{"url":"/dataset/physionet-challenge-2018","name":"PhysioNet Challenge 2018","full_name":"You Snooze You Win - The PhysioNet Computing in Cardiology Challenge 2018","description_markdown":"Data for this challenge were contributed by the Massachusetts General Hospital’s (MGH) Computational Clinical Neurophysiology Laboratory (CCNL), and the Clinical Data Animation Laboratory (CDAC). The dataset includes 1,985 subjects which were monitored at an MGH sleep laboratory for the diagnosis of sleep disorders. The data were partitioned into balanced training (n = 994), and test sets (n = 989).\r\n\r\nThe sleep stages of the subjects were annotated by clinical staff at the MGH according to the American Academy of Sleep Medicine (AASM) manual for the scoring of sleep. More specifically, the following six sleep stages were annotated in 30 second contiguous intervals: wakefulness, stage 1, stage 2, stage 3, rapid eye movement (REM), and undefined.\r\n\r\nCertified sleep technologists at the MGH also annotated waveforms for the presence of arousals that interrupted the sleep of the subjects. The annotated arousals were classified as either: spontaneous arousals, respiratory effort related arousals (RERA), bruxisms, hypoventilations, hypopneas, apneas (central, obstructive and mixed), vocalizations, snores, periodic leg movements, Cheyne-Stokes breathing or partial airway obstructions.\r\n\r\nThe subjects had a variety of physiological signals recorded as they slept through the night including: electroencephalography (EEG), electrooculography (EOG), electromyography (EMG), electrocardiology (EKG), and oxygen saturation (SaO2). Excluding SaO2, all signals were sampled to 200 Hz and were measured in microvolts. For analytic convenience, SaO2 was resampled to 200 Hz, and is measured as a percentage.","description_withheld":null,"homepage":"https://physionet.org/content/challenge-2018/1.0.0/","introduced_date":null,"introduced_date_note":null,"introduced_by":null,"license":null,"modalities":[],"tasks":[{"name":"Sleep Stage Detection","url":"/task/sleep-stage-detection","datasets_with_task":"/datasets/task/sleep-stage-detection"}],"languages":[],"variants":["PhysioNet Challenge 2018"],"data_loaders":[],"num_papers_in_archive":4,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/sleep-arousal-detection-on-you-snooze-you-win","task":"Sleep Arousal Detection","dataset_variant":"You Snooze You Win - The PhysioNet Computing in Cardiology Challenge 2018","rows":2,"metrics":["AUPRC","AUROC"],"first_row_in_archive_order":{"model":"DeepSleep","paper":"/paper/deepsleep-fast-and-accurate-delineation-of","metrics":{"AUPRC":"0.550","AUROC":"0.927"},"code_links":[{"title":"GuanLab/DeepSleep","url":"https://github.com/GuanLab/DeepSleep"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/sleep-stage-detection-on-physionet-challenge-1","task":"Sleep Stage Detection","dataset_variant":"PhysioNet Challenge 2018","rows":2,"metrics":["Accuracy","Cohen's Kappa","Macro-F1"],"first_row_in_archive_order":{"model":"XSleepNet (EEG, EOG, EMG)","paper":"/paper/xsleepnet-multi-view-sequential-model-for","metrics":{"Accuracy":"81.1%","Cohen's Kappa":"0.742","Macro-F1":"0.794"},"code_links":[{"title":"pquochuy/xsleepnet","url":"https://github.com/pquochuy/xsleepnet"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/sleepyco-automatic-sleep-scoring-with-feature","title":"SleePyCo: Automatic Sleep Scoring with Feature Pyramid and Contrastive Learning","date":"2022-09-20","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/deepsleep-2-0-automated-sleep-arousal","title":"DeepSleep 2.0: Automated Sleep Arousal Segmentation via Deep Learning","date":"2022-03-01","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/xsleepnet-multi-view-sequential-model-for","title":"XSleepNet: Multi-View Sequential Model for Automatic Sleep Staging","date":"2020-07-08","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/deepsleep-fast-and-accurate-delineation-of","title":"Deepsleep: Fast and Accurate Delineation of Sleep Arousals at Millisecond Resolution by Deep Learning","date":"2019-09-07","rows_on_this_dataset":1,"code_links":1,"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."}