ArticleJournal of the American Medical Informatics Association : JAMIA2018
Expert-level sleep scoring with deep neural networks.
Article in Journal of the American Medical Informatics Association : JAMIA, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 83 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
83 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A Systematic Review of Detecting Sleep Apnea Using Deep Learning.Sensors (Basel, Switzerland) · 2019Pooled it
- Long-range interdependencies aware signal analysis for rapid and accurate obstructive sleep apnea prediction.Nature communications · 2026Article
- Expert-level probabilistic breathing event detector informs phenotyping of sleep apnea.Nature communications · 2026Article
- Ordinal Sleep Depth: A Data-Driven Continuous Measurement of Sleep Depth.Journal of sleep research · 2026Article
- Modulation-Based Feature Extraction for Robust Sleep Stage Classification Across Apnea-Based Cohorts.Biosensors · 2026Article
- Quantum-entangled feature selection and spiking graph transformer networks for early detection of childhood behavioral markers.Frontiers in behavioral neuroscience · 2026Article
- Innovation in technologies for monitoring lung function in patients with respiratory disease.Breathe (Sheffield, England) · 2025Article
- The Boston Children's Hospital Sleep Corpus: A Collection of 15,695 Annotated Pediatric Polysomnograms.Sleep · 2025Article
- Article
- CAISR: achieving human-level performance in automated sleep analysis across all clinical sleep metrics.Sleep · 2025Article
- A case study on generative artificial intelligence to extract the fundamental sleep parameters from polysomnography notes.Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine · 2025Article
- AttenCRF-U: Joint Detection of Sleep-Disordered Breathing and Leg Movements in OSA Patients.Bioengineering (Basel, Switzerland) · 2025Article
- Continuous sleep depth index annotation with deep learning yields novel digital biomarkers for sleep health.NPJ digital medicine · 2025Article
- Artificial Intelligence Can Drive Sleep Medicine.Sleep medicine clinics · 2025Review
- SASBLS: An Advanced Model for Sleep Apnea Detection Based on Single-Channel SpO2.Sensors (Basel, Switzerland) · 2025Article
- Transforming Sleep Monitoring: Review of Wearable and Remote Devices Advancing Home Polysomnography and Their Role in Predicting Neurological Disorders.Biosensors · 2025Review
- Optimizing wearable single-channel electroencephalography sleep staging in a heterogeneous sleep-disordered population using transfer learning.Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine · 2025Article
- Article
- Weakly Supervised Deep Learning for Monitoring Sleep Apnea Severity Using Coarse-grained Labels.IEEE transactions on automation science and engineering : a publication of the IEEE Robotics and Automation Society · 2025Article
- Article
23 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Objectives: Scoring laboratory polysomnography (PSG) data remains a manual task of visually annotating 3 primary categories: sleep stages, sleep disordered breathing, and limb movements. Attempts to automate this process have been hampered by the complexity of PSG signals and physiological heterogeneity between patients. Deep neural networks, which have recently achieved expert-level performance for other complex medical tasks, are ideally suited to PSG scoring, given sufficient training data. Methods: We used a combination of deep recurrent and convolutional neural networks (RCNN) for supervised learning of clinical labels designating sleep stages, sleep apnea events, and limb movements. The data for testing and training were derived from 10 000 clinical PSGs and 5804 research PSGs. Results: When trained on the clinical dataset, the RCNN reproduces PSG diagnostic scoring for sleep staging, sleep apnea, and limb movements with accuracies of 87.6%, 88.2% and 84.7% on held-out test data, a level of performance comparable to human experts. The RCNN model performs equally well when tested on the independent research PSG database. Only small reductions in accuracy were noted when training on limited channels to mimic at-home monitoring devices: frontal leads only for sleep staging, and thoracic belt signals only for the apnea-hypopnea index. Conclusions: By creating accurate deep learning models for sleep scoring, our work opens the path toward broader and more timely access to sleep diagnostics. Accurate scoring automation can improve the utility and efficiency of in-lab and at-home approaches to sleep diagnostics, potentially extending the reach of sleep expertise beyond specialty clinics.
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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.