{"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/inter-and-intra-patient-ecg-heartbeat","title":"Inter- and intra- patient ECG heartbeat classification for arrhythmia detection: a sequence to sequence deep learning approach","arxiv_id":"1812.07421","date":"2018-12-09","proceeding":"arXiv:1812.07421 2018 12","authors":["Sajad Mousavi","Fatemeh Afghah"],"abstract":"Electrocardiogram (ECG) signal is a common and powerful tool to study heart\nfunction and diagnose several abnormal arrhythmia. While there have been\nremarkable improvements in cardiac arrhythmia classification methods, they\nstill cannot offer an acceptable performance in detecting different heart\nconditions, especially when dealing with imbalanced datasets. In this paper, we\npropose a solution to address this limitation of current classification\napproaches by developing an automatic heartbeat classification method using\ndeep convolutional neural networks and sequence to sequence models. We\nevaluated the proposed method on the MIT-BIH arrhythmia database, considering\nthe intra-patient and inter-patient paradigms, and the AAMI EC57 standard. The\nevaluation results for both paradigms show that our method achieves the best\nperformance in the literature (a positive predictive value of 96.46% and\nsensitivity of 100% for the category S, and a positive predictive value of\n98.68% and sensitivity of 97.40% for the category F for the intra-patient\nscheme; a positive predictive value of 92.57% and sensitivity of 88.94% for the\ncategory S, and a positive predictive value of 99.50% and sensitivity of 99.94%\nfor the category V for the inter-patient scheme.)","url_abs":"http://arxiv.org/abs/1812.07421v1","url_pdf":"http://arxiv.org/pdf/1812.07421v1.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":"inter-and-intra-patient-ecg-heartbeat","repo_url":"https://github.com/SajadMo/ECG-Heartbeat-Classification-seq2seq-model","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"inter-and-intra-patient-ecg-heartbeat","repo_url":"https://github.com/SSajadM/ECG-Heartbeat-Classification-seq2seq-model","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"inter-and-intra-patient-ecg-heartbeat","repo_url":"https://github.com/SajadMo/SleepEEGNet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}}],"tasks":[{"task_slug":"arrhythmia-detection","task_name":"Arrhythmia Detection"},{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"heartbeat-classification","task_name":"Heartbeat Classification"},{"task_slug":"sensitivity","task_name":"Sensitivity"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/arrhythmia-detection-on-mit-bih-ar","task":"Arrhythmia Detection","dataset":"MIT-BIH AR","model":"BiRNN","rank_in_archive_order":1,"of":6,"metrics":{"Accuracy (Inter-Patient)":"99.53%","Accuracy (Intra-Patient)":"99.92%"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}