[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-290463-105":59,"doc-detail-290463-en":131},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":124,"head_meta":126,"extra_data":128,"updated_unix":130},105,"en","machine-learning-approaches-in-atrial-fibrillation-detection","Machine Learning Approaches in Atrial Fibrillation Detection","","Atrial fibrillation (AF) is a prevalent cardiac arrhythmia marked by rapid, irregular atrial electrical activity that strains healthcare systems due to its association with higher mortality and morbidity. This paper emphasizes the importance of early AF detection and surveys how machine learning (ML) improves both accuracy and efficiency. It reviews methodologies for AF detection using electrocardiogram (ECG) data, covering dataset characteristics, signal lengths, preprocessing strategies, and a range of ML approaches. ",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/machine-learning-approaches-in-atrial-fibrillation-detection/290463/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/machine-learning-approaches-in-atrial-fibrillation-detection/290463.png","ImageObject",300,407,{"name":92,"@type":93},"Violet","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-30","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",14,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"Why is early atrial fibrillation (AF) detection important?","Question",{"text":113,"@type":114},"AF detection is critical because AF is linked to serious health problems such as stroke, heart failure, and increased mortality. Earlier detection supports more accurate and effective management and treatment.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"How does the paper describe AF signals on an ECG?",{"text":118,"@type":114},"The paper states that normal ECG includes a P wave, QRS complex, and T wave. When AF is present, ECG typically shows the absence of a P wave while QRS complex and T waves remain, along with an irregular pattern of R-waves.",{"name":120,"@type":111,"acceptedAnswer":121},"What public databases does the paper highlight for ML-based AF detection?",{"text":122,"@type":114},"The paper highlights PhysioNet-provided datasets, especially the MIT-BIH Atrial Fibrillation Database (AFDB) and the Computing in Cardiology (CinC) Challenge 2017 Database. It also notes other commonly used resources such as MIT-BIH Arrhythmia (MITDB) and MIT-BIH Normal Sinus Rhythm (NSRDB).","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},290463,1789641740,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":52,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":130,"read_time":144,"preview_page_count":4},4398048950312,"https://ap-avatar.wpscdn.com/avatar/400002538284de19e3c?_k=1778320343897328908","Machine Learning Approaches in Atrial Fibrillation Detection  \nDeividas Butkus, Jolita Bernatavičienė  \nVilnius University, Institute of Data Science and Digital Technologies Akademijos str. 4, Vilnius  \n[deividas.butkus@mif.stud.vu.lt](deividas.butkus@mif.stud.vu.lt)  \nAbstract. Atrial fibrillation (AF) characterized by rapid and irregular electrical activity in the atria represents a prevalent form of cardiac arrhythmia that significantly challenges healthcare systems due to its links to heightened mortality and morbidity rates. Early detection of AF is critical for accurate and effective management and treatment. In response to this pressing need, numerous researchers have used machine learning (ML) to enhance the precision and efficiency of AF detection. By analyzing available datasets, signal lengths, preprocessing techniques, and a diverse array of ML approaches, this paper aims to cover methodologies of AF detection using electrocardiogram (ECG) data and ML.  \nKeywords: atrial fibrillation, machine learning, ECG, artificial intelligence, deep learning  \n1 Introduction  \nAtrial fibrillation (AF) is the most common cardiac arrhythmia seen in clinical settings leading to serious health problems such as stroke, heart failure, and increased mortality rates. The electrocardiogram (ECG) provides a graphical representation of the heart’s electrical activity over time and is the standard diagnostic tool for detecting AF. The normal electrocardiogram in sinus rhythm depicted in Figure 1 comprises a P wave, a QRS complex, anda T wave. The QRS complex is often, but not always, three separate waves: the Q wave, the R wave, and the S wave. When AF is present, ECG usually comprises the absence of a P wave (while the QRS complex and T waves remain present), and an irregular pattern of R-waves.  \nThe diagnosis of AF based on ECG signals requires the expertise of a trained specialist, typically a doctor, making it a time and resource-intensive process. The need for human interpretation poses challenges, including potential delays in diagnosis and limitations in scalability. Given these  \nCopyright © Deividas Butkus, Jolita Bernatavičienė, 2024. Published by Vilnius University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (CC BY), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.  \nDOI: [https://doi.org/10.15388/LMITT.2024.2](https://doi.org/10.15388/LMITT.2024.2)  \nchallenges and the evolving landscape of healthcare technology, there is an increasing recognition of the potential role of machine learning algorithms in AF detection. Understanding nuances of the development of machine learning (ML) algorithms capable of identifying AF from ECG data with high accuracy is crucial for the advancement of automated diagnostic tools. This paper explores various machine learning approaches applied to ECG data for the detection of AF, aiming to systematically present advancements in AF detection in the ML field. Through an examination of different methodologies, from data preprocessing to model selection, this analysis seeks to review the most common strategies for this vital diagnostic task.  \n2 Literature review  \n2.1 Databases  \nMany recent publications in the field of ML for AF detection use public datasets provided by PhysioNet [2], a data repository for biomedical research. Based on the research by [3], the MIT-BIH Atrial Fibrillation Database (AFDB) [4] and the Computing in Cardiology (CinC) Challenge 2017 Database [5] are the two  \n16 / Konferencijos „Lietuvos magistrantų informatikos ir IT tyrimai“ darbai  \nmost popular databases for AF detection, used in around 50 studies out of 147 reviewed, and both provided by PhysioNet. The AFDB database consists of 25 long-term ECG recordings of human subjects with AF, while the CinC Challenge 2017 contains a training set with 8528 single lead ECG recording","cbCaigot1qc5J1vx","https://ap.wps.com/l/cbCaigot1qc5J1vx","pdf",700589,"English","# Introduction\n# Literature review\n## Databases\n## Signal length\n## ECG preprocessing techniques and features extraction","[{\"question\":\"Why is early atrial fibrillation (AF) detection important?\",\"answer\":\"AF detection is critical because AF is linked to serious health problems such as stroke, heart failure, and increased mortality. Earlier detection supports more accurate and effective management and treatment.\"},{\"question\":\"How does the paper describe AF signals on an ECG?\",\"answer\":\"The paper states that normal ECG includes a P wave, QRS complex, and T wave. When AF is present, ECG typically shows the absence of a P wave while QRS complex and T waves remain, along with an irregular pattern of R-waves.\"},{\"question\":\"What public databases does the paper highlight for ML-based AF detection?\",\"answer\":\"The paper highlights PhysioNet-provided datasets, especially the MIT-BIH Atrial Fibrillation Database (AFDB) and the Computing in Cardiology (CinC) Challenge 2017 Database. It also notes other commonly used resources such as MIT-BIH Arrhythmia (MITDB) and MIT-BIH Normal Sinus Rhythm (NSRDB).\"}]","Machine Learning Approaches in Atrial Fibrillation Detection | PDF",25]