| RobustSleepNetYear=2021, Technique=RNN, LP=LFS, Signals=EEG + EOG2022.07 | 78.6 | — | — | |
| DeepSleepNet+Year=2020, Technique=CNN, LP=FT, Signals=EEG2022.07 | 77.5 | 0.815 | 0.738 | |
| SeqSleepNet+Year=2020, Technique=RNN, LP=FT, Signals=EEG2022.07 | 77.5 | 0.81 | 0.734 | |
| Perslev et al.2026.02 | 76 | — | — |