SOTAVerified

Sleep Stage Detection

Human Sleep Staging into W-N1-N2-N3-REM classes from multiple or single polysomnography signals

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1–10 of 34 papers

TitleStatusHype
Quasi Zigzag Persistence: A Topological Framework for Analyzing Time-Varying DataCode0
Toward Foundational Model for Sleep Analysis Using a Multimodal Hybrid Self-Supervised Learning FrameworkCode1
MC2SleepNet: Multi-modal Cross-masking with Contrastive Learning for Sleep Stage ClassificationCode1
HeartBERT: A Self-Supervised ECG Embedding Model for Efficient and Effective Medical Signal AnalysisCode1
Multi-Task Learning for Arousal and Sleep Stage Detection Using Fully Convolutional Networks—0
NeuroNet: A Novel Hybrid Self-Supervised Learning Framework for Sleep Stage Classification Using Single-Channel EEGCode2
Structure-Preserving Transformers for Sequences of SPD MatricesCode1
CoRe-Sleep: A Multimodal Fusion Framework for Time Series Robust to Imperfect Modalities—0
Self-Supervised PPG Representation Learning Shows High Inter-Subject VariabilityCode1
SleePyCo: Automatic Sleep Scoring with Feature Pyramid and Contrastive LearningCode1
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All datasetsSHHSSleep-EDFMASS SS3SHHS (single-channel)DODOSleep-EDFxSleep-EDFx (single-channel)DODHISRUC-SleepMASS (single-channel)MASS SS2Montreal Archive of Sleep Studies

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1SimpleSleepNetAccuracy89.9—Unverified
2DeepSleepNetAccuracy89.6—Unverified