ArticleInternational journal of environmental research and public health2019
A Deep Learning Model for Automated Sleep Stages Classification Using PSG Signals.
Article in International journal of environmental research and public health, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 65 papers, 1 of them a synthesis that pooled it.
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Who cites it
65 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep learning for EEG-based sleep stage classification: a review.Medical & biological engineering & computing · 2026Pooled it
- Impact of chronic sleep restriction on sleep continuity, sleep structure, and neurobehavioral performance.Sleep · 2022Trial
- A pilot study on essential oil aroma stimulation for enhancing slow-wave EEG in sleeping brain.Scientific reports · 2021Trial
- Neural network architectures and normalization techniques for automated sleep stage classification using rodent EEG and EMG signals.PloS one · 2026Article
- PMMCT: A Parallel Multimodal CNN-Transformer Model to Detect Slow Eye Movement for Recognizing Driver Sleepiness.Sensors (Basel, Switzerland) · 2025Article
- A review of hybrid EEG-based multimodal human-computer interfaces using deep learning: applications, advances, and challenges.Biomedical engineering letters · 2025Review
- Article
- Real-time sleep disorder monitoring design using dynamic temporal graphs with facial and acoustic feature fusion.Frontiers in artificial intelligence · 2025Article
- A Review on Deep Learning for Quality of Life Assessment Through the Use of Wearable Data.IEEE open journal of engineering in medicine and biology · 2025Article
- FPJA-Net: A Lightweight End-to-End Network for Sleep Stage Prediction Based on Feature Pyramid and Joint Attention.Interdisciplinary sciences, computational life sciences · 2024Article
- GRU-powered sleep stage classification with permutation-based EEG channel selection.Scientific reports · 2024Article
- ADHD/CD-NET: automated EEG-based characterization of ADHD and CD using explainable deep neural network technique.Cognitive neurodynamics · 2024Article
- ZleepAnlystNet: a novel deep learning model for automatic sleep stage scoring based on single-channel raw EEG data using separating training.Scientific reports · 2024Article
- Automatic classification of sleep stages using EEG signals and convolutional neural networks.PloS one · 2024Article
- Sleep stage prediction using multimodal body network and circadian rhythm.PeerJ. Computer science · 2024Article
- Development of generalizable automatic sleep staging using heart rate and movement based on large databases.Biomedical engineering letters · 2023Article
- Temporal Feature Extraction and Machine Learning for Classification of Sleep Stages Using Telemetry Polysomnography.Brain sciences · 2023Article
- Electroencephalography Signal Processing: A Comprehensive Review and Analysis of Methods and Techniques.Sensors (Basel, Switzerland) · 2023Review
- Mixed-Input Deep Learning Approach to Sleep/Wake State Classification by Using EEG Signals.Diagnostics (Basel, Switzerland) · 2023Article
- Multi-scored sleep databases: how to exploit the multiple-labels in automated sleep scoring.Sleep · 2023Article
5 more citing papers are in PubMed but not listed here.
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Sleep disorder is a symptom of many neurological diseases that may significantly affect the quality of daily life. Traditional methods are time-consuming and involve the manual scoring of polysomnogram (PSG) signals obtained in a laboratory environment. However, the automated monitoring of sleep stages can help detect neurological disorders accurately as well. In this study, a flexible deep learning model is proposed using raw PSG signals. A one-dimensional convolutional neural network (1D-CNN) is developed using electroencephalogram (EEG) and electrooculogram (EOG) signals for the classification of sleep stages. The performance of the system is evaluated using two public databases (sleep-edf and sleep-edfx). The developed model yielded the highest accuracies of 98.06%, 94.64%, 92.36%, 91.22%, and 91.00% for two to six sleep classes, respectively, using the sleep-edf database. Further, the proposed model obtained the highest accuracies of 97.62%, 94.34%, 92.33%, 90.98%, and 89.54%, respectively for the same two to six sleep classes using the sleep-edfx dataset. The developed deep learning model is ready for clinical usage, and can be tested with big PSG data.
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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.