{"url":"/dataset/mit-bih-arrhythmia-database","name":"MIT-BIH Arrhythmia Database","full_name":null,"description_markdown":"The MIT-BIH Arrhythmia Database contains 48 half-hour excerpts of two-channel ambulatory ECG recordings, obtained from 47 subjects studied by the BIH Arrhythmia Laboratory between 1975 and 1979. Twenty-three recordings were chosen at random from a set of 4000 24-hour ambulatory ECG recordings collected from a mixed population of inpatients (about 60%) and outpatients (about 40%) at Boston's Beth Israel Hospital; the remaining 25 recordings were selected from the same set to include less common but clinically significant arrhythmias that would not be well-represented in a small random sample.\r\n\r\nThe recordings were digitized at 360 samples per second per channel with 11-bit resolution over a 10 mV range. Two or more cardiologists independently annotated each record; disagreements were resolved to obtain the computer-readable reference annotations for each beat (approximately 110,000 annotations in all) included with the database.\r\n\r\nThis directory contains the entire MIT-BIH Arrhythmia Database. About half (25 of 48 complete records, and reference annotation files for all 48 records) of this database has been freely available here since PhysioNet's inception in September 1999. The 23 remaining signal files, which had been available only on the MIT-BIH Arrhythmia Database CD-ROM, were posted here in February 2005.\r\n\r\nMuch more information about this database may be found in the [MIT-BIH Arrhythmia Database Directory](https://archive.physionet.org/physiobank/database/html/mitdbdir/mitdbdir.htm).","description_withheld":null,"homepage":"https://physionet.org/content/mitdb/1.0.0/","introduced_date":"2027-05-28","introduced_date_note":null,"introduced_by":null,"license":{"name":"Open Data Commons Attribution License v1.0","url":"https://physionet.org/content/mitdb/view-license/1.0.0/"},"modalities":[{"name":"Medical","url":"/datasets/modality/medical"}],"tasks":[{"name":"Anomaly Detection","url":"/task/anomaly-detection","datasets_with_task":"/datasets/task/anomaly-detection"},{"name":"Arrhythmia Detection","url":"/task/arrhythmia-detection","datasets_with_task":"/datasets/task/arrhythmia-detection"},{"name":"Heartbeat Classification","url":"/task/heartbeat-classification","datasets_with_task":"/datasets/task/heartbeat-classification"},{"name":"QRS Complex Detection","url":"/task/qrs-complex-detection","datasets_with_task":"/datasets/task/qrs-complex-detection"}],"languages":[{"name":"English","url":"/datasets/language/english"},{"name":"Persian","url":"/datasets/language/persian"}],"variants":["MIT-BIH AR","MIT-BIH Arrhythmia Database"],"data_loaders":[],"num_papers_in_archive":31,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/arrhythmia-detection-on-mit-bih-ar","task":"Arrhythmia Detection","dataset_variant":"MIT-BIH AR","rows":6,"metrics":["Accuracy (Inter-Patient)","Accuracy (Intra-Patient)"],"first_row_in_archive_order":{"model":"BiRNN","paper":"/paper/inter-and-intra-patient-ecg-heartbeat","metrics":{"Accuracy (Inter-Patient)":"99.53%","Accuracy (Intra-Patient)":"99.92%"},"code_links":[{"title":"SajadMo/SleepEEGNet","url":"https://github.com/SajadMo/SleepEEGNet"},{"title":"SSajadM/ECG-Heartbeat-Classification-seq2seq-model","url":"https://github.com/SSajadM/ECG-Heartbeat-Classification-seq2seq-model"},{"title":"SajadMo/ECG-Heartbeat-Classification-seq2seq-model","url":"https://github.com/SajadMo/ECG-Heartbeat-Classification-seq2seq-model"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/qrs-complex-detection-on-mit-bih-ar","task":"QRS Complex Detection","dataset_variant":"MIT-BIH AR","rows":5,"metrics":["Accuracy","F1-score"],"first_row_in_archive_order":{"model":"CNN (LOSO, Intra-Db)","paper":"/paper/impact-of-ecg-dataset-diversity-on","metrics":{"Accuracy":"99.60%","F1-score":"-"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/arrhythmia-detection-on-mit-bih-arrhythmia","task":"Arrhythmia Detection","dataset_variant":"MIT-BIH Arrhythmia Database","rows":2,"metrics":["F1","Precision","Recall","specificity","Accuracy"],"first_row_in_archive_order":{"model":"ATD","paper":"/paper/alternative-telescopic-displacement-an","metrics":{"Accuracy":"98.9","F1":"98.2"},"code_links":[{"title":"D-ST-Sword/ATD","url":"https://github.com/D-ST-Sword/ATD"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/heartbeat-classification-on-mit-bih-ar","task":"Heartbeat Classification","dataset_variant":"MIT-BIH AR","rows":2,"metrics":["PPV (VEB)","Sensitivity (VEB)"],"first_row_in_archive_order":{"model":"ESN Ensembles (II Leads)","paper":"/paper/a-fast-machine-learning-model-for-ecg-based","metrics":{"PPV (VEB)":"95.7%","Sensitivity (VEB)":"92.7%"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/anomaly-detection-on-mit-bih-arrhythmia","task":"Anomaly Detection","dataset_variant":"MIT-BIH Arrhythmia Database","rows":1,"metrics":["F1 score"],"first_row_in_archive_order":{"model":"RCALAD","paper":"/paper/regularized-complete-cycle-consistent-gan-for","metrics":{"F1 score":"60.6"},"code_links":[{"title":"zahradehghanian97/rcalad","url":"https://github.com/zahradehghanian97/rcalad"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/alternative-telescopic-displacement-an","title":"Alternative Telescopic Displacement: An Efficient Multimodal Alignment Method","date":"2023-06-29","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/regularized-complete-cycle-consistent-gan-for","title":"Spot The Odd One Out: Regularized Complete Cycle Consistent Anomaly Detector GAN","date":"2023-04-16","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/a-personalized-zero-shot-ecg-arrhythmia","title":"A Personalized Zero-Shot ECG Arrhythmia Monitoring System: From Sparse Representation Based Domain Adaption to Energy Efficient Abnormal Beat Detection for Practical ECG Surveillance","date":"2022-07-14","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/interpretability-analysis-of-heartbeat","title":"Interpretability Analysis of Heartbeat Classification Based on Heartbeat Activity’s Global Sequence Features and BiLSTM-Attention Neural Network","date":"2019-08-07","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/reservoir-computing-models-for-patient","title":"Reservoir Computing Models for Patient-Adaptable ECG Monitoring in Wearable Devices","date":"2019-07-22","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/a-fast-machine-learning-model-for-ecg-based","title":"A Fast Machine Learning Model for ECG-Based Heartbeat Classification and Arrhythmia Detection","date":"2019-07-18","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/impact-of-ecg-dataset-diversity-on","title":"Impact of ECG Dataset Diversity on Generalization of CNN Model for Detecting QRS Complex","date":"2019-07-10","rows_on_this_dataset":4,"code_links":0,"syntology":null},{"paper":"/paper/inter-and-intra-patient-ecg-heartbeat","title":"Inter- and intra- patient ECG heartbeat classification for arrhythmia detection: a sequence to sequence deep learning approach","date":"2018-12-09","rows_on_this_dataset":1,"code_links":3,"syntology":null},{"paper":"/paper/ecg-heartbeat-classification-a-deep","title":"ECG Heartbeat Classification: A Deep Transferable Representation","date":"2018-04-19","rows_on_this_dataset":1,"code_links":13,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":2,"samples_ran":0,"samples_unverified":2,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/inter-patient-ecg-heartbeat-classification","title":"Inter-Patient ECG Heartbeat Classification with Temporal VCG Optimized by PSO","date":"2017-09-05","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/simple-real-time-qrs-detector-with-the-mamemi","title":"Simple real-time QRS detector with the MaMeMi filter","date":"2015-08-01","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/a-new-hierarchical-method-for-inter-patient","title":"A new hierarchical method for inter-patient heartbeat classification using random projections and RR intervals","date":"2014-06-30","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/support-vector-machine-based-arrhythmia","title":"Support vector machine based arrhythmia classification using reduced features","date":"2005-01-01","rows_on_this_dataset":1,"code_links":0,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":1,"samples_harvested":2,"samples_ran":0,"samples_unverified":2,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":1,"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."}