{"about":{"site":"https://codewithpapers.app","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.","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"},"url":"/paper/ecg-heartbeat-classification-a-deep","title":"ECG Heartbeat Classification: A Deep Transferable Representation","arxiv_id":"1805.00794","date":"2018-04-19","proceeding":null,"authors":["Mohammad Kachuee","Shayan Fazeli","Majid Sarrafzadeh"],"abstract":"Electrocardiogram (ECG) can be reliably used as a measure to monitor the\nfunctionality of the cardiovascular system. Recently, there has been a great\nattention towards accurate categorization of heartbeats. While there are many\ncommonalities between different ECG conditions, the focus of most studies has\nbeen classifying a set of conditions on a dataset annotated for that task\nrather than learning and employing a transferable knowledge between different\ntasks. In this paper, we propose a method based on deep convolutional neural\nnetworks for the classification of heartbeats which is able to accurately\nclassify five different arrhythmias in accordance with the AAMI EC57 standard.\nFurthermore, we suggest a method for transferring the knowledge acquired on\nthis task to the myocardial infarction (MI) classification task. We evaluated\nthe proposed method on PhysionNet's MIT-BIH and PTB Diagnostics datasets.\nAccording to the results, the suggested method is able to make predictions with\nthe average accuracies of 93.4% and 95.9% on arrhythmia classification and MI\nclassification, respectively.","url_abs":"http://arxiv.org/abs/1805.00794v2","url_pdf":"http://arxiv.org/pdf/1805.00794v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"ecg-heartbeat-classification-a-deep","repo_url":"https://github.com/CVxTz/ECG_Heartbeat_Classification","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"ecg-heartbeat-classification-a-deep","repo_url":"https://github.com/Drajan/DDxNet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}},{"paper_slug":"ecg-heartbeat-classification-a-deep","repo_url":"https://github.com/Lynda-Starkus/Abnormal_ECG_Myocardial_infraction_cnn","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"ecg-heartbeat-classification-a-deep","repo_url":"https://github.com/MartinTschechne/ML4H2020","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"NOASSERTION"}},{"paper_slug":"ecg-heartbeat-classification-a-deep","repo_url":"https://github.com/atabas/Heartbeat-Classification","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"ecg-heartbeat-classification-a-deep","repo_url":"https://github.com/dave-fernandes/ECGClassifier","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"Apache-2.0"}},{"paper_slug":"ecg-heartbeat-classification-a-deep","repo_url":"https://github.com/jacobmeisel/EE269_Final_Project","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"ecg-heartbeat-classification-a-deep","repo_url":"https://github.com/ljleeworking/4-Heartbeat-Categorization-from-ECG-Signal","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"ecg-heartbeat-classification-a-deep","repo_url":"https://github.com/nlinc1905/dsilt-tsa","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"ecg-heartbeat-classification-a-deep","repo_url":"https://github.com/rgmyr/tf-prosenet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"ecg-heartbeat-classification-a-deep","repo_url":"https://github.com/triarts/ECG-classification","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"ecg-heartbeat-classification-a-deep","repo_url":"https://github.com/triarts/ECG-classification-OLD_VERISON","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"ecg-heartbeat-classification-a-deep","repo_url":"https://github.com/mmontana/ECG-heartbeat-classification","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"arrhythmia-detection","task_name":"Arrhythmia Detection"},{"task_slug":"electrocardiography-ecg","task_name":"Electrocardiography (ECG)"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"heartbeat-classification","task_name":"Heartbeat Classification"},{"task_slug":"myocardial-infarction-detection","task_name":"Myocardial infarction detection"},{"task_slug":"visual-question-answering","task_name":"Visual Question Answering (VQA)"}],"methods":[],"datasets_introduced":[{"slug":"ecg-heartbeat-categorization-dataset","name":"ECG Heartbeat Categorization Dataset","full_name":""}],"methods_introduced":[],"results":[{"leaderboard":"/sota/arrhythmia-detection-on-mit-bih-ar","task":"Arrhythmia Detection","dataset":"MIT-BIH AR","model":"Deep residual CNN","rank_in_archive_order":4,"of":6,"metrics":{"Accuracy (Inter-Patient)":"93.4%"},"uses_additional_data":false},{"leaderboard":"/sota/myocardial-infarction-detection-on-ptb","task":"Myocardial infarction detection","dataset":"PTB dataset, ECG lead II","model":"Deep residual CNN","rank_in_archive_order":2,"of":4,"metrics":{"Accuracy":"95.9%"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1805.00794","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1805.00794"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/nlinc1905/dsilt-tsa","reach":null},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/dave-fernandes/ECGClassifier","reach":{"status":"ok","spdx":"Apache-2.0"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/atabas/Heartbeat-Classification","reach":{"status":"ok"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/ljleeworking/4-Heartbeat-Categorization-from-ECG-Signal","reach":{"status":"ok"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/CVxTz/ECG_Heartbeat_Classification","reach":{"status":"ok","spdx":"MIT"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/mmontana/ECG-heartbeat-classification","reach":{"status":"ok"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/triarts/ECG-classification-OLD_VERISON","reach":{"status":"ok"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/Drajan/DDxNet","reach":{"status":"ok","spdx":"Apache-2.0"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/triarts/ECG-classification","reach":{"status":"ok"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/jacobmeisel/EE269_Final_Project","reach":{"status":"ok"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/MartinTschechne/ML4H2020","reach":{"status":"ok","spdx":"NOASSERTION"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/rgmyr/tf-prosenet","reach":{"status":"ok"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/Lynda-Starkus/Abnormal_ECG_Myocardial_infraction_cnn","reach":{"status":"ok"}}],"summary":{"unverified":2},"by_repo_kind":{"listed":{"samples":2,"ran":0,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"7c9871f6c1da69a6","entry":"get_loader","repo":"Drajan/DDxNet","repo_kind":"listed","path":"utils/data_loader.py","file_url":"https://github.com/Drajan/DDxNet/blob/HEAD/utils/data_loader.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"7c9871f6c1da69a6"}},{"code_sha256_prefix":"86ae527363b9081b","entry":"relu_conv","repo":"Drajan/DDxNet","repo_kind":"listed","path":"model/ddxnet_model.py","file_url":"https://github.com/Drajan/DDxNet/blob/HEAD/model/ddxnet_model.py","link_basis":"harvester_set","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"86ae527363b9081b"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}