ArticleSleep2019
Automated sleep stage scoring of the Sleep Heart Health Study using deep neural networks.
Article in Sleep, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 1 of them a synthesis that pooled it.
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Who cites it
29 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
- MFSleepNet: An Interactive Multimodal Fusion Framework for Automatic Sleep Staging.Sensors (Basel, Switzerland) · 2026Article
- Article
- Quantifying the sources of discrepancy between total recording time and total sleep time in home sleep apnea testing: insights from home-based polysomnography.Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine · 2025Observational
- An ensemble method for improving robustness against the electrode contact problems in automated sleep stage scoring.Scientific reports · 2024Article
- Deep learning-based sleep stage classification with cardiorespiratory and body movement activities in individuals with suspected sleep disorders.Scientific reports · 2023Article
- Clinical applications of artificial intelligence in sleep medicine: a sleep clinician's perspective.Sleep & breathing = Schlaf & Atmung · 2023Review
- [Study on the method of polysomnography sleep stage staging based on attention mechanism and bidirectional gate recurrent unit].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2023Article
- Automated Sleep Stages Classification Using Convolutional Neural Network From Raw and Time-Frequency Electroencephalogram Signals: Systematic Evaluation Study.Journal of medical Internet research · 2023Article
- Article
- A two-branch trade-off neural network for balanced scoring sleep stages on multiple cohorts.Frontiers in neuroscience · 2023Article
- Overview of the hypnodensity approach to scoring sleep for polysomnography and home sleep testing.Frontiers in sleep · 2023Review
- Article
- Automated sleep stage scoring employing a reasoning mechanism and evaluation of its explainability.Scientific reports · 2022Article
- An Automated Wavelet-Based Sleep Scoring Model Using EEG, EMG, and EOG Signals with More Than 8000 Subjects.International journal of environmental research and public health · 2022Article
- Validation Study on Automated Sleep Stage Scoring Using a Deep Learning Algorithm.Medicina (Kaunas, Lithuania) · 2022Article
- Automated Scoring of Sleep and Associated Events.Advances in experimental medicine and biology · 2022Article
- Multiple Machine Learning Methods Reveal Key Biomarkers of Obstructive Sleep Apnea and Continuous Positive Airway Pressure Treatment.Frontiers in genetics · 2022Article
- WaveSleepNet: An interpretable deep convolutional neural network for the continuous classification of mouse sleep and wake.Journal of neuroscience methods · 2021Article
- Imagined character recognition through EEG signals using deep convolutional neural network.Medical & biological engineering & computing · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
STUDY
objectivesPolysomnography (PSG) scoring is labor intensive and suffers from variability in inter- and intra-rater reliability. Automated PSG scoring has the potential to reduce the human labor costs and the variability inherent to this task. Deep learning is a form of machine learning that uses neural networks to recognize data patterns by inspecting many examples rather than by following explicit programming.
methodsA sleep staging classifier trained using deep learning methods scored PSG data from the Sleep Heart Health Study (SHHS). The training set was composed of 42 560 hours of PSG data from 5213 patients. To capture higher-order data, spectrograms were generated from electroencephalography, electrooculography, and electromyography data and then passed to the neural network. A holdout set of 580 PSGs not included in the training set was used to assess model accuracy and discrimination via weighted F1-score, per-stage accuracy, and Cohen's kappa (K).
resultsThe optimal neural network model was composed of spectrograms in the input layer feeding into convolutional neural network layers and a long short-term memory layer to achieve a weighted F1-score of 0.87 and K = 0.82.
conclusionsThe deep learning sleep stage classifier demonstrates excellent accuracy and agreement with expert sleep stage scoring, outperforming human agreement on sleep staging. It achieves comparable or better F1-scores, accuracy, and Cohen's kappa compared to literature for automated sleep stage scoring of PSG epochs. Accurate automated scoring of other PSG events may eventually allow for fully automated PSG scoring.
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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.