{"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/vfpred-a-fusion-of-signal-processing-and","title":"VFPred: A Fusion of Signal Processing and Machine Learning techniques in Detecting Ventricular Fibrillation from ECG Signals","arxiv_id":"1807.02684","date":"2018-07-07","proceeding":null,"authors":["Nabil Ibtehaz","M. Saifur Rahman","M. Sohel Rahman"],"abstract":"Ventricular Fibrillation (VF), one of the most dangerous arrhythmias, is\nresponsible for sudden cardiac arrests. Thus, various algorithms have been\ndeveloped to predict VF from Electrocardiogram (ECG), which is a binary\nclassification problem. In the literature, we find a number of algorithms based\non signal processing, where, after some robust mathematical operations the\ndecision is given based on a predefined threshold over a single value. On the\nother hand, some machine learning based algorithms are also reported in the\nliterature; however, these algorithms merely combine some parameters and make a\nprediction using those as features. Both the approaches have their perks and\npitfalls; thus our motivation was to coalesce them to get the best out of the\nboth worlds. Hence we have developed, VFPred that, in addition to employing a\nsignal processing pipeline, namely, Empirical Mode Decomposition and Discrete\nTime Fourier Transform for useful feature extraction, uses a Support Vector\nMachine for efficient classification. VFPred turns out to be a robust algorithm\nas it is able to successfully segregate the two classes with equal confidence\n(Sensitivity = 99.99%, Specificity = 98.40%) even from a short signal of 5\nseconds long, whereas existing works though requires longer signals, flourishes\nin one but fails in the other.","url_abs":"http://arxiv.org/abs/1807.02684v3","url_pdf":"http://arxiv.org/pdf/1807.02684v3.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":"vfpred-a-fusion-of-signal-processing-and","repo_url":"https://github.com/robin-0/VFPred","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"arrhythmia-detection","task_name":"Arrhythmia Detection"},{"task_slug":"binary-classification","task_name":"Binary Classification"},{"task_slug":"electrocardiography-ecg","task_name":"Electrocardiography (ECG)"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"specificity","task_name":"Specificity"},{"task_slug":"ventricular-fibrillation-detection","task_name":"Ventricular fibrillation detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}