[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127235-en":3,"doc-seo-127235-105":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":20,"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},127235,2336475104042,"Skyler","https://ap-avatar.wpscdn.com/avatar/22000c4c32af1715be0?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786537525561427321",7,"Healthcare","A Machine Learning Approach to Automated Localization of Targets for Ventricular Tachycardia Ablation Using Sinus Rhythm Signal Features","Catheter ablation offers an effective treatment for ventricular tachycardia (VT), yet identifying ablation sites still depends heavily on operator judgment. This study proposes a machine learning method that localizes VT ablation targets using signal features derived from intracardiac electrograms recorded during sinus rhythm. In a porcine infarction model, 56 substrate maps and 46 signal features supported a random-forest pipeline validated by cross-validation, achieving ROC-AUC of 77.8%. The approach shows promise for clinician-assisted VT target localization.","A Machine Learning Approach to Automated Localization of Targets for Ventricular Tachycardia Ablation Using Sinus Rhythm Signal Features  \nXuezhe Wang 1, Adam Dennis 1, Tarv Dhanjal2, Pier D Lambiase1,3, Michele Orini 1,4  \n1 Institute of Cardiovascular Science, University College London, United Kingdom  \n2 University of Warwick, Honorary Consultant Cardiologist & Electrophysiologist, University Hospital Coventry & Warwickshire, United Kingdom  \n3 Barts Heart Centre, Barts Health NHS Trust, London, United Kingdom  \n4 School of Biomedical Engineering & Imaging Sciences, King’s College London, United Kingdom  \nAbstract  \nCatheter ablation has the potential to become an effective treatment for ventricular tachycardia (VT), but the current identification of ablation sites relies on the operator 's judgement and experience. This study proposesa novel machine learning approach to identify ablation targets based on signal features derived from intracardiac electrograms recorded in sinus rhythm. 56 substrate maps were collected during pacing and sinus rhythm using amultipolar catheter (Advisor HD grid, Ensite Precision) in 13 pigs with chronic myocardial infarction (n=31,515 mapping points). 36 VTs were induced and critical components of the VT circuit including early-, mid- and late-diastolic signals, were localized. Cardiac sites within 6 mm from these critical VT sites were considered as potential ablation targets (7.3% of all cardiac sites). 46 features representing signal morphology, function, spatial and spectral properties were extracted from each bipolar and unipolar signal recorded during pacing or sinus rhythm. A random forest algorithm was trained on 80% of the data to identify the 20 most important features and 10 times 10-fold cross-validation was used to identify the best model. Validation on the remaining 20% of data showed an area under the ROC curve of 77.8%, and both 70% of sensitivity and specificity, for the best model. This study demonstrates for the first time that machine learning may support clinicians in the localization of targets for VTablation.  \n1. Introduction  \nVentricular tachycardia is a life-threatening cardiac condition, and catheter ablation has the potential of becoming an effective and established treatment [1] . However, often more than half of the patients experience recurrence after the procedure due to the inability to accurately locate the critical points leading to VT during  \nthe electrophysiological study [2 , 3] . A standard approach for the localization of ablation targets is to induce VT and to identify its critical components (early-, mid- and latediastolic pathway) through pacing manoeuvres (e.g. entrainment) or activation mapping. Due to the limitation of being able to only map hemodynamically stable VTs, substrate mapping has been proposed as a safer and potentially more effective alternative. This consists of mapping ventricular electrical activity during sinus rhythm or pacing to identify arrhythmogenic properties closely related to potential components of VT circuits. Various signal metrics derived from substrate mapping have been shown to correlate with critical regions of VT circuit, such as, simultaneous amplitude frequency electrogram transformation [4], decrement evoked potential mapping [5], re-entry vulnerability index [6], but their accuracy remains limited. While machine learning and artificial intelligence are having a strong impact on the analysis of the body surface electrocardiogram (ECG) [7], little is known about their potential to improve targeted VTablation. A recent study has shown that machine learning approaches can be used to accurately identify abnormal ventricular potentials [8], but their use for the identification of critical components of VT circuits remains unexplored. The potential relationship between signal features extracted from intracardiac electrograms (EGMs) and critical components of VT circuits also needs further research. The aim of this stud","cbCaidDeSHWUfsOu","https://ap.wps.com/l/cbCaidDeSHWUfsOu","pdf",374366,1,4,"English","en",105,"# Abstract\n# Introduction\n# Methods\n## Data collection and signal processing\n## Feature extraction and model training\n## Validation","[{\"question\":\"What problem does the study address in VT catheter ablation?\",\"answer\":\"The study addresses the limited ability to accurately identify critical ablation sites during electrophysiological study, which often leads to VT recurrence.\"},{\"question\":\"How are the training data and labels defined in the proposed approach?\",\"answer\":\"Intracardiac electrograms are recorded during sinus rhythm and pacing, substrate maps are collected, and cardiac sites within 6 mm of localized critical VT components are treated as potential ablation targets.\"},{\"question\":\"Which machine learning method is used and how is it validated?\",\"answer\":\"A random forest algorithm is trained using 80% of the data; 10× 10-fold cross-validation selects the best model, and performance is assessed on the remaining 20% using ROC-AUC and sensitivity/specificity.\"}]","A Machine Learning Approach to Automated Localization of Targets for Ventricular Tachycardia Ablation Using Sinus Rhythm Signal Features | PDF",1785937665,10,{"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},"a-machine-learning-approach-to-automated-localization-of-targets-for-ventricular-tachycardia-ablation-using-sinus-rhythm-signal-features","",{"@graph":36,"@context":85},[37,53,68],{"@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/healthcare/",3,{"item":52,"name":13,"@type":43,"position":21},"https://docshare.wps.com/document/a-machine-learning-approach-to-automated-localization-of-targets-for-ventricular-tachycardia-ablation-using-sinus-rhythm-signal-features/127235/",{"url":52,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-24","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the study address in VT catheter ablation?","Question",{"text":75,"@type":76},"The study addresses the limited ability to accurately identify critical ablation sites during electrophysiological study, which often leads to VT recurrence.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How are the training data and labels defined in the proposed approach?",{"text":80,"@type":76},"Intracardiac electrograms are recorded during sinus rhythm and pacing, substrate maps are collected, and cardiac sites within 6 mm of localized critical VT components are treated as potential ablation targets.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning method is used and how is it validated?",{"text":84,"@type":76},"A random forest algorithm is trained using 80% of the data; 10× 10-fold cross-validation selects the best model, and performance is assessed on the remaining 20% using ROC-AUC and sensitivity/specificity.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,118,123,128,131,134],{"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":21,"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":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":116,"slug":117},40,"healthcare",{"id":119,"doc_module":4,"doc_module_name":46,"category_name":120,"show_sort_weight":121,"slug":122},8,"Research & Report",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":29,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":29,"slug":133},"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]