{"url":"/dataset/ecg5000","name":"ECG5000","full_name":"ECG5000","description_markdown":"The original dataset for \"ECG5000\" is a 20-hour long ECG downloaded from Physionet. The name is BIDMC Congestive Heart Failure Database(chfdb) and it is record \"chf07\". It was originally published in \"Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PCh, Mark RG, Mietus JE, Moody GB, Peng C-K, Stanley HE. PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals. Circulation 101(23)\". The data was pre-processed in two steps: (1) extract each heartbeat, (2) make each heartbeat equal length using interpolation. This dataset was originally used in paper \"A general framework for never-ending learning from time series streams\", DAMI 29(6). After that, 5,000 heartbeats were randomly selected. The patient has severe congestive heart failure and the class values were obtained by automated annotation","description_withheld":null,"homepage":"http://www.timeseriesclassification.com/description.php?Dataset=ECG5000","introduced_date":null,"introduced_date_note":null,"introduced_by":null,"license":null,"modalities":[],"tasks":[{"name":"Time Series Classification","url":"/task/time-series-classification","datasets_with_task":"/datasets/task/time-series-classification"},{"name":"Unsupervised Anomaly Detection","url":"/task/unsupervised-anomaly-detection","datasets_with_task":"/datasets/task/unsupervised-anomaly-detection"},{"name":"Outlier Detection","url":"/task/outlier-detection","datasets_with_task":"/datasets/task/outlier-detection"}],"languages":[],"variants":["ECG5000"],"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/outlier-detection-on-ecg5000","task":"Outlier Detection","dataset_variant":"ECG5000","rows":3,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"VRAE+SVM","paper":"/paper/learning-representations-from-healthcare-time","metrics":{"Accuracy":"0.9843"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/time-series-classification-on-ecg5000","task":"Time Series Classification","dataset_variant":"ECG5000","rows":1,"metrics":["Accuracy(30-fold)"],"first_row_in_archive_order":{"model":"R_DST_Ensemble","paper":"/paper/convolutional-shapelet-transform-a-new","metrics":{"Accuracy(30-fold)":"0.9467629629629628"},"code_links":[{"title":"baraline/convst","url":"https://github.com/baraline/convst"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/unsupervised-anomaly-detection-on-ecg5000","task":"Unsupervised Anomaly Detection","dataset_variant":"ECG5000","rows":1,"metrics":["AUC"],"first_row_in_archive_order":{"model":"VRAE+SVM","paper":"/paper/learning-representations-from-healthcare-time","metrics":{"AUC":"0.9836"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/convolutional-shapelet-transform-a-new","title":"Random Dilated Shapelet Transform: A New Approach for Time Series Shapelets","date":"2021-09-28","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/gendis-genetic-discovery-of-shapelets","title":"GENDIS: GENetic DIscovery of Shapelets","date":"2019-09-13","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/learning-representations-from-healthcare-time","title":"Learning Representations from Healthcare Time Series Data for Unsupervised Anomaly Detection","date":"2019-04-04","rows_on_this_dataset":2,"code_links":0,"syntology":null},{"paper":"/paper/lstm-fully-convolutional-networks-for-time","title":"LSTM Fully Convolutional Networks for Time Series Classification","date":"2017-09-08","rows_on_this_dataset":1,"code_links":9,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":2,"samples_ran":2,"samples_unverified":0,"pointer_only_for_licence":2,"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":1,"samples_harvested":2,"samples_ran":2,"samples_unverified":0,"pointer_only_for_licence":2,"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."}