mlsanityData to 14 Oct 2026

SleepEEGNet: Automated Sleep Stage Scoring with Sequence to Sequence Deep Learning Approach

arXiv 1903.02108 ↗First posted 5 Mar 2019eess.SP cs.LG q-bio.QMcited by 54

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Abstract (arXiv)

Electroencephalogram (EEG) is a common base signal used to monitor brain activity and diagnose sleep disorders. Manual sleep stage scoring is a time-consuming task for sleep experts and is limited by inter-rater reliability. In this paper, we propose an automatic sleep stage annotation method called SleepEEGNet using a single-channel EEG signal. The SleepEEGNet is composed of deep convolutional neural networks (CNNs) to extract time-invariant features, frequency information, and a sequence to sequence model to capture the complex and long short-term context dependencies between sleep epochs and scores. In addition, to reduce the effect of the class imbalance problem presented in the available sleep datasets, we applied novel loss functions to have an equal misclassified error for each sleep stage while training the network. We evaluated the proposed method on different single-EEG channels (i.e., Fpz-Cz and Pz-Oz EEG channels) from the Physionet Sleep-EDF datasets published in 2013 and 2018. The evaluation results demonstrate that the proposed method achieved the best annotation performance compared to current literature, with an overall accuracy of 84.26%, a macro F1-score of 79.66% and Cohen's Kappa coefficient = 0.79. Our developed model is ready to test with more sleep EEG signals and aid the sleep specialists to arrive at an accurate diagnosis. The source code is available at https://github.com/SajadMo/SleepEEGNet.

Key facts. Cited by 54 papers in the map (data through 14 Oct 2026). First posted on arXiv 5 Mar 2019.

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Citing papers per year: 2021: 9; 2022: 11; 2023: 8; 2024: 8; 2025: 8; 2026: 10.

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  1. 2021BENDR: using transformers and a contrastive self-supervised learning task to learn from massive amounts of EEG datacited by 199
  2. 2021SleepTransformer: Automatic Sleep Staging with Interpretability and Uncertainty Quantificationcited by 84
  3. 2021Automatic Sleep Staging of EEG Signals: Recent Development, Challenges, and Future Directionscited by 43
  4. 2024SleepFM: Multi-modal Representation Learning for Sleep Across Brain Activity, ECG and Respiratory Signalscited by 41
  5. 2021SalientSleepNet: Multimodal Salient Wave Detection Network for Sleep Stagingcited by 32
  6. 2021Self-supervised Contrastive Learning for EEG-based Sleep Stagingcited by 27
  7. 2021DeepSleepNet-Lite: A Simplified Automatic Sleep Stage Scoring Model with Uncertainty Estimatescited by 26
  8. 2023L-SeqSleepNet: Whole-cycle Long Sequence Modelling for Automatic Sleep Stagingcited by 21
  9. 2024Self-supervised Learning for Electroencephalogram: A Systematic Surveycited by 19
  10. 2021ADAST: Attentive Cross-domain EEG-based Sleep Staging Framework with Iterative Self-Trainingcited by 17
  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
38 more
  1. 2022U-Sleep's resilience to AASM guidelinescited by 12
  2. 2022Do Not Sleep on Traditional Machine Learning: Simple and Interpretable Techniques Are Competitive to Deep Learning for Sleep Scoringcited by 7
  3. 2022SleePyCo: Automatic Sleep Scoring with Feature Pyramid and Contrastive Learningcited by 7
  4. 2024Personalized Sleep Staging Leveraging Source-free Unsupervised Domain Adaptationcited by 7
  5. 2022A CNN-Transformer Deep Learning Model for Real-time Sleep Stage Classification in an Energy-Constrained Wireless Devicecited by 6
  6. 2023CSLP-AE: A Contrastive Split-Latent Permutation Autoencoder Framework for Zero-Shot Electroencephalography Signal Conversioncited by 6
  7. 2022PearNet: A Pearson Correlation-based Graph Attention Network for Sleep Stage Recognitioncited by 5
  8. 2022Multi-Scored Sleep Databases: How to Exploit the Multiple-Labels in Automated Sleep Scoringcited by 4
  9. 2022A multi-level interpretable sleep stage scoring system by infusing experts' knowledge into a deep network architecturecited by 4
  10. 2026sleep2vec: Unified Cross-Modal Alignment for Heterogeneous Nocturnal Biosignalscited by 4
  11. 2021Temporal convolutional networks and transformers for classifying the sleep stage in awake or asleep using pulse oximetry signalscited by 3
  12. 2024A Deep Generative Model for Five-Class Sleep Staging with Arbitrary Sensor Inputcited by 3
  13. 2025Deep Learning-Powered Electrical Brain Signals Analysis: Advancing Neurological Diagnosticscited by 3
  14. 2025EEG-VLM: A Hierarchical Vision-Language Model with Multi-Level Feature Alignment and Visually Enhanced Language-Guided Reasoning for EEG Image-Based Sleep Stage Predictioncited by 3
  15. 2025Stanford Sleep Bench: Evaluating Polysomnography Pre-training Methods for Sleep Foundation Modelscited by 3
  16. 2022LEAVES: Learning Views for Time-Series Biobehavioral Data in Contrastive Learningcited by 2
  17. 2023SleepEGAN: A GAN-enhanced Ensemble Deep Learning Model for Imbalanced Classification of Sleep Stagescited by 2
  18. 2024WaveSleepNet: An Interpretable Network for Expert-like Sleep Stagingcited by 2
  19. 2024A Systematic Review on Sleep Stage Classification and Sleep Disorder Detection Using Artificial Intelligencecited by 2
  20. 2026Demographic-Aware Transfer Learning for Sleep Stage Classification in Clinical Polysomnographycited by 2
  21. 2022Attention mechanisms for physiological signal deep learning: which attention should we take?cited by 1
  22. 2023Approximating DTW with a convolutional neural network on EEG datacited by 1
  23. 2023Quantifying the Impact of Data Characteristics on the Transferability of Sleep Stage Scoring Modelscited by 1
  24. 2023Transparency in Sleep Staging: Deep Learning Method for EEG Sleep Stage Classification with Model Interpretabilitycited by 1
  25. 2023Domain Invariant Representation Learning and Sleep Dynamics Modeling for Automatic Sleep Stagingcited by 1
  26. 2025Introducing Multimodal Paradigm for Learning Sleep Staging PSG via General-Purpose Modelcited by 1
  27. 2025EEG Sleep Stage Classification with Continuous Wavelet Transform and Deep Learningcited by 1
  28. 2021MRNet: a Multi-scale Residual Network for EEG-based Sleep Stagingcited by 0
  29. 2022Machine Learning-based EEG Applications and Marketscited by 0
  30. 2023Contrastive Learning for Sleep Staging based on Inter Subject Correlationcited by 0
  31. 2024Clustering and Data Augmentation to Improve Accuracy of Sleep Assessment and Sleep Individuality Analysiscited by 0
  32. 2025PSG-MAE: Robust Multitask Sleep Event Monitoring using Multichannel PSG Reconstruction and Inter-channel Contrastive Learningcited by 0
  33. 2025FetalSleepNet: A Transfer Learning Framework with Spectral Equalisation Domain Adaptation for Fetal Sleep Stage Classificationcited by 0
  34. 2025A Context-Aware Temporal Modeling through Unified Multi-Scale Temporal Encoding and Hierarchical Sequence Learning for Single-Channel EEG Sleep Stagingcited by 0
  35. 2026Making Brain-Computer Interfaces More Securecited by 0
  36. 2026Lightweight ML-Based Automatic Sleep Staging Framework with Constrained CNN and Mamba for Small-Sample EEG Datasetscited by 0
  37. 2026PHINN-EEG: Topological Time-Series Analysis of Dream-State EEG -- Dynamic Betti Curves for Dream Content Classification and Topology-Conditioned Neural Signal Synthesiscited by 0
  38. 2026Predicting the Outcome of rTMS Depression Therapy using EEG Signals and CNNcited by 0

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