{"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-classification-system-for-arrhythmia","title":"ECG Classification System for Arrhythmia Detection Using Convolutional Neural Networks","arxiv_id":"2303.03660","date":"2023-03-07","proceeding":null,"authors":["Aryan odugoudar","Jaskaran Singh Walia"],"abstract":"Arrhythmia is just one of the many cardiovascular illnesses that have been extensively studied throughout the years. Using multi-lead ECG data, this research describes a deep learning (DL) pipeline technique based on convolutional neural network (CNN) algorithms to detect cardiovascular lar arrhythmia in patients. The suggested model architecture has hidden layers with a residual block in addition to the input and output layers. In this study, the classification of the ECG signals into five main groups, namely: Left Bundle Branch Block (LBBB), Right Bundle Branch Block (RBBB), Atrial Premature Contraction (APC), Premature Ventricular Contraction (PVC), and Normal Beat (N), are performed. Using the MIT-BIH arrhythmia dataset, we assessed the suggested technique. The findings show that our suggested strategy classified 15,000 cases with a high accuracy of 98.2%","url_abs":"https://arxiv.org/abs/2303.03660v2","url_pdf":"https://arxiv.org/pdf/2303.03660v2.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-classification-system-for-arrhythmia","repo_url":"https://github.com/karanwxliaa/Arrythmia-Detection-using-ECG","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"arrhythmia-detection","task_name":"Arrhythmia Detection"},{"task_slug":"ecg-classification","task_name":"ECG Classification"}],"methods":[{"method_slug":"convolution","method_name":"Convolution"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}