{"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-patient-ecg-heartbeat-classification","title":"Inter-Patient ECG Heartbeat Classification with Temporal VCG Optimized by PSO","arxiv_id":null,"date":"2017-09-05","proceeding":"Scientific Reports 2017 9","authors":["Gabriel Garcia","Gladston Moreira","David Menotti","Eduardo Luz"],"abstract":"Classifying arrhythmias can be a tough task for a human being and automating this task is highly desirable. Nevertheless fully automatic arrhythmia classification through Electrocardiogram (ECG) signals is a challenging task when the inter-patient paradigm is considered. For the inter-patient paradigm, classifiers are evaluated on signals of unknown subjects, resembling the real world scenario. In this work, we explore a novel ECG representation based on vectorcardiogram (VCG), called temporal vectorcardiogram (TVCG), along with a complex network for feature extraction. We also fine-tune the SVM classifier and perform feature selection with a particle swarm optimization (PSO) algorithm. Results for the inter-patient paradigm show that the proposed method achieves the results comparable to state-of-the-art in MIT-BIH database (53% of Positive predictive (+P) for the Supraventricular ectopic beat (S) class and 87.3% of Sensitivity (Se) for the Ventricular ectopic beat (V) class) that TVCG is a richer representation of the heartbeat and that it could be useful for problems involving the cardiac signal and pattern recognition.*\r\n\r\nSource code available from http://www.decom.ufop.br/csilab/site_media/uploads/code/tvcg_pso.zip","url_abs":"https://doi.org/10.1038/s41598-017-09837-3","url_pdf":"https://www.nature.com/articles/s41598-017-09837-3.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":[],"tasks":[{"task_slug":"arrhythmia-detection","task_name":"Arrhythmia Detection"},{"task_slug":"ecg-classification","task_name":"ECG Classification"},{"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":"feature-selection","task_name":"feature selection"}],"methods":[{"method_slug":"svm","method_name":"SVM"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/arrhythmia-detection-on-mit-bih-ar","task":"Arrhythmia Detection","dataset":"MIT-BIH AR","model":"TVCG_PSO","rank_in_archive_order":5,"of":6,"metrics":{"Accuracy (Inter-Patient)":"92.4%"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}