{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/ecg-classification/papers/2","list_of":"/task/ecg-classification","task":"ECG Classification","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":2,"pages_in_order":2,"rows_per_page":100,"rows":[101,116],"of":116,"counts":{"archive_papers_tagged":116,"with_a_code_link":49,"where_syntology_ran_a_sample":5,"not_listed_spam_title":0,"listed":116,"listed_where_code_ran":5,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":4,"every_run_a_failure_of_syntologys_instrument":1,"listed_with_a_run_with_no_instrument_failure":4,"listed_every_run_a_failure_of_syntologys_instrument":1,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/ecg-classification","prev":"/task/ecg-classification","next":null,"papers":[{"url":null,"slug":"combining-scatter-transform-and-deep-neural","title":"Combining Scatter Transform and Deep Neural Networks for Multilabel Electrocardiogram Signal Classification","date":"2020-10-15","arxiv_id":"2010.07639","repositories_listed":0,"syntology":null},{"url":null,"slug":"ecg-classification-with-a-convolutional","title":"ECG Classification with a Convolutional Recurrent Neural Network","date":"2020-09-28","arxiv_id":"2009.13320","repositories_listed":0,"syntology":null},{"url":null,"slug":"piece-wise-matching-layer-in-representation","title":"Piece-wise Matching Layer in Representation Learning for ECG Classification","date":"2020-09-26","arxiv_id":"2010.06510","repositories_listed":0,"syntology":null},{"url":null,"slug":"simgans-simulator-based-generative","title":"SimGANs: Simulator-Based Generative Adversarial Networks for ECG Synthesis to Improve Deep ECG Classification","date":"2020-06-27","arxiv_id":"2006.15353","repositories_listed":0,"syntology":null},{"url":null,"slug":"multi-lead-ecg-classification-via-an","title":"Multi-Lead ECG Classification via an Information-Based Attention Convolutional Neural Network","date":"2020-03-25","arxiv_id":"2003.12009","repositories_listed":0,"syntology":null},{"url":null,"slug":"analysis-and-classification-of-heart-diseases","title":"Analysis and classification of heart diseases using heartbeat features and machine learning algorithms","date":"2019-08-31","arxiv_id":null,"repositories_listed":0,"syntology":null},{"url":"/paper/reservoir-computing-models-for-patient","slug":"reservoir-computing-models-for-patient","title":"Reservoir Computing Models for Patient-Adaptable ECG Monitoring in Wearable Devices","date":"2019-07-22","arxiv_id":"1907.09504","repositories_listed":0,"syntology":null},{"url":null,"slug":"adversarial-examples-for-electrocardiograms","title":"Adversarial Examples for Electrocardiograms","date":"2019-05-13","arxiv_id":"1905.05163","repositories_listed":0,"syntology":null},{"url":null,"slug":"ultra-low-power-and-real-time-ecg-1","title":"Ultra Low-Power and Real-time ECG Classification Based on STDP and R-STDP Neural Networks for Wearable Devices","date":"2019-05-08","arxiv_id":"1905.02954","repositories_listed":0,"syntology":null},{"url":null,"slug":"fine-grained-ecg-classification-based-on-deep","title":"Deep Time-Frequency Representation and Progressive Decision Fusion for ECG Classification","date":"2019-01-19","arxiv_id":"1901.06469","repositories_listed":0,"syntology":null},{"url":null,"slug":"kalman-based-spectro-temporal-ecg-analysis","title":"Kalman-based Spectro-Temporal ECG Analysis using Deep Convolutional Networks for Atrial Fibrillation Detection","date":"2018-12-12","arxiv_id":"1812.05555","repositories_listed":0,"syntology":null},{"url":null,"slug":"lstm-based-ecg-classification-for-continuous","title":"LSTM-Based ECG Classification for Continuous Monitoring on Personal Wearable Devices","date":"2018-12-12","arxiv_id":"1812.04818","repositories_listed":0,"syntology":null},{"url":null,"slug":"classification-of-12-lead-ecg-signals-with-bi","title":"Classification of 12-Lead ECG Signals with Bi-directional LSTM Network","date":"2018-11-05","arxiv_id":"1811.02090","repositories_listed":0,"syntology":null},{"url":null,"slug":"inter-patient-ecg-classification-with","title":"Inter-Patient ECG Classification with Convolutional and Recurrent Neural Networks","date":"2018-09-27","arxiv_id":"1810.04121","repositories_listed":0,"syntology":null},{"url":null,"slug":"a-generative-modeling-approach-to-limited","title":"A Generative Modeling Approach to Limited Channel ECG Classification","date":"2018-02-18","arxiv_id":"1802.06458","repositories_listed":0,"syntology":null},{"url":"/paper/inter-patient-ecg-heartbeat-classification","slug":"inter-patient-ecg-heartbeat-classification","title":"Inter-Patient ECG Heartbeat Classification with Temporal VCG Optimized by PSO","date":"2017-09-05","arxiv_id":null,"repositories_listed":0,"syntology":null}],"record_sha256":"89f33c91df596a73d7c149031af64a6ba84251388360de2875e6988a810724c0","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}