{"url":"/dataset/ptb","name":"PTB Diagnostic ECG Database","full_name":null,"description_markdown":"The ECGs in this collection were obtained using a non-commercial, PTB prototype recorder with the following specifications:\r\n\r\n16 input channels, (14 for ECGs, 1 for respiration, 1 for line voltage)\r\nInput voltage: ±16 mV, compensated offset voltage up to ± 300 mV\r\nInput resistance: 100 Ω (DC)\r\nResolution: 16 bit with 0.5 μV/LSB (2000 A/D units per mV)\r\nBandwidth: 0 - 1 kHz (synchronous sampling of all channels)\r\nNoise voltage: max. 10 μV (pp), respectively 3 μV (RMS) with input short circuit\r\nOnline recording of skin resistance\r\nNoise level recording during signal collection\r\nThe database contains 549 records from 290 subjects (aged 17 to 87, mean 57.2; 209 men, mean age 55.5, and 81 women, mean age 61.6; ages were not recorded for 1 female and 14 male subjects). Each subject is represented by one to five records. There are no subjects numbered 124, 132, 134, or 161. Each record includes 15 simultaneously measured signals: the conventional 12 leads (i, ii, iii, avr, avl, avf, v1, v2, v3, v4, v5, v6) together with the 3 Frank lead ECGs (vx, vy, vz). Each signal is digitized at 1000 samples per second, with 16 bit resolution over a range of ± 16.384 mV. On special request to the contributors of the database, recordings may be available at sampling rates up to 10 KHz.\r\n\r\nWithin the header (.hea) file of most of these ECG records is a detailed clinical summary, including age, gender, diagnosis, and where applicable, data on medical history, medication and interventions, coronary artery pathology, ventriculography, echocardiography, and hemodynamics. The clinical summary is not available for 22 subjects. \r\n\r\nSource: [PTB](https://physionet.org/content/ptbdb/1.0.0/)","description_withheld":null,"homepage":"https://physionet.org/content/ptbdb/1.0.0/","introduced_date":"1995-01-01","introduced_date_note":null,"introduced_by":null,"license":{"name":"Open Data Commons Attribution License v1.0","url":"https://physionet.org/content/ptbdb/view-license/1.0.0/"},"modalities":[{"name":"Medical","url":"/datasets/modality/medical"}],"tasks":[{"name":"Language Modelling","url":"/task/language-modelling","datasets_with_task":"/datasets/task/language-modelling"},{"name":"ECG Classification","url":"/task/ecg-classification","datasets_with_task":"/datasets/task/ecg-classification"},{"name":"Constituency Grammar Induction","url":"/task/constituency-grammar-induction","datasets_with_task":"/datasets/task/constituency-grammar-induction"},{"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"},{"name":"Myocardial infarction detection","url":"/task/myocardial-infarction-detection","datasets_with_task":"/datasets/task/myocardial-infarction-detection"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["PTB Diagnostic ECG Database"],"data_loaders":[],"num_papers_in_archive":22,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/constituency-grammar-induction-on-ptb","task":"Constituency Grammar Induction","dataset_variant":"PTB Diagnostic ECG Database","rows":24,"metrics":["Mean F1 (WSJ)","Max F1 (WSJ)","Mean F1 (WSJ10)","Max F1 (WSJ10)"],"first_row_in_archive_order":{"model":"Ensemble (Generative MBR)","paper":"/paper/ensemble-distillation-for-unsupervised","metrics":{"Max F1 (WSJ)":"71.9","Mean F1 (WSJ)":"70.4"},"code_links":[{"title":"manga-uofa/ed4ucp","url":"https://github.com/manga-uofa/ed4ucp"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/ecg-patient-identification-gallery-probe-on-2","task":"ECG Patient Identification (gallery-probe)","dataset_variant":"PTB Diagnostic ECG Database","rows":1,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"ElectroCardioGuard","paper":"/paper/electrocardioguard-preventing-patient","metrics":{"Accuracy":"77.0%"},"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"},{"leaderboard":"/sota/language-modelling-on-ptb","task":"Language Modelling","dataset_variant":"PTB Diagnostic ECG Database","rows":1,"metrics":["PPL"],"first_row_in_archive_order":{"model":"I-DARTS","paper":"/paper/improved-differentiable-architecture-search","metrics":{"PPL":"56.0"},"code_links":[{"title":"jiangyingjunn/i-darts","url":"https://github.com/jiangyingjunn/i-darts"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/myocardial-infarction-detection-on-ptb-1","task":"Myocardial infarction detection","dataset_variant":"PTB Diagnostic ECG Database","rows":1,"metrics":["Accuracy (%)"],"first_row_in_archive_order":{"model":"ConvNetQuake","paper":"/paper/deep-learning-for-cardiologist-level","metrics":{"Accuracy (%)":"99.43%"},"code_links":[{"title":"arjung128/mi_detection","url":"https://github.com/arjung128/mi_detection"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/on-eliciting-syntax-from-language-models-via","title":"On Eliciting Syntax from Language Models via Hashing","date":"2024-10-05","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/improving-unsupervised-constituency-parsing","title":"Improving Unsupervised Constituency Parsing via Maximizing Semantic Information","date":"2024-10-03","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":1,"samples_ran":1,"samples_unverified":0,"pointer_only_for_licence":1,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/structural-optimization-ambiguity-and","title":"Structural Optimization Ambiguity and Simplicity Bias in Unsupervised Neural Grammar Induction","date":"2024-07-23","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/generative-pretrained-structured-transformers","title":"Generative Pretrained Structured Transformers: Unsupervised Syntactic Language Models at Scale","date":"2024-03-13","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":7,"samples_ran":4,"samples_unverified":3,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/ensemble-distillation-for-unsupervised","title":"Ensemble Distillation for Unsupervised Constituency Parsing","date":"2023-10-03","rows_on_this_dataset":2,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":22,"samples_ran":16,"samples_unverified":6,"pointer_only_for_licence":22,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/augmenting-transformers-with-recursively","title":"Augmenting Transformers with Recursively Composed Multi-grained Representations","date":"2023-09-28","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":3,"samples_ran":3,"samples_unverified":0,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"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},{"paper":"/paper/dynamic-programming-in-rank-space-scaling-1","title":"Dynamic Programming in Rank Space: Scaling Structured Inference with Low-Rank HMMs and PCFGs","date":"2022-05-01","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/fast-r2d2-a-pretrained-recursive-neural","title":"Fast-R2D2: A Pretrained Recursive Neural Network based on Pruned CKY for Grammar Induction and Text Representation","date":"2022-03-01","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":1,"samples_ran":0,"samples_unverified":1,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/co-training-an-unsupervised-constituency","title":"Co-training an Unsupervised Constituency Parser with Weak Supervision","date":"2021-10-05","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":3,"samples_ran":2,"samples_unverified":1,"pointer_only_for_licence":1,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/neural-bi-lexicalized-pcfg-induction","title":"Neural Bi-Lexicalized PCFG Induction","date":"2021-05-31","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/pcfgs-can-do-better-inducing-probabilistic","title":"PCFGs Can Do Better: Inducing Probabilistic Context-Free Grammars with Many Symbols","date":"2021-04-28","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/unsupervised-parsing-with-s-diora-single-tree","title":"Unsupervised Parsing with S-DIORA: Single Tree Encoding for Deep Inside-Outside Recursive Autoencoders","date":"2020-11-01","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/deep-learning-for-cardiologist-level","title":"Deep Learning for Cardiologist-level Myocardial Infarction Detection in Electrocardiograms","date":"2019-12-16","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/improved-differentiable-architecture-search","title":"Improved Differentiable Architecture Search for Language Modeling and Named Entity Recognition","date":"2019-11-01","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/compound-probabilistic-context-free-grammars","title":"Compound Probabilistic Context-Free Grammars for Grammar Induction","date":"2019-06-24","rows_on_this_dataset":2,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":1,"samples_ran":1,"samples_unverified":0,"pointer_only_for_licence":1,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/unsupervised-latent-tree-induction-with-deep-1","title":"Unsupervised Latent Tree Induction with Deep Inside-Outside Recursive Auto-Encoders","date":"2019-06-01","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/unsupervised-recurrent-neural-network","title":"Unsupervised Recurrent Neural Network Grammars","date":"2019-04-07","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/ordered-neurons-integrating-tree-structures","title":"Ordered Neurons: Integrating Tree Structures into Recurrent Neural Networks","date":"2018-10-22","rows_on_this_dataset":2,"code_links":7,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":12,"samples_ran":3,"samples_unverified":9,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/unsupervised-learning-of-syntactic-structure","title":"Unsupervised Learning of Syntactic Structure with Invertible Neural Projections","date":"2018-08-28","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/neural-language-modeling-by-jointly-learning","title":"Neural Language Modeling by Jointly Learning Syntax and Lexicon","date":"2017-11-02","rows_on_this_dataset":2,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":5,"samples_ran":0,"samples_unverified":5,"pointer_only_for_licence":0,"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":9,"samples_harvested":55,"samples_ran":30,"samples_unverified":25,"pointer_only_for_licence":25,"papers_with_no_sample_that_ran":2,"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."}