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DeepSleepNet: a Model for Automatic Sleep Stage Scoring based on Raw Single-Channel EEG

arXiv 1703.04046 ↗First posted 12 Mar 2017stat.MLcited by 154

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Abstract (arXiv)

The present study proposes a deep learning model, named DeepSleepNet, for automatic sleep stage scoring based on raw single-channel EEG. Most of the existing methods rely on hand-engineered features which require prior knowledge of sleep analysis. Only a few of them encode the temporal information such as transition rules, which is important for identifying the next sleep stages, into the extracted features. In the proposed model, we utilize Convolutional Neural Networks to extract time-invariant features, and bidirectional-Long Short-Term Memory to learn transition rules among sleep stages automatically from EEG epochs. We implement a two-step training algorithm to train our model efficiently. We evaluated our model using different single-channel EEGs (F4-EOG(Left), Fpz-Cz and Pz-Oz) from two public sleep datasets, that have different properties (e.g., sampling rate) and scoring standards (AASM and R&K). The results showed that our model achieved similar overall accuracy and macro F1-score (MASS: 86.2%-81.7, Sleep-EDF: 82.0%-76.9) compared to the state-of-the-art methods (MASS: 85.9%-80.5, Sleep-EDF: 78.9%-73.7) on both datasets. This demonstrated that, without changing the model architecture and the training algorithm, our model could automatically learn features for sleep stage scoring from different raw single-channel EEGs from different datasets without utilizing any hand-engineered features.

Key facts. Cited by 154 papers in the map (data through 7 Oct 2026). First posted on arXiv 12 Mar 2017.

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Citing papers per year: 2021: 24; 2022: 27; 2023: 20; 2024: 19; 2025: 26; 2026: 38.

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  1. 2024CBraMod: A Criss-Cross Brain Foundation Model for EEG Decodingcited by 188
  2. 2021SleepTransformer: Automatic Sleep Staging with Interpretability and Uncertainty Quantificationcited by 83
  3. 2024NeuroLM: A Universal Multi-task Foundation Model for Bridging the Gap between Language and EEG Signalscited by 83
  4. 2021Automatic Sleep Staging of EEG Signals: Recent Development, Challenges, and Future Directionscited by 41
  5. 2022Self-supervised Contrastive Representation Learning for Semi-supervised Time-Series Classificationcited by 39
  6. 2024SleepFM: Multi-modal Representation Learning for Sleep Across Brain Activity, ECG and Respiratory Signalscited by 38
  7. 2021Multi-View Spatial-Temporal Graph Convolutional Networks with Domain Generalization for Sleep Stage Classificationcited by 37
  8. 2021SalientSleepNet: Multimodal Salient Wave Detection Network for Sleep Stagingcited by 31
  9. 2022Modeling Multivariate Biosignals With Graph Neural Networks and Structured State Space Modelscited by 29
  10. 2021Self-supervised Contrastive Learning for EEG-based Sleep Stagingcited by 27
  11. 2021DeepSleepNet-Lite: A Simplified Automatic Sleep Stage Scoring Model with Uncertainty Estimatescited by 26
  12. 2021RobustSleepNet: Transfer learning for automated sleep staging at scalecited by 24
38 more
  1. 2023Frequency-Aware Masked Autoencoders for Multimodal Pretraining on Biosignalscited by 22
  2. 2022COCOA: Cross Modality Contrastive Learning for Sensor Datacited by 21
  3. 2023L-SeqSleepNet: Whole-cycle Long Sequence Modelling for Automatic Sleep Stagingcited by 21
  4. 2023Finding Order in Chaos: A Novel Data Augmentation Method for Time Series in Contrastive Learningcited by 18
  5. 2024EEGMamba: Bidirectional State Space Model with Mixture of Experts for EEG Multi-task Classificationcited by 18
  6. 2021ADAST: Attentive Cross-domain EEG-based Sleep Staging Framework with Iterative Self-Trainingcited by 17
  7. 2023Label-efficient Time Series Representation Learning: A Reviewcited by 16
  8. 2025AdaBrain-Bench: Benchmarking Brain Foundation Models for Brain-Computer Interface Applicationscited by 16
  9. 2021Robust learning from corrupted EEG with dynamic spatial filteringcited by 14
  10. 2022Self-supervised Learning for Label-Efficient Sleep Stage Classification: A Comprehensive Evaluationcited by 14
  11. 2022Toward Interpretable Sleep Stage Classification Using Cross-Modal Transformerscited by 13
  12. 2024A Tale of Single-channel Electroencephalogram: Devices, Datasets, Signal Processing, Applications, and Future Directionscited by 13
  13. 2021A Compact and Interpretable Convolutional Neural Network for Cross-Subject Driver Drowsiness Detection from Single-Channel EEGcited by 12
  14. 2022U-Sleep's resilience to AASM guidelinescited by 12
  15. 2024SPDIM: Source-Free Unsupervised Conditional and Label Shift Adaptation in EEGcited by 12
  16. 2022mulEEG: A Multi-View Representation Learning on EEG Signalscited by 11
  17. 2021Deep Efficient Continuous Manifold Learning for Time Series Modelingcited by 10
  18. 2023CoRe-Sleep: A Multimodal Fusion Framework for Time Series Robust to Imperfect Modalitiescited by 10
  19. 2024A Systematic Literature Review of Spatio-Temporal Graph Neural Network Models for Time Series Forecasting and Classificationcited by 9
  20. 2022TransSleep: Transitioning-aware Attention-based Deep Neural Network for Sleep Stagingcited by 8
  21. 2022SemiPFL: Personalized Semi-Supervised Federated Learning Framework for Edge Intelligencecited by 7
  22. 2022Automated Sleep Staging via Parallel Frequency-Cut Attentioncited by 7
  23. 2022An Adaptive Federated Relevance Framework for Spatial Temporal Graph Learningcited by 7
  24. 2022Do Not Sleep on Traditional Machine Learning: Simple and Interpretable Techniques Are Competitive to Deep Learning for Sleep Scoringcited by 7
  25. 2022SleePyCo: Automatic Sleep Scoring with Feature Pyramid and Contrastive Learningcited by 7
  26. 2023S4Sleep: Elucidating the design space of deep-learning-based sleep stage classification modelscited by 7
  27. 2024NeuroNet: A Novel Hybrid Self-Supervised Learning Framework for Sleep Stage Classification Using Single-Channel EEGcited by 7
  28. 2026DeeperBrain: A Neuro-Grounded EEG Foundation Model Towards Universal BCIcited by 7
  29. 2021Convolutional Neural Networks for Sleep Stage Scoring on a Two-Channel EEG Signalcited by 6
  30. 2022A Novel Semi-supervised Meta Learning Method for Subject-transfer Brain-computer Interfacecited by 6
  31. 2022A CNN-Transformer Deep Learning Model for Real-time Sleep Stage Classification in an Energy-Constrained Wireless Devicecited by 6
  32. 2021MSED: a multi-modal sleep event detection model for clinical sleep analysiscited by 5
  33. 2022PearNet: A Pearson Correlation-based Graph Attention Network for Sleep Stage Recognitioncited by 5
  34. 2022Learning Product Graphs from Spectral Templatescited by 5
  35. 2022ProductGraphSleepNet: Sleep Staging using Product Spatio-Temporal Graph Learning with Attentive Temporal Aggregationcited by 5
  36. 2023PrecTime: A Deep Learning Architecture for Precise Time Series Segmentation in Industrial Manufacturing Operationscited by 5
  37. 2022Multi-Scored Sleep Databases: How to Exploit the Multiple-Labels in Automated Sleep Scoringcited by 4
  38. 2022A multi-level interpretable sleep stage scoring system by infusing experts' knowledge into a deep network architecturecited by 4

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