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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
SynthSleepNet (EEG2+EOG2+EMG1)89.89%0.8600.845Toward Foundational Model for Sleep Analysis Using a Multimodal Hybrid Self-Supervised Learning Framework
CoRe-Sleep (EEG-EOG)89.5%0.8530.823CoRe-Sleep: A Multimodal Fusion Framework for Time Series Robust to Imperfect Modalities
SynthSleepNet (EEG1+EOG1+EMG1)89.28%0.8500.835Toward Foundational Model for Sleep Analysis Using a Multimodal Hybrid Self-Supervised Learning Framework
XSleepNet2 (EEG, EOG, EMG)89.1%0.8470.823-
MC2SleepNet 50% Masking (C4-A1 only)88.6%0.8410.821MC2SleepNet: Multi-modal Cross-masking with Contrastive Learning for Sleep Stage Classification
MC2SleepNet 15% Masking (C4-A1 only)88.5%0.8400.823MC2SleepNet: Multi-modal Cross-masking with Contrastive Learning for Sleep Stage Classification
SynthSleepNet (EEG1+EOG1)88.31%0.8400.820Toward Foundational Model for Sleep Analysis Using a Multimodal Hybrid Self-Supervised Learning Framework
CoRe-Sleep (EEG)88.2%0.8340.808-
SleePyCo (C4-A1 only)87.9%0.8300.807-
NeuroNet (C4-A1 only)86.88%-0.812NeuroNet: A Novel Hybrid Self-Supervised Learning Framework for Sleep Stage Classification Using Single-Channel EEG
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