ArticleNPJ digital medicine2023
U-Sleep's resilience to AASM guidelines.
Article in NPJ digital medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed.
- Fully-automated sleep staging for Parkinson's disease and isolated REM sleep behavior disorder.NPJ digital medicine · 2026Article
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- Comorbidity Classification from Clinical Free-Text using Large Language Models: Application to Sleep Disorder Patients.Journal of medical systems · 2026Article
- A unified time-frequency foundation model for sleep decoding.Nature communications · 2026Article
- AI digital-twin ecosystem translating gut-microbiome-neuroimmune signals into precision sleep-mood interventions.Frontiers in psychiatry · 2026Review
- An explainable deep learning approach for sleep staging in sleep apnea patients across all age subgroups from pulse oximetry signals.Engineering applications of artificial intelligence · 2025Article
- SLEEPYLAND: trust begins with fair evaluation of automatic sleep staging models.NPJ digital medicine · 2025Article
- Sleep staging through an unsupervised learning lens.Patterns (New York, N.Y.) · 2025Article
- Deciphering Insomnia: Benchmarking Automated Sleep Staging Algorithms for Complex Sleep Disorders.Journal of sleep research · 2025Article
- Sleep stage mixing is associated with poor prognosis in early Parkinson's disease.NPJ Parkinson's disease · 2025Article
- Deep-Learning-Based Automated REM Sleep Detection in Patients With REM Sleep Behavior Disorder: Is It Reliable?Journal of clinical neurology (Seoul, Korea) · 2025Article
- Beyond accuracy: a framework for evaluating algorithmic bias and performance, applied to automated sleep scoring.Scientific reports · 2025Article
- Variability and predictability in human sleep.Brain communications · 2025Article
- What does my network learn? Assessing interpretability of deep learning for EEG.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- Deep learning approaches for EEG-based healthcare applications: a comprehensive review.Frontiers in human neuroscience · 2025Review
- Generalized sleep decoding with basal ganglia signals in multiple movement disorders.NPJ digital medicine · 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
- Article
- A robust deep learning detector for sleep spindles and K-complexes: towards population norms.Scientific reports · 2024Article
- Common sleep data pipeline for combined data sets.PloS one · 2024Review
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
AASM guidelines are the result of decades of efforts aiming at standardizing sleep scoring procedure, with the final goal of sharing a worldwide common methodology. The guidelines cover several aspects from the technical/digital specifications, e.g., recommended EEG derivations, to detailed sleep scoring rules accordingly to age. Automated sleep scoring systems have always largely exploited the standards as fundamental guidelines. In this context, deep learning has demonstrated better performance compared to classical machine learning. Our present work shows that a deep learning-based sleep scoring algorithm may not need to fully exploit the clinical knowledge or to strictly adhere to the AASM guidelines. Specifically, we demonstrate that U-Sleep, a state-of-the-art sleep scoring algorithm, can be strong enough to solve the scoring task even using clinically non-recommended or non-conventional derivations, and with no need to exploit information about the chronological age of the subjects. We finally strengthen a well-known finding that using data from multiple data centers always results in a better performing model compared with training on a single cohort. Indeed, we show that this latter statement is still valid even by increasing the size and the heterogeneity of the single data cohort. In all our experiments we used 28528 polysomnography studies from 13 different clinical studies.
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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.