Multimodal Sleep Stage Detection On Sleep Edf
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 |
|---|---|---|---|---|---|
| Linear model | 85.7% | 0.806 | 0.809 | Do Not Sleep on Traditional Machine Learning: Simple and Interpretable Techniques Are Competitive to Deep Learning for Sleep Scoring | |
| Scratch SeqSleepNet+ (EEG+EOG) | 82.2% | - | - | Towards More Accurate Automatic Sleep Staging via Deep Transfer Learning | |
| CatBoost | 86.4% | 0.812 | 0.802 | Do Not Sleep on Traditional Machine Learning: Simple and Interpretable Techniques Are Competitive to Deep Learning for Sleep Scoring |
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