Sleep Stage Detection On Shhs
Métriques
Accuracy
Cohen's Kappa
Macro-F1
Résultats
Résultats de performance de divers modèles sur ce benchmark
Tableau comparatif
| Nom du modèle | Accuracy | Cohen's Kappa | Macro-F1 |
|---|---|---|---|
| mc2sleepnet-multi-modal-cross-masking-with | 88.6% | 0.841 | 0.821 |
| toward-foundational-model-for-sleep-analysis | 89.28% | 0.850 | 0.835 |
| neuronet-a-novel-hybrid-self-supervised | 86.88% | - | 0.812 |
| Modèle 4 | 88.2% | 0.834 | 0.808 |
| Modèle 5 | 87.9% | 0.830 | 0.807 |
| Modèle 6 | 89.1% | 0.847 | 0.823 |
| toward-foundational-model-for-sleep-analysis | 89.89% | 0.860 | 0.845 |
| toward-foundational-model-for-sleep-analysis | 88.31% | 0.840 | 0.820 |
| core-sleep-a-multimodal-fusion-framework-for | 89.5% | 0.853 | 0.823 |
| mc2sleepnet-multi-modal-cross-masking-with | 88.5% | 0.840 | 0.823 |