Sleep Stage Detection On Shhs Single Channel
Metrics
Accuracy
Cohen's Kappa
Macro-F1
Results
Performance results of various models on this benchmark
Model Name | Accuracy | Cohen's Kappa | Macro-F1 | Paper Title | Repository |
|---|---|---|---|---|---|
| MC2SleepNet 15% Masking (C4-A1 only) | 88.5% | 0.840 | 0.823 | MC2SleepNet: Multi-modal Cross-masking with Contrastive Learning for Sleep Stage Classification | |
| SleePyCo (C4-A1 only) | 87.9% | 0.830 | 0.807 | SleePyCo: Automatic Sleep Scoring with Feature Pyramid and Contrastive Learning | |
| XSleepNet (C4-A1 only) | 87.7% | 0.828 | 0.801 | XSleepNet: Multi-View Sequential Model for Automatic Sleep Staging | |
| MC2SleepNet 50% Masking (C4-A1 only) | 88.6% | 0.841 | 0.821 | MC2SleepNet: Multi-modal Cross-masking with Contrastive Learning for Sleep Stage Classification | |
| NeuroNet (C4-A1 only) | 86.88% | - | 0.812 | NeuroNet: A Novel Hybrid Self-Supervised Learning Framework for Sleep Stage Classification Using Single-Channel EEG | - |
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