Sleep Stage Detection On Sleep Edfx
Métriques
Accuracy
Macro-F1
Résultats
Résultats de performance de divers modèles sur ce benchmark
Nom du modèle | Accuracy | Macro-F1 | Paper Title | Repository |
|---|---|---|---|---|
| NeuroNet (Fpz-Cz only) | 85.24% | 0.798 | NeuroNet: A Novel Hybrid Self-Supervised Learning Framework for Sleep Stage Classification Using Single-Channel EEG | - |
| XSleepNet (EEG, EOG) | 84.0% | 0.787 | XSleepNet: Multi-View Sequential Model for Automatic Sleep Staging | |
| SleePyCo (Fpz-Cz only) | 84.6% | 0.790 | SleePyCo: Automatic Sleep Scoring with Feature Pyramid and Contrastive Learning |
0 of 3 row(s) selected.