[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125355-en":3,"doc-seo-125355-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},125355,962075114101,"Seraphina","https://ap-avatar.wpscdn.com/avatar/e000253a75eb197efd?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780044092746381165",8,"Research & Report","Statistics and behavior of clinically significant extrapulmonary vein atrial fibrillation sources - machine-learning-enhanced electrographic flow mapping in persistent atrial fibrillation","Machine-learning-enhanced electrographic flow (EGF) mapping is presented as a method to identify clinically significant extrapulmonary vein sources sustaining persistent atrial fibrillation (AF). The EGF model is trained on procedural outcomes from 199 fully anonymized retrospective patient datasets to learn an activity threshold and optimize model hyperparameters. Divergent wavefront patterns reconstructed from 64-electrode basket recordings are quantified for temporal prevalence. The threshold is validated in 85 prospective FLOW-AF trial patients, where persisting post-procedure sources predict higher recurrence and ON-state switching reduces AF cycle-length spatial variability by over 50%, suggesting an entraining effect.","TYPE Original Research PUBLISHED 26 August 2025  \nDOI 10.3389/fcvm.2025.1517484  \nEDITED BY  \nRui Providencia,  \nUniversity College London, United Kingdom  \nREVIEWED BY  \nJames P. Hummel,  \nYale University, United States Cristiano F. Pisani, University of São Paulo, Brazil  \n*CORRESPONDENCE  \nPeter Ruppersberg  \n [pruppersberg@cortexep.com](pruppersberg@cortexep.com)  \nRECEIVED 26 October 2024  \nACCEPTED 03 July 2025  \nPUBLISHED 26 August 2025  \nCITATION  \nRuppersberg P, Castellano S, Haeusser P, Ahapov K, Kong MH, Spitzer SG, Nölker G, Rillig A and Szili-Torok T (2025) Statistics and behavior of clinically significant extrapulmonary vein atrial fibrillation sources:  \nmachine-learning-enhanced electrographic flow mapping in persistent atrial fibrillation. Front. Cardiovasc. Med. 12:1517484 .  \ndoi: 10.3389/fcvm.2025.1517484  \nCOPYRIGHT  \n© 2025 Ruppersberg, Castellano, Haeusser, Ahapov, Kong, Spitzer, Nölker, Rillig and Szili-Torok. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nStatistics and behavior of clinically significant extrapulmonary vein atrial fibrillation sources: machine-learningenhanced electrographic flow mapping in persistent atrial fibrillation  \nPeter Ruppersberg1*, Steven Castellano1, Philip Haeusser1, Kostiantyn Ahapov1, Melissa H. Kong1, Stefan G. Spitzer2,3, Georg Nölker4, Andreas Rillig5 and Tamas Szili-Torok6  \n1Cortex, Inc. Menlo Park, CA, United States, 2Praxisklinik Herz und Gefäße Dresden, Akademische Lehrpraxisklinik der TU Dresden, Dresden, Germany, 3Brandenburg University of Technology Cottbus-Senftenberg, Institute of Medical Technology, Cottbus, Germany, 4Department of Cardiology, Heart and Diabetes Center North Rhine-Westphalia, Ruhr University Bochum, Bad Oyenhausen, Germany, 5Interventional Electrophysiology, University Heart Center, Hamburg, Germany, 6Department of Cardiology, University of Szeged Albert Szent-Györgyi Medical School, Szeged, Hungary  \nIntroduction: Electrographic flow (EGF) mapping is an FDA 510(k)-cleared method for visualizing atrial activation wavefronts in atrial fibrillation (AF) . Its clinical efficacy was demonstrated in the FLOW-AF randomized controlled trial, and its fundamental principles have been previously described. However, the underlying machine learning strategy used to develop and refine the EGF algorithm has not yet been detailed. Here, we present how our EGF Model—trained on procedural outcomes from 199 fully anonymized retrospective patient datasets—identifies clinically significant sources of AF and how this machine learning–driven hyperparameter optimization underlies its clinical effectiveness. We also examine the statistical characteristics of the identified sources and their impact on cycle length variability, offering insights into potential pathophysiological mechanisms.  \nMethods and results: Unipolar electrograms were recorded from patients with persistent or long-standing persistent AF using 64-electrode basket catheters. The EGF Model processes these recordings to reconstruct divergent wavefront propagation patterns and quantify their temporal prevalence. We included 399 retrospective patients in total: 199 for training and optimizing 24 model hyperparameters, and 200 for subsequent analyses of source prevalence and characteristics. Our machine learning approach established an activity threshold, above which divergent wavefront patterns—termed “significant sources” —predicted AF recurrence. This threshold was validated in 85 prospective patients from the published FLOW-AF trial. Significant sources persisting post-procedure were associated with significantly hig","cbCairYyqXPQneTN","https://ap.wps.com/l/cbCairYyqXPQneTN","pdf",4626657,1,11,"English","en",105,"# Introduction\n## Electrographic flow (EGF) mapping and machine learning gap\n## ExtraPV mechanisms in persistent atrial fibrillation\n# Methods and Results\n## Patient datasets and model training\n## Activity threshold and validation in FLOW-AF\n## Clinical recurrence and cycle-length variability effects\n# Conclusions\n## Outcome-based optimization for actionable AF source detection","[{\"question\":\"What does the EGF model learn to identify in persistent atrial fibrillation?\",\"answer\":\"The EGF model learns how to identify clinically significant AF sources by using a machine-learning-driven activity threshold and hyperparameter optimization based on procedural outcomes.\"},{\"question\":\"How was the model validated in prospective patients?\",\"answer\":\"The activity threshold was validated in 85 prospective patients from the published FLOW-AF randomized controlled trial.\"},{\"question\":\"What relationship did significant sources show with AF recurrence and cycle-length variability?\",\"answer\":\"Significant sources persisting after the procedure were associated with higher recurrence rates, and when sources switched ON, spatial variability of AF cycle lengths in the atrium decreased by more than 50%.\"}]","Statistics and behavior of clinically significant extrapulmonary vein atrial fibrillation sources - machine-learning-enhanced electrographic flow mapping in persistent atrial fibrillation | PDF",1785898385,28,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"statistics-and-behavior-of-clinically-significant-extrapulmonary-vein-atrial-fibrillation-sources-machine-learning-enhanced-electrographic-flow-mapping-in-persistent-atrial-fibrillation","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/statistics-and-behavior-of-clinically-significant-extrapulmonary-vein-atrial-fibrillation-sources-machine-learning-enhanced-electrographic-flow-mapping-in-persistent-atrial-fibrillation/125355/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What does the EGF model learn to identify in persistent atrial fibrillation?","Question",{"text":75,"@type":76},"The EGF model learns how to identify clinically significant AF sources by using a machine-learning-driven activity threshold and hyperparameter optimization based on procedural outcomes.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How was the model validated in prospective patients?",{"text":80,"@type":76},"The activity threshold was validated in 85 prospective patients from the published FLOW-AF randomized controlled trial.",{"name":82,"@type":73,"acceptedAnswer":83},"What relationship did significant sources show with AF recurrence and cycle-length variability?",{"text":84,"@type":76},"Significant sources persisting after the procedure were associated with higher recurrence rates, and when sources switched ON, spatial variability of AF cycle lengths in the atrium decreased by more than 50%.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]