ArticlePloS one2019
SleepEEGNet: Automated sleep stage scoring with sequence to sequence deep learning approach.
Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 99 papers.
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Who cites it
99 citing papers in PubMed, 508 citations in OpenAlex.
- Performance Investigation of Somfit Sleep Staging Algorithm.Nature and science of sleep · 2024Trial
- SleepPACNet: new convolutional neural network considering phase-amplitude coupling for automatic sleep stage classification using single-channel electroencephalogram.Scientific reports · 2026Article
- AI-Driven Sleep Staging Using Instantaneous Heart Rate and Accelerometry: Insights From an Apple Watch Study.IEEE transactions on bio-medical engineering · 2026Article
- MLAR-SleepNet: a automatic sleep staging model based on residual and multi-level attention network.Medical & biological engineering & computing · 2026Article
- SleepMFormer: An Efficient Attention Framework with Contrastive Learning for Single-Channel EEG Sleep Staging.Brain sciences · 2026Article
- Modulation-Based Feature Extraction for Robust Sleep Stage Classification Across Apnea-Based Cohorts.Biosensors · 2026Article
- SimpleKANSleepNet: a Kolmogorov-Arnold network based sleep stage classification method.Frontiers in bioinformatics · 2026Article
- Article
- Quantum-entangled feature selection and spiking graph transformer networks for early detection of childhood behavioral markers.Frontiers in behavioral neuroscience · 2026Article
- SE-Res-U-Net: an improved U-Net architecture for efficient sleep state detection and classification.Scientific reports · 2025Article
- Automated analysis of the American Academy of Sleep Medicine Inter-Scorer Reliability gold-standard polysomnogram dataset.Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine · 2025Article
- Research on Driver Fatigue Detection in Real Driving Environments Based on Semi-Dry Electrodes with Automatic Conductive Fluid Replenishment.Sensors (Basel, Switzerland) · 2025Article
- MultiScaleSleepNet: A Hybrid CNN-BiLSTM-Transformer Architecture with Multi-Scale Feature Representation for Single-Channel EEG Sleep Stage Classification.Sensors (Basel, Switzerland) · 2025Article
- LMCSleepNet: A Lightweight Multi-Channel Sleep Staging Model Based on Wavelet Transform and Muli-Scale Convolutions.Sensors (Basel, Switzerland) · 2025Article
- Domain-Invariant Representation Learning and Sleep Dynamics Modeling for Automatic Sleep Staging.ACM transactions on computing for healthcare · 2025Article
- About Digitalisation and AI, Data Protection, Data Exchange, Data Mining-Legal Constraints/Challenges Concerning Sleep Medicine.Journal of sleep research · 2025Review
- EEG Connectivity is an Objective Signature of Reduced Consciousness and Sleep Depth.Brain topography · 2025Article
- Article
- BrainFusion: a Low-Code, Reproducible, and Deployable Software Framework for Multimodal Brain‒Computer Interface and Brain‒Body Interaction Research.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- MCAF-Net: Multi-Channel Temporal Cross-Attention Network with Dynamic Gating for Sleep Stage Classification.Sensors (Basel, Switzerland) · 2025Article
39 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 3 countries.
Funding
Abstract
Electroencephalogram (EEG) is a common base signal used to monitor brain activities 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 performance of 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 κ = 0.79. Our developed model can be applied to other 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.