{"url":"/dataset/code-15","name":"CODE-15%","full_name":null,"description_markdown":"A dataset of 12-lead ECGs with annotations. The dataset contains 345 779 exams from 233 770 patients. It was obtained through stratified sampling from the CODE dataset ( 15% of the patients). The data was collected by the Telehealth Network of Minas Gerais in the period between 2010 and 2016.\r\n\r\nSource: [CODE-15%](https://zenodo.org/record/4916206)","description_withheld":null,"homepage":"https://zenodo.org/record/4916206","introduced_date":"2019-04-02","introduced_date_note":null,"introduced_by":{"paper":"/paper/automatic-diagnosis-of-the-short-duration-12","title":"Automatic diagnosis of the 12-lead ECG using a deep neural network","first_author":"Antônio H. Ribeiro","url":null},"license":{"name":"Creative Commons Attribution 4.0 International","url":"https://creativecommons.org/licenses/by/4.0/legalcode"},"modalities":[{"name":"Medical","url":"/datasets/modality/medical"}],"tasks":[{"name":"ECG Classification","url":"/task/ecg-classification","datasets_with_task":"/datasets/task/ecg-classification"},{"name":"ECG Patient Identification","url":"/task/ecg-patient-identification","datasets_with_task":"/datasets/task/ecg-patient-identification"},{"name":"ECG Patient Identification (gallery-probe)","url":"/task/ecg-patient-identification-gallery-probe","datasets_with_task":"/datasets/task/ecg-patient-identification-gallery-probe"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["CODE-15%"],"data_loaders":[],"num_papers_in_archive":6,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/ecg-patient-identification-gallery-probe-on","task":"ECG Patient Identification (gallery-probe)","dataset_variant":"CODE-15%","rows":1,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"ElectroCardioGuard","paper":"/paper/electrocardioguard-preventing-patient","metrics":{"Accuracy":"60.3%"},"code_links":[{"title":"captaintrojan/electrocardioguard","url":"https://github.com/captaintrojan/electrocardioguard"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/electrocardioguard-preventing-patient","title":"ElectroCardioGuard: Preventing Patient Misidentification in Electrocardiogram Databases through Neural Networks","date":"2023-06-09","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."}