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Sleep Stage Detection On Shhs

Metrics

Accuracy

Cohen's Kappa

Macro-F1

Results

Performance results of various models on this benchmark

				Paper Title	Repository
SynthSleepNet (EEG2+EOG2+EMG1)	89.89%	0.860	0.845	Toward Foundational Model for Sleep Analysis Using a Multimodal Hybrid Self-Supervised Learning Framework
CoRe-Sleep (EEG-EOG)	89.5%	0.853	0.823	CoRe-Sleep: A Multimodal Fusion Framework for Time Series Robust to Imperfect Modalities	-
SynthSleepNet (EEG1+EOG1+EMG1)	89.28%	0.850	0.835	Toward Foundational Model for Sleep Analysis Using a Multimodal Hybrid Self-Supervised Learning Framework
XSleepNet2 (EEG, EOG, EMG)	89.1%	0.847	0.823	-	-
MC2SleepNet 50% Masking (C4-A1 only)	88.6%	0.841	0.821	MC2SleepNet: Multi-modal Cross-masking with Contrastive Learning for Sleep Stage Classification
MC2SleepNet 15% Masking (C4-A1 only)	88.5%	0.840	0.823	MC2SleepNet: Multi-modal Cross-masking with Contrastive Learning for Sleep Stage Classification
SynthSleepNet (EEG1+EOG1)	88.31%	0.840	0.820	Toward Foundational Model for Sleep Analysis Using a Multimodal Hybrid Self-Supervised Learning Framework
CoRe-Sleep (EEG)	88.2%	0.834	0.808	-	-
SleePyCo (C4-A1 only)	87.9%	0.830	0.807	-	-
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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Sleep Stage Detection On Shhs | SOTA | HyperAI