{"url":"/dataset/sleep-edf","name":"Sleep-EDF","full_name":"Sleep-EDF Expanded","description_markdown":"The sleep-edf database contains 197 whole-night PolySomnoGraphic sleep recordings, containing EEG, EOG, chin EMG, and event markers. Some records also contain respiration and body temperature. Corresponding hypnograms (sleep patterns) were manually scored by well-trained technicians according to the Rechtschaffen and Kales manual, and are also available.\r\n\r\nSource: [https://www.physionet.org/content/sleep-edfx/1.0.0/](https://www.physionet.org/content/sleep-edfx/1.0.0/)","description_withheld":null,"homepage":"https://www.physionet.org/content/sleep-edfx/1.0.0/","introduced_date":null,"introduced_date_note":null,"introduced_by":{"paper":null,"title":"Physiobank, physiotoolkit, and physionet: components of a new research resource for complex physiologic signals","first_author":null,"url":"http://circ.ahajournals.org/content/101/23/e215.full"},"license":{"name":"ODC-By v1.0","url":"https://www.physionet.org/content/sleep-edfx/view-license/1.0.0/"},"modalities":[{"name":"Audio","url":"/datasets/modality/audio"},{"name":"Medical","url":"/datasets/modality/medical"},{"name":"EEG","url":"/datasets/modality/eeg"}],"tasks":[{"name":"Sleep Stage Detection","url":"/task/sleep-stage-detection","datasets_with_task":"/datasets/task/sleep-stage-detection"},{"name":"Multimodal Sleep Stage Detection","url":"/task/multimodal-sleep-stage-detection","datasets_with_task":"/datasets/task/multimodal-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":["Sleep-EDF-SC","Sleep-EDF-ST","Sleep-EDF","Sleep-EDFx (single-channel)"],"data_loaders":[],"num_papers_in_archive":94,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/sleep-stage-detection-on-sleep-edf","task":"Sleep Stage Detection","dataset_variant":"Sleep-EDF","rows":8,"metrics":["Accuracy","Cohen's kappa","Macro-F1"],"first_row_in_archive_order":{"model":"SleePyCo (Fpz-Cz only)","paper":"/paper/sleepyco-automatic-sleep-scoring-with-feature","metrics":{"Accuracy":"86.8%","Cohen's kappa":"0.820","Macro-F1":"0.812"},"code_links":[{"title":"gist-ailab/sleepyco","url":"https://github.com/gist-ailab/sleepyco"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/automatic-sleep-stage-classification-on-sleep-1","task":"Automatic Sleep Stage Classification","dataset_variant":"Sleep-EDF","rows":4,"metrics":["Accuracy","Cohen’s Kappa score","Number of parameters (M)"],"first_row_in_archive_order":{"model":"multi-head attention","paper":"/paper/an-attention-based-deep-learning-approach-for","metrics":{"Accuracy":"84.4"},"code_links":[{"title":"emadeldeen24/AttnSleep","url":"https://github.com/emadeldeen24/AttnSleep"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/multimodal-sleep-stage-detection-on-sleep-edf","task":"Multimodal Sleep Stage Detection","dataset_variant":"Sleep-EDF-SC","rows":3,"metrics":["Accuracy","Macro-F1","Cohen's kappa"],"first_row_in_archive_order":{"model":"CatBoost","paper":"/paper/do-not-sleep-on-linear-models-simple-and","metrics":{"Accuracy":"86.4%","Cohen's kappa":"0.812","Macro-F1":"0.802"},"code_links":[{"title":"predict-idlab/sleep-linear","url":"https://github.com/predict-idlab/sleep-linear"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/multimodal-sleep-stage-detection-on-sleep-edf-1","task":"Multimodal Sleep Stage Detection","dataset_variant":"Sleep-EDF-ST","rows":3,"metrics":["Accuracy"," Macro-F1","Cohen's kappa"],"first_row_in_archive_order":{"model":"CatBoost","paper":"/paper/do-not-sleep-on-linear-models-simple-and","metrics":{" Macro-F1":"0.795","Accuracy":"83.6%","Cohen's kappa":"0.765"},"code_links":[{"title":"predict-idlab/sleep-linear","url":"https://github.com/predict-idlab/sleep-linear"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/sleep-stage-detection-on-sleep-edfx-single","task":"Sleep Stage Detection","dataset_variant":"Sleep-EDFx (single-channel)","rows":3,"metrics":["Accuracy","Cohen's Kappa","Macro-F1"],"first_row_in_archive_order":{"model":"NeuroNet (Fpz-Cz only)","paper":"/paper/neuronet-a-novel-hybrid-self-supervised","metrics":{"Accuracy":"85.24%","Macro-F1":"0.798"},"code_links":[{"title":"dlcjfgmlnasa/NeuroNet","url":"https://github.com/dlcjfgmlnasa/NeuroNet"}]},"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/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":2,"code_links":1,"syntology":null},{"paper":"/paper/towards-interpretable-sleep-stage","title":"Toward Interpretable Sleep Stage Classification Using Cross-Modal Transformers","date":"2022-08-15","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/do-not-sleep-on-linear-models-simple-and","title":"Do Not Sleep on Traditional Machine Learning: Simple and Interpretable Techniques Are Competitive to Deep Learning for Sleep Scoring","date":"2022-07-15","rows_on_this_dataset":6,"code_links":1,"syntology":null},{"paper":"/paper/time-series-representation-learning-via","title":"Time-Series Representation Learning via Temporal and Contextual Contrasting","date":"2021-06-26","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":2,"samples_ran":2,"samples_unverified":0,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/an-attention-based-deep-learning-approach-for","title":"An Attention-Based Deep Learning Approach for Sleep Stage Classification With Single-Channel EEG","date":"2021-04-28","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":2,"code_links":1,"syntology":null},{"paper":"/paper/towards-more-accurate-automatic-sleep-staging","title":"Towards More Accurate Automatic Sleep Staging via Deep Transfer Learning","date":"2019-07-30","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/intra-and-inter-epoch-temporal-context","title":"Intra- and Inter-epoch Temporal Context Network (IITNet) Using Sub-epoch Features for Automatic Sleep Scoring on Raw Single-channel EEG","date":"2019-02-18","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/joint-classification-and-prediction-cnn","title":"Joint Classification and Prediction CNN Framework for Automatic Sleep Stage Classification","date":"2018-05-16","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/deep-convolutional-neural-networks-for-10","title":"Deep Convolutional Neural Networks for Interpretable Analysis of EEG Sleep Stage Scoring","date":"2017-10-02","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/deepsleepnet-a-model-for-automatic-sleep","title":"DeepSleepNet: a Model for Automatic Sleep Stage Scoring based on Raw Single-Channel EEG","date":"2017-03-12","rows_on_this_dataset":1,"code_links":8,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":33,"samples_ran":0,"samples_unverified":33,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":2,"samples_harvested":35,"samples_ran":2,"samples_unverified":33,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":1,"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."}