{"url":"/dataset/shhs","name":"SHHS","full_name":"Sleep Heart Health Study","description_markdown":"The Sleep Heart Health Study (SHHS) is a multi-center cohort study implemented by the National Heart Lung & Blood Institute to determine the cardiovascular and other consequences of sleep-disordered breathing. It tests whether sleep-related breathing is associated with an increased risk of coronary heart disease, stroke, all cause mortality, and hypertension.  In all, 6,441 men and women aged 40 years and older were enrolled between November 1, 1995 and January 31, 1998 to take part in SHHS Visit 1. During exam cycle 3 (January 2001- June 2003), a second polysomnogram (SHHS Visit 2) was obtained in 3,295 of the participants. CVD Outcomes data were monitored and adjudicated by parent cohorts between baseline and 2011. More than 130 manuscripts have been published investigating predictors and outcomes of sleep disorders.","description_withheld":null,"homepage":"https://sleepdata.org/datasets/shhs","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":["SHHS"],"data_loaders":[],"num_papers_in_archive":11,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/sleep-stage-detection-on-shhs","task":"Sleep Stage Detection","dataset_variant":"SHHS","rows":10,"metrics":["Accuracy","Cohen's Kappa","Macro-F1"],"first_row_in_archive_order":{"model":"SynthSleepNet (EEG2+EOG2+EMG1)","paper":"/paper/toward-foundational-model-for-sleep-analysis","metrics":{"Accuracy":"89.89%","Cohen's Kappa":"0.860","Macro-F1":"0.845"},"code_links":[{"title":"dlcjfgmlnasa/SynthSleepNet","url":"https://github.com/dlcjfgmlnasa/SynthSleepNet"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/toward-foundational-model-for-sleep-analysis","title":"Toward Foundational Model for Sleep Analysis Using a Multimodal Hybrid Self-Supervised Learning Framework","date":"2025-02-18","rows_on_this_dataset":3,"code_links":1,"syntology":null},{"paper":"/paper/mc2sleepnet-multi-modal-cross-masking-with","title":"MC2SleepNet: Multi-modal Cross-masking with Contrastive Learning for Sleep Stage Classification","date":"2025-02-13","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"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/core-sleep-a-multimodal-fusion-framework-for","title":"CoRe-Sleep: A Multimodal Fusion Framework for Time Series Robust to Imperfect Modalities","date":"2023-03-27","rows_on_this_dataset":1,"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."}