{"url":"/task/automatic-sleep-stage-classification","name":"Automatic Sleep Stage Classification","slug":"automatic-sleep-stage-classification","description_markdown":null,"categories":[{"name":"Medical","url":"/area/medical"}],"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","slug_source":"archive_url"},"counts":{"papers_tagged":25,"papers_with_code":14,"benchmarks":2,"benchmark_tables_in_archive":2,"benchmark_tables_shown":2,"benchmark_tables_withheld_as_spam":0,"benchmark_definition":"a leaderboard table with at least one row; benchmark_tables_shown also counts the zero-row tables; benchmark_tables_in_archive adds the tables withheld as spam","datasets":3,"subtasks":0,"parent_tasks":0},"benchmarks":[{"leaderboard":"/sota/automatic-sleep-stage-classification-on-sleep-1","slug":"automatic-sleep-stage-classification-on-sleep-1","dataset":"Sleep-EDF","dataset_url":"/dataset/sleep-edf","rows_in_archive":4,"metrics":["Accuracy","Cohen’s Kappa score","Number of parameters (M)"],"first_row_in_archive_order":{"model":"multi-head attention","paper_title":"An Attention-Based Deep Learning Approach for Sleep Stage Classification With Single-Channel EEG","paper_url":"/paper/an-attention-based-deep-learning-approach-for","paper_date":"2021-04-28","arxiv_id":null,"code_links":[{"title":"emadeldeen24/AttnSleep","url":"https://github.com/emadeldeen24/AttnSleep"}],"syntology":null}},{"leaderboard":"/sota/automatic-sleep-stage-classification-on-isruc","slug":"automatic-sleep-stage-classification-on-isruc","dataset":"ISRUC-Sleep","dataset_url":"/dataset/isruc-sleep","rows_in_archive":1,"metrics":["AUROC","Accuracy","Kappa"],"first_row_in_archive_order":{"model":"SLEEPER-GBT","paper_title":"SLEEPER: interpretable Sleep staging via Prototypes from Expert Rules","paper_url":"/paper/sleeper-interpretable-sleep-staging-via","paper_date":"2019-10-14","arxiv_id":"1910.06100","code_links":[],"syntology":null}}],"datasets":[{"url":"/dataset/sleep-edf","name":"Sleep-EDF","full_name":"Sleep-EDF Expanded","num_papers_in_archive":94},{"url":"/dataset/montreal-archive-of-sleep-studies","name":"Montreal Archive of Sleep Studies","full_name":"Montreal Archive of Sleep Studies","num_papers_in_archive":15},{"url":"/dataset/isruc-sleep","name":"ISRUC-Sleep","full_name":"ISRUC-Sleep","num_papers_in_archive":7}],"subtasks":[],"parent_tasks":[],"papers":{"order":"repositories listed in the archive (desc), then date (desc); the archive holds no stars","population":"papers tagged with this task that list at least one repository in the archive","shown":14,"of":14,"tagged_in_all":25,"items":[{"url":"/paper/time-series-representation-learning-via","title":"Time-Series Representation Learning via Temporal and Contextual Contrasting","date":"2021-06-26","arxiv_id":"2106.14112","repositories_listed":2,"syntology":{"n":2,"n_ran":2,"n_unverified":0,"n_pointer_only":0}},{"url":"/paper/dreem-open-datasets-multi-scored-sleep","title":"Dreem Open Datasets: Multi-Scored Sleep Datasets to compare Human and Automated sleep staging","date":"2019-10-31","arxiv_id":"1911.03221","repositories_listed":2,"syntology":{"n":22,"n_ran":3,"n_unverified":19,"n_pointer_only":0}},{"url":"/paper/msa-cnn-a-lightweight-multi-scale-cnn-with","title":"MSA-CNN: A Lightweight Multi-Scale CNN with Attention for Sleep Stage Classification","date":"2025-01-06","arxiv_id":"2501.02949","repositories_listed":1,"syntology":null},{"url":"/paper/contrastive-learning-for-sleep-staging-based","title":"Contrastive Learning for Sleep Staging based on Inter Subject Correlation","date":"2023-05-05","arxiv_id":"2305.03178","repositories_listed":1,"syntology":null},{"url":"/paper/towards-interpretable-sleep-stage","title":"Toward Interpretable Sleep Stage Classification Using Cross-Modal Transformers","date":"2022-08-15","arxiv_id":"2208.06991","repositories_listed":1,"syntology":null},{"url":"/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","arxiv_id":"2207.07753","repositories_listed":1,"syntology":null},{"url":"/paper/adversarial-domain-adaptation-with-self","title":"ADAST: Attentive Cross-domain EEG-based Sleep Staging Framework with Iterative Self-Training","date":"2021-07-09","arxiv_id":"2107.04470","repositories_listed":1,"syntology":null},{"url":"/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","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/robustsleepnet-transfer-learning-for","title":"RobustSleepNet: Transfer learning for automated sleep staging at scale","date":"2021-01-07","arxiv_id":"2101.02452","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_unverified":0,"n_pointer_only":0}},{"url":"/paper/automatic-sleep-stage-classification-with","title":"Automatic sleep stage classification with deep residual networks in a mixed-cohort setting","date":"2020-08-21","arxiv_id":"2008.09416","repositories_listed":1,"syntology":null},{"url":"/paper/graphsleepnet-adaptive-spatial-temporal-graph","title":"GraphSleepNet: Adaptive Spatial-Temporal Graph Convolutional Networks for Sleep Stage Classification","date":"2020-07-09","arxiv_id":null,"repositories_listed":1,"syntology":null},{"url":"/paper/metasleeplearner-fast-adaptation-of-bio","title":"MetaSleepLearner: A Pilot Study on Fast Adaptation of Bio-signals-Based Sleep Stage Classifier to New Individual Subject Using Meta-Learning","date":"2020-04-08","arxiv_id":"2004.04157","repositories_listed":1,"syntology":null},{"url":"/paper/towards-more-accurate-automatic-sleep-staging","title":"Towards More Accurate Automatic Sleep Staging via Deep Transfer Learning","date":"2019-07-30","arxiv_id":"1907.13177","repositories_listed":1,"syntology":null},{"url":"/paper/joint-classification-and-prediction-cnn","title":"Joint Classification and Prediction CNN Framework for Automatic Sleep Stage Classification","date":"2018-05-16","arxiv_id":"1805.06546","repositories_listed":1,"syntology":null}],"syntology_records":3,"syntology_note":"a paper without a record is not a recorded non-run: it may lack an arXiv id or simply be absent from the graph layer"},"description_links":{"kept":0,"unwrapped_to_text":0,"bare_urls_linked":0,"relative_images_dropped":0,"rule":"internal links are kept only when the target slug exists in the catalog"},"syntology":{"read_at":"2026-09-24T18:15:14+00:00","claim":"Per-sample execution status on synthesized fixtures ('ran N of M samples'); not a correctness claim and not a ranking signal.","status_vocabulary":{"ran_honours":"ran, honoured the contract we drafted","ran_violates":"ran, violated the contract we drafted","ran_draft_wrong":"ran; our contract draft was wrong, not the code","ran_fixture":"ran; our fixture could not drive it","ran":"ran on a synthesized input","unverified":"unverified (harvested, no recorded run)"}},"not_shown":{"libraries":"the archive has no per-task library table","trend_sparklines":"the Trend column of the benchmarks table was a rendered image; it is not in the archive","social_and_latest_sorts":"stars and social signals are not in the archive"}}