{"url":"/dataset/phsyionet-challenge-2024","name":"ECG-Image-Database","full_name":"Digitization and Classification of ECG Images: The George B. Moody PhysioNet Challenge 2024","description_markdown":"The George B. Moody PhysioNet Challenges are annual competitions that invite participants to develop automated approaches for addressing important physiological and clinical problems. The 2024 Challenge invites teams to develop algorithms for digitizing and classifying electrocardiograms (ECGs) captured from images or paper printouts. Despite the recent advances in digital ECG devices, physical or paper ECGs remain common, especially in the Global South. These physical ECGs document the history and diversity of cardiovascular diseases (CVDs), and algorithms that can digitize and classify these images have the potential to improve our understanding and treatment of CVDs, especially for underrepresented and underserved populations.","description_withheld":null,"homepage":"https://moody-challenge.physionet.org/2024/","introduced_date":"2024-01-25","introduced_date_note":null,"introduced_by":null,"license":null,"modalities":[{"name":"Time series","url":"/datasets/modality/time-series"}],"tasks":[{"name":"Classification","url":"/task/classification-1","datasets_with_task":"/datasets/task/classification-1"},{"name":"Segmentation","url":"/task/segmentation","datasets_with_task":"/datasets/task/segmentation"},{"name":"ECG Digitization","url":"/task/ecg-digitization","datasets_with_task":"/datasets/task/ecg-digitization"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["ECG-Image-Database"],"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/ecg-digitization-on-physionet-challenge-2024","task":"ECG Digitization","dataset_variant":"ECG-Image-Database","rows":3,"metrics":["SNR"],"first_row_in_archive_order":{"model":"ECG-Digitiser","paper":"/paper/combining-hough-transform-and-deep-learning","metrics":{"SNR":"12.15"},"code_links":[{"title":"felixkrones/ECG-Digitiser","url":"https://github.com/felixkrones/ECG-Digitiser"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/wavie-a-modular-and-open-source-python","title":"WAVIE: A Modular and Open-Source Python Implementation for Fully Automated Digitisation of Paper Electrocardiograms","date":"2024-12-20","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/segmentation-based-extraction-of-key","title":"Segmentation-based Extraction of Key Components from ECG Images: A Framework for Precise Classification and Digitization","date":"2024-12-20","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/combining-hough-transform-and-deep-learning","title":"Combining Hough Transform and Deep Learning Approaches to Reconstruct ECG Signals From Printouts","date":"2024-10-18","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."}