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Sleep Stage Detection On Sleep Edf

Metrics

Accuracy

Cohen's kappa

Macro-F1

Results

Performance results of various models on this benchmark

				Paper Title	Repository
SleePyCo (Fpz-Cz only)	86.8%	0.820	0.812	SleePyCo: Automatic Sleep Scoring with Feature Pyramid and Contrastive Learning
CatBoost	86.6%	0.816	0.810	Do Not Sleep on Traditional Machine Learning: Simple and Interpretable Techniques Are Competitive to Deep Learning for Sleep Scoring
XSleepNet (EEG, EOG)	86.4%	0.813	0.809	XSleepNet: Multi-View Sequential Model for Automatic Sleep Staging
Linear model	86.3%	0.813	0.805	Do Not Sleep on Traditional Machine Learning: Simple and Interpretable Techniques Are Competitive to Deep Learning for Sleep Scoring
IITNet CRNN (Fpz-Cz only)	84.0%	-	-	Intra- and Inter-epoch Temporal Context Network (IITNet) Using Sub-epoch Features for Automatic Sleep Scoring on Raw Single-channel EEG
DeepSleepNet	82%	0.76	0.769	DeepSleepNet: a Model for Automatic Sleep Stage Scoring based on Raw Single-Channel EEG
Multitask 1-max CNN	81.9%	-	-	Joint Classification and Prediction CNN Framework for Automatic Sleep Stage Classification
Deep CNN with transfer-learning	81.3%	-	-	Deep Convolutional Neural Networks for Interpretable Analysis of EEG Sleep Stage Scoring	-

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Sleep Stage Detection On Sleep Edf | SOTA | HyperAI