Sleep Stage Detection on SHHS

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Accuracy

Rank	Model	Accuracy	Cohen's Kappa	Macro-F1	Paper	Code	Year
1	SynthSleepNet (EEG2+EOG2+EMG1)	89.89%	0.860	0.845	Toward Foundational Model for Sleep Analysis Using a Multimodal Hybrid Self-Supervised Learning Framework	dlcjfgmlnasa/SynthSleepNet	2025
2	CoRe-Sleep (EEG-EOG)	89.5%	0.853	0.823	CoRe-Sleep: A Multimodal Fusion Framework for Time Series Robust to Imperfect Modalities		2023
3	SynthSleepNet (EEG1+EOG1+EMG1)	89.28%	0.850	0.835	Toward Foundational Model for Sleep Analysis Using a Multimodal Hybrid Self-Supervised Learning Framework	dlcjfgmlnasa/SynthSleepNet	2025
4	XSleepNet2 (EEG, EOG, EMG)	89.1%	0.847	0.823
5	MC2SleepNet 50% Masking (C4-A1 only)	88.6%	0.841	0.821	MC2SleepNet: Multi-modal Cross-masking with Contrastive Learning for Sleep Stage Classification	younghoonNa/MC2SleepNet	2025
6	MC2SleepNet 15% Masking (C4-A1 only)	88.5%	0.840	0.823	MC2SleepNet: Multi-modal Cross-masking with Contrastive Learning for Sleep Stage Classification	younghoonNa/MC2SleepNet	2025
7	SynthSleepNet (EEG1+EOG1)	88.31%	0.840	0.820	Toward Foundational Model for Sleep Analysis Using a Multimodal Hybrid Self-Supervised Learning Framework	dlcjfgmlnasa/SynthSleepNet	2025
8	CoRe-Sleep (EEG)	88.2%	0.834	0.808
9	SleePyCo (C4-A1 only)	87.9%	0.830	0.807
10	NeuroNet (C4-A1 only)	86.88%	–	0.812	NeuroNet: A Novel Hybrid Self-Supervised Learning Framework for Sleep Stage Classification Using Single-Channel EEG	dlcjfgmlnasa/NeuroNet	2024
11	SynthSleepNet (EEG2+EOG2+EMG1)	89.89%	0.860	0.845	Toward Foundational Model for Sleep Analysis Using a Multimodal Hybrid Self-Supervised Learning Framework	dlcjfgmlnasa/SynthSleepNet	2025
12	CoRe-Sleep (EEG-EOG)	89.5%	0.853	0.823	CoRe-Sleep: A Multimodal Fusion Framework for Time Series Robust to Imperfect Modalities		2023
13	SynthSleepNet (EEG1+EOG1+EMG1)	89.28%	0.850	0.835	Toward Foundational Model for Sleep Analysis Using a Multimodal Hybrid Self-Supervised Learning Framework	dlcjfgmlnasa/SynthSleepNet	2025
14	XSleepNet2 (EEG, EOG, EMG)	89.1%	0.847	0.823
15	MC2SleepNet 50% Masking (C4-A1 only)	88.6%	0.841	0.821	MC2SleepNet: Multi-modal Cross-masking with Contrastive Learning for Sleep Stage Classification	younghoonNa/MC2SleepNet	2025
16	MC2SleepNet 15% Masking (C4-A1 only)	88.5%	0.840	0.823	MC2SleepNet: Multi-modal Cross-masking with Contrastive Learning for Sleep Stage Classification	younghoonNa/MC2SleepNet	2025
17	SynthSleepNet (EEG1+EOG1)	88.31%	0.840	0.820	Toward Foundational Model for Sleep Analysis Using a Multimodal Hybrid Self-Supervised Learning Framework	dlcjfgmlnasa/SynthSleepNet	2025
18	CoRe-Sleep (EEG)	88.2%	0.834	0.808
19	SleePyCo (C4-A1 only)	87.9%	0.830	0.807
20	NeuroNet (C4-A1 only)	86.88%	–	0.812	NeuroNet: A Novel Hybrid Self-Supervised Learning Framework for Sleep Stage Classification Using Single-Channel EEG	dlcjfgmlnasa/NeuroNet	2024