{"url":"/dataset/isruc-sleep","name":"ISRUC-Sleep","full_name":"ISRUC-Sleep","description_markdown":"**ISRUC-Sleep** is a polysomnographic (PSG) dataset. The data were obtained from human adults, including healthy subjects, and subjects with sleep disorders under the effect of sleep medication. The dataset, which is structured to support different research objectives, comprises three groups of data: (a) data concerning 100 subjects, with one recording session per subject, (b) data gathered from 8 subjects; two recording sessions were performed per subject, which are useful for studies involving changes in the PSG signals over time, (c) data collected from one recording session related to 10 healthy subjects, which are useful for studies involving comparison of healthy subjects with the patients suffering from sleep disorders.\n\nSource: [https://sleeptight.isr.uc.pt/](https://sleeptight.isr.uc.pt/)\nImage Source: [https://sleeptight.isr.uc.pt/](https://sleeptight.isr.uc.pt/)","description_withheld":null,"homepage":"https://sleeptight.isr.uc.pt/","introduced_date":null,"introduced_date_note":null,"introduced_by":null,"license":null,"modalities":[{"name":"Medical","url":"/datasets/modality/medical"},{"name":"PSG","url":"/datasets/modality/psg"}],"tasks":[{"name":"Sleep Stage Detection","url":"/task/sleep-stage-detection","datasets_with_task":"/datasets/task/sleep-stage-detection"},{"name":"Automatic Sleep Stage Classification","url":"/task/automatic-sleep-stage-classification","datasets_with_task":"/datasets/task/automatic-sleep-stage-classification"}],"languages":[],"variants":["ISRUC-Sleep"],"data_loaders":[{"repo":"https://github.com/neergaard/deep-sleep-pytorch","url":"https://github.com/neergaard/deep-sleep-pytorch","frameworks":["pytorch"]}],"num_papers_in_archive":7,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/sleep-stage-detection-on-isruc-sleep","task":"Sleep Stage Detection","dataset_variant":"ISRUC-Sleep","rows":2,"metrics":["Accuracy","AUROC","Kappa","Macro-F1"],"first_row_in_archive_order":{"model":"SLEEPER-DT","paper":"/paper/sleeper-interpretable-sleep-staging-via","metrics":{"AUROC":"84.7","Accuracy":"78.5","Kappa":"0.72"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/automatic-sleep-stage-classification-on-isruc","task":"Automatic Sleep Stage Classification","dataset_variant":"ISRUC-Sleep","rows":1,"metrics":["AUROC","Accuracy","Kappa"],"first_row_in_archive_order":{"model":"SLEEPER-GBT","paper":"/paper/sleeper-interpretable-sleep-staging-via","metrics":{"AUROC":"86","Accuracy":"80.1","Kappa":"0.741"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/neuronet-a-novel-hybrid-self-supervised","title":"NeuroNet: A Novel Hybrid Self-Supervised Learning Framework for Sleep Stage Classification Using Single-Channel EEG","date":"2024-04-10","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/sleeper-interpretable-sleep-staging-via","title":"SLEEPER: interpretable Sleep staging via Prototypes from Expert Rules","date":"2019-10-14","rows_on_this_dataset":2,"code_links":0,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}