{"doi":"10.1038/s41746-021-00440-5","title":"U-Sleep: resilient high-frequency sleep staging","abstract":"Sleep disorders affect a large portion of the global population and are strong predictors of morbidity and all-cause mortality. Sleep staging segments a period of sleep into a sequence of phases providing the basis for most clinical decisions in sleep medicine. Manual sleep staging is difficult and time-consuming as experts must evaluate hours of polysomnography (PSG) recordings with electroencephalography (EEG) and electrooculography (EOG) data for each patient. Here, we present U-Sleep, a publicly available, ready-to-use deep-learning-based system for automated sleep staging ( sleep.ai.ku.dk ). U-Sleep is a fully convolutional neural network, which was trained and evaluated on PSG recordings from 15,660 participants of 16 clinical studies. It provides accurate segmentations across a wide range of patient cohorts and PSG protocols not considered when building the system. U-Sleep works for arbitrary combinations of typical EEG and EOG channels, and its special deep learning architecture can label sleep stages at shorter intervals than the typical 30 s periods used during training. We show that these labels can provide additional diagnostic information and lead to new ways of analyzing sleep. U-Sleep performs on par with state-of-the-art automatic sleep staging systems on multiple clinical datasets, even if the other systems were built specifically for the particular data. A comparison with consensus-scores from a previously unseen clinic shows that U-Sleep performs as accurately as the best of the human experts. U-Sleep can support the sleep staging workflow of medical experts, which decreases healthcare costs, and can provide highly accurate segmentations when human expertize is lacking.","journal":"npj Digital Medicine","year":2021,"id":145662,"datarank":0.8765316625547042,"base_score":5.84354441703136,"endowment":5.84354441703136,"self_citation_contribution":0.8765316625547042,"citation_network_contribution":0.0,"self_endowment_contribution":0.8765316625547042,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":344,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9655,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2021-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":618396,"name":"Sune Darkner","orcid":"0000-0001-6114-7100","position":1,"is_corresponding":false},{"id":619649,"name":"Lykke Kempfner","orcid":null,"position":2,"is_corresponding":false},{"id":619650,"name":"Miki Nikolic","orcid":null,"position":3,"is_corresponding":false},{"id":490231,"name":"Poul Jennum","orcid":"0000-0001-6986-5254","position":4,"is_corresponding":false},{"id":618397,"name":"Christian Igel","orcid":"0000-0003-2868-0856","position":5,"is_corresponding":false},{"id":618395,"name":"Mathias Perslev","orcid":"0000-0002-0358-4692","position":0,"is_corresponding":true}],"reference_count":85,"raw_metadata":{"citation_network_status":"fetched"},"created_at":"2026-07-18T23:42:12.871665Z","pmid":"33859353","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}