Multimodal Sleep Stage Detection On Sleep Edf
Metriken
Accuracy
Cohen's kappa
Macro-F1
Ergebnisse
Leistungsergebnisse verschiedener Modelle zu diesem Benchmark
| Paper Title | ||||
|---|---|---|---|---|
| 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 |
| 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 |
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