[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124412-en":3,"doc-seo-124412-105":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},124412,1374391974585,"Genevieve","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",7,"Healthcare","Machine learning algorithms for predicting atrial fibrillation using single-lead data derived from","Machine learning algorithms are applied to predict atrial fibrillation using single-lead ECG data. The material reviews model development and evaluation using multiple performance metrics, including AUC values reported for ROC analysis. Experimental setup compares different learning approaches and discusses the contribution of single-lead signals to predictive capability. Findings emphasize the potential of algorithmic ECG interpretation to support earlier identification of atrial fibrillation risk in clinical workflows.","TYP􀀚 O􀀏􀀃g􀀃􀀍􀀂􀀅 R􀀉􀀄􀀉􀀂􀀏􀀛􀀔 PUBLIS􀀞􀀚D 07 O􀀛􀀐􀀑b􀀉􀀏 2025 DOI 10.3389/􀀒􀀛􀀎􀀙 .2025.1612750  \n􀀁􀀂􀀃􀀄􀀁􀀂 􀀆􀀇  \n􀀁􀀂􀀃􀀄􀀂􀀅 􀀇􀀈􀀉􀀊􀀋  \n􀀌􀀍􀀃􀀎􀀉􀀏􀀄􀀃􀀐􀀈 􀀑􀀒 􀀓􀀑􀀏􀀐􀀔 􀀕􀀂􀀏􀀑􀀅􀀃􀀍􀀂 􀀂􀀐 􀀕􀀔􀀂􀀖􀀉􀀅 􀀗􀀃􀀅􀀅􀀋􀀌􀀍􀀃􀀐􀀉􀀊 􀀇􀀐􀀂􀀐􀀉􀀄  \n􀀈􀀁􀀉􀀃􀀁􀀊􀀁􀀂 􀀆􀀇  \n􀀘􀀂􀀄􀀄􀀃􀀙􀀑 􀀇􀀂􀀅􀀎􀀃􀀋  \n􀀚􀀑􀀅􀀈􀀐􀀉􀀛􀀔􀀍􀀃􀀛 􀀌􀀍􀀃􀀎􀀉􀀏􀀄􀀃􀀐􀀈 􀀑􀀒 􀀜􀀝􀀏􀀃􀀍􀀋 􀀞􀀐􀀂􀀅􀀈􀀗􀀂􀀑 􀀃􀀂􀀍g􀀋  \n􀀗􀀝􀀍􀀂􀀍 􀀌􀀍􀀃􀀎􀀉􀀏􀀄􀀃􀀐􀀈 􀀑􀀒 􀀕􀀔􀀃􀀍􀀉􀀄􀀉 􀀘􀀉􀀊􀀃􀀛􀀃􀀍􀀉􀀋 􀀕􀀔􀀃􀀍􀀂  \n*􀀋􀀌􀀈􀀈􀀁􀀍􀀎􀀌􀀏􀀂􀀁􀀏􀀋􀀁  \nK􀀈􀀑􀀝􀀍g-􀀘􀀃􀀍 􀀚􀀂􀀏k  \n k􀀈􀀑􀀝􀀍g􀀙􀀃􀀍 . 􀀖􀀂􀀏k@􀀄􀀂􀀙􀀄􀀝􀀍g.􀀛􀀑􀀙  \n†􀀜􀀔􀀉􀀄􀀉 􀀂􀀝􀀐􀀔􀀑􀀏􀀄 􀀔􀀂􀀎􀀉 􀀛􀀑􀀍􀀐􀀏􀀃b􀀝􀀐􀀉􀀊 􀀉q􀀝􀀂􀀅􀀅􀀈 􀀐􀀑􀀐􀀔􀀃􀀄 w􀀑􀀏k  \n􀀈􀀁􀀋􀀁􀀃􀀉􀀁􀀂 16 A􀀖􀀏􀀃􀀅 2025  \n􀀐􀀋􀀋􀀁􀀎􀀄􀀁􀀂 08 􀀇􀀉􀀖􀀐􀀉􀀙b􀀉􀀏 2025  \n􀀎􀀑􀀆􀀒􀀃􀀍􀀓􀀁􀀂 07 O􀀛􀀐􀀑b􀀉􀀏 2025  \n􀀋􀀃􀀄􀀐􀀄􀀃􀀌􀀏  \n􀀕􀀔􀀑􀀃 J-􀀗􀀋 􀀇􀀑􀀍g 􀀇-􀀗􀀋 K􀀃􀀙 J􀀋 J􀀉􀀑􀀍 J􀀋 W􀀑􀀑 K􀀋􀀕􀀔􀀑 􀀇J􀀋 􀀚􀀂􀀏k 􀀇-J􀀋 O􀀍 Y K􀀋 K􀀃􀀙 JY 􀀂􀀍􀀊 􀀚􀀂􀀏k K-􀀘(2025) 􀀘􀀂􀀛􀀔􀀃􀀍􀀉 􀀅􀀉􀀂􀀏􀀍􀀃􀀍g 􀀂􀀅g􀀑􀀏􀀃􀀐􀀔􀀙􀀄 􀀒􀀑􀀏􀀖􀀏􀀉􀀊􀀃􀀛􀀐􀀃􀀍g 􀀂􀀐􀀏􀀃􀀂􀀅 fib􀀏􀀃􀀅􀀅􀀂􀀐􀀃􀀑􀀍 􀀝􀀄􀀃􀀍g 􀀄􀀃􀀍g􀀅􀀉-􀀅􀀉􀀂􀀊􀀊􀀂􀀐􀀂 􀀊􀀉􀀏􀀃􀀎􀀉􀀊 􀀒􀀏􀀑􀀙 12-􀀅􀀉􀀂􀀊 E􀀕G􀀄 .  \n􀀁􀀏􀀑􀀍􀀐 . 􀀕􀀂􀀏􀀊􀀃􀀑􀀎􀀂􀀄􀀛 . 􀀘􀀉􀀊 . 12:1612750 .􀀊􀀑􀀃: 10.3389/􀀒􀀛􀀎􀀙 .2025.1612750  \n􀀋􀀌􀀎􀀇􀀈􀀃􀀔􀀓􀀄  \n© 2025 􀀕􀀔􀀑􀀃􀀋 􀀇􀀑􀀍g􀀋 K􀀃􀀙􀀋 J􀀉􀀑􀀍􀀋 W􀀑􀀑􀀋 􀀕􀀔􀀑􀀋􀀚􀀂􀀏k􀀋 O􀀍􀀋 K􀀃􀀙 􀀂􀀍􀀊 􀀚􀀂􀀏k. 􀀜􀀔􀀃􀀄 􀀃􀀄 􀀂􀀍 􀀑􀀖􀀉􀀍-􀀂􀀛􀀛􀀉􀀄􀀄􀀂􀀏􀀐􀀃􀀛􀀅􀀉 􀀊􀀃􀀄􀀐􀀏􀀃b􀀝􀀐􀀉􀀊 􀀝􀀍􀀊􀀉􀀏 􀀐􀀔􀀉 􀀐􀀉􀀏􀀙􀀄 􀀑􀀒 􀀐􀀔􀀉  \n􀀕􀀏􀀉􀀂􀀐􀀃􀀎􀀉 􀀕􀀑􀀙􀀙􀀑􀀍􀀄 A􀀐􀀐􀀏􀀃b􀀝􀀐􀀃􀀑􀀍 􀀃􀀛􀀉􀀍􀀄􀀉 (􀀕􀀕 BY) . 􀀜􀀔􀀉 􀀝􀀄􀀉􀀋 􀀊􀀃􀀄􀀐􀀏􀀃b􀀝􀀐􀀃􀀑􀀍 􀀑􀀏 􀀏􀀉􀀖􀀏􀀑􀀊􀀝􀀛􀀐􀀃􀀑􀀍 􀀃􀀍􀀑􀀐􀀔􀀉􀀏 􀀒􀀑􀀏􀀝􀀙􀀄 􀀃􀀄 􀀖􀀉􀀏􀀙􀀃􀀐􀀐􀀉􀀊􀀋 􀀖􀀏􀀑􀀎􀀃􀀊􀀉􀀊 􀀐􀀔􀀉􀀑􀀏􀀃g􀀃􀀍􀀂􀀅 􀀂􀀝􀀐􀀔􀀑􀀏(􀀄) 􀀂􀀍􀀊 􀀐􀀔􀀉 􀀛􀀑􀀖􀀈􀀏􀀃g􀀔􀀐 􀀑w􀀍􀀉􀀏(􀀄)􀀂􀀏􀀉 􀀛􀀏􀀉􀀊􀀃􀀐􀀉􀀊 􀀂􀀍􀀊 􀀐􀀔􀀂􀀐 􀀐􀀔􀀉 􀀑􀀏􀀃g􀀃􀀍􀀂􀀅 􀀖􀀝b􀀅􀀃􀀛􀀂􀀐􀀃􀀑􀀍􀀃􀀍 􀀐􀀔􀀃􀀄 j􀀑􀀝􀀏􀀍􀀂􀀅 􀀃􀀄 􀀛􀀃􀀐􀀉􀀊􀀋 􀀃􀀍 􀀂􀀛􀀛􀀑􀀏􀀊􀀂􀀍􀀛􀀉 w􀀃􀀐􀀔􀀂􀀛􀀛􀀉􀀖􀀐􀀉􀀊 􀀂􀀛􀀂􀀊􀀉􀀙􀀃􀀛 􀀖􀀏􀀂􀀛􀀐􀀃􀀛􀀉 . 􀀓􀀑 􀀝􀀄􀀉􀀋􀀊􀀃􀀄􀀐􀀏􀀃b􀀝􀀐􀀃􀀑􀀍 􀀑􀀏 􀀏􀀉􀀖􀀏􀀑􀀊􀀝􀀛􀀐􀀃􀀑􀀍 􀀃􀀄 􀀖􀀉􀀏􀀙􀀃􀀐􀀐􀀉􀀊 w􀀔􀀃􀀛􀀔 􀀊􀀑􀀉􀀄 􀀍􀀑􀀐 􀀛􀀑􀀙􀀖􀀅􀀈 w􀀃􀀐􀀔 􀀐􀀔􀀉􀀄􀀉 􀀐􀀉􀀏􀀙􀀄 .  \n􀀁􀀂􀀃􀀄􀀅􀀆􀀇 􀀉􀀇􀀂􀀊􀀆􀀅􀀆􀀋 􀀂􀀉􀀋􀀌􀀊􀀅􀀍􀀄􀀎􀀏 􀀐􀀌􀀊􀀑􀀊􀀇􀀒􀀅􀀃􀀍􀀅􀀆􀀋 􀀂􀀍􀀊􀀅􀀂􀀉 􀀓􀀔􀀊􀀅􀀉􀀉􀀂􀀍􀀅􀀌􀀆 􀀕􀀏􀀅􀀆􀀋􀀏􀀅􀀆􀀋􀀉􀀇􀀖􀀉􀀇􀀂􀀒 􀀒􀀂􀀍􀀂 􀀒􀀇􀀊􀀅􀀗􀀇􀀒 􀀐􀀊􀀌􀀎􀀘􀀙􀀖􀀉􀀇􀀂􀀒 􀀚􀀛􀀜􀀏  \n􀀝􀀅􀀖􀀞􀀌􀀌􀀆 􀀛􀀄􀀌􀀅 􀀘 , S􀀕􀀆􀀋􀀖􀀞􀀇􀀇 S􀀌􀀆􀀋 􀀙 , 􀀝􀀌􀀆􀀋w􀀌􀀌 K􀀅􀀎 􀀙, 􀀝􀀂􀀇􀀞􀀕 􀀝􀀇􀀌􀀆 3, Ky􀀕􀀆􀀋􀀛􀀄􀀂􀀆􀀋 W􀀌􀀌3, S􀀌􀀌 􀀝􀀅􀀆 􀀛􀀄􀀌4, S􀀇􀀕􀀆􀀋􀀖􀀝􀀕􀀆􀀋 P􀀂􀀊k5, Y􀀌􀀕􀀆􀀋 K􀀇􀀕􀀆 O􀀆 5, 􀀝􀀕 Y􀀌􀀕􀀆 K􀀅􀀎 5 􀀂􀀆􀀒 Ky􀀌􀀕􀀆􀀋􀀖􀀁􀀅􀀆 P􀀂􀀊k5*  \n1 D􀀃􀀎􀀃􀀄􀀃􀀑􀀍 􀀑􀀒 􀀕􀀂􀀏􀀊􀀃􀀑􀀅􀀑g􀀈􀀋 D􀀉􀀖􀀂􀀏􀀐􀀙􀀉􀀍􀀐 􀀑􀀒 􀀞􀀍􀀐􀀉􀀏􀀍􀀂􀀅 􀀘􀀉􀀊􀀃􀀛􀀃􀀍􀀉􀀋 K􀀑􀀍k􀀝k 􀀌􀀍􀀃􀀎􀀉􀀏􀀄􀀃􀀐􀀈 􀀘􀀉􀀊􀀃􀀛􀀂􀀅 􀀕􀀉􀀍􀀐􀀉􀀏􀀋 K􀀑􀀍k􀀝k 􀀌􀀍􀀃􀀎􀀉􀀏􀀄􀀃􀀐􀀈 􀀇􀀛􀀔􀀑􀀑􀀅 􀀑􀀒 􀀘􀀉􀀊􀀃􀀛􀀃􀀍􀀉􀀋 􀀇􀀉􀀑􀀝􀀅􀀋 R􀀉􀀖􀀝b􀀅􀀃􀀛 􀀑􀀒 K􀀑􀀏􀀉􀀂􀀋 2W􀀉􀀅􀀅􀀈􀀄􀀃􀀄 􀀕􀀑􀀏􀀖 . 􀀋 􀀇􀀉􀀑􀀝􀀅􀀋 R􀀉􀀖􀀝b􀀅􀀃􀀛 􀀑􀀒 K􀀑􀀏􀀉􀀂􀀋 3􀀘􀀉􀀊􀀃􀀁􀀂􀀏􀀙􀀇􀀑􀀒􀀐 􀀕􀀑 . 􀀋 􀀐􀀊 . 􀀋 􀀇􀀉􀀑􀀝􀀅􀀋 R􀀉􀀖􀀝b􀀅􀀃􀀛 􀀑􀀒 K􀀑􀀏􀀉􀀂􀀋 4􀀕􀀉􀀍􀀐􀀉􀀏 􀀒􀀑􀀏 􀀗􀀉􀀂􀀅􀀐􀀔 􀀚􀀏􀀑􀀙􀀑􀀐􀀃􀀑􀀍􀀋 􀀇􀀂􀀙􀀄􀀝􀀍g 􀀘􀀉􀀊􀀃􀀛􀀂􀀅􀀕􀀉􀀍􀀐􀀉􀀏􀀋 􀀇􀀝􀀍gk􀀈􀀝􀀍kw􀀂􀀍 􀀌􀀍􀀃􀀎􀀉􀀏􀀄􀀃􀀐􀀈 􀀇􀀛􀀔􀀑􀀑􀀅 􀀑􀀒 􀀘􀀉􀀊􀀃􀀛􀀃􀀍􀀉􀀋 􀀇􀀉􀀑􀀝􀀅􀀋 R􀀉􀀖􀀝b􀀅􀀃􀀛 􀀑􀀒 K􀀑􀀏􀀉􀀂􀀋 5 D􀀃􀀎􀀃􀀄􀀃􀀑􀀍 􀀑􀀒􀀕􀀂􀀏􀀊􀀃􀀑􀀅􀀑g􀀈􀀋 D􀀉􀀖􀀂􀀏􀀐􀀙􀀉􀀍􀀐 􀀑􀀒 􀀘􀀉􀀊􀀃􀀛􀀃􀀍􀀉􀀋 􀀗􀀉􀀂􀀏􀀐 V􀀂􀀄􀀛􀀝􀀅􀀂􀀏 􀀇􀀐􀀏􀀑k􀀉 􀀞􀀍􀀄􀀐􀀃􀀐􀀝􀀐􀀉􀀋 􀀇􀀂􀀙􀀄􀀝􀀍g 􀀘􀀉􀀊􀀃􀀛􀀂􀀅 􀀕􀀉􀀍􀀐􀀉􀀏􀀋􀀇􀀝􀀍gk􀀈􀀝􀀍kw􀀂􀀍 􀀌􀀍􀀃􀀎􀀉􀀏􀀄􀀃􀀐􀀈 􀀇􀀛􀀔􀀑􀀑􀀅 􀀑􀀒 􀀘􀀉􀀊􀀃􀀛􀀃􀀍􀀉􀀋 􀀇􀀉􀀑􀀝􀀅􀀋 R􀀉􀀖􀀝b􀀅􀀃􀀛 􀀑􀀒 K􀀑􀀏􀀉􀀂  \n􀀆􀀕􀀖􀀗􀀘􀀙􀀚􀀛􀀜􀀝􀀞 W􀀉􀀂􀀏􀀂b􀀅􀀉 􀀉􀀅􀀉􀀛􀀐􀀏􀀑􀀛􀀂􀀏􀀊􀀃􀀑g􀀏􀀂􀀙 (E􀀕G) 􀀙􀀑􀀍􀀃􀀐􀀑􀀏􀀃􀀍g 􀀊􀀉􀀎􀀃􀀛􀀉􀀄 􀀐􀀔􀀂􀀐 􀀝􀀐􀀃􀀅􀀃z􀀉􀀄􀀃􀀍g􀀅􀀉-􀀅􀀉􀀂􀀊 E􀀕G 􀀐􀀉􀀛􀀔􀀍􀀑􀀅􀀑g􀀈 􀀔􀀂􀀎􀀉 b􀀉􀀛􀀑􀀙􀀉 􀀎􀀂􀀅􀀝􀀂b􀀅􀀉 􀀐􀀑􀀑􀀅􀀄 􀀒􀀑􀀏 􀀃􀀊􀀉􀀍􀀐􀀃􀀒􀀈􀀃􀀍g 􀀖􀀂􀀏􀀑x􀀈􀀄􀀙􀀂􀀅 􀀂􀀐􀀏􀀃􀀂􀀅 fib􀀏􀀃􀀅􀀅􀀂􀀐􀀃􀀑􀀍 (A􀀁) . 􀀜􀀔􀀃􀀄 􀀄􀀐􀀝􀀊􀀈 􀀂􀀃􀀙􀀉􀀊 􀀐􀀑 􀀊􀀉􀀎􀀉􀀅􀀑􀀖 􀀂 􀀙􀀂􀀛􀀔􀀃􀀍􀀉􀀅􀀉􀀂􀀏􀀍􀀃􀀍g (􀀘 ) 􀀂􀀅g􀀑􀀏􀀃􀀐􀀔􀀙 􀀐􀀑 􀀖􀀏􀀉􀀊􀀃􀀛􀀐 􀀍􀀉w-􀀑􀀍􀀄􀀉􀀐 A􀀁 b􀀈 􀀐􀀏􀀂􀀃􀀍􀀃􀀍g 􀀃􀀐 􀀑􀀍 􀀄􀀃􀀍g􀀅􀀉-􀀅􀀉􀀂􀀊􀀊􀀂􀀐􀀂 􀀉x􀀐􀀏􀀂􀀛􀀐􀀉􀀊 􀀒􀀏􀀑􀀙 12-􀀅􀀉􀀂􀀊 E􀀕G 􀀏􀀉􀀛􀀑􀀏􀀊􀀃􀀍g􀀄 .  \neth􀀚􀀝s 􀀕􀀜􀀝 􀀙es􀀛lts􀀞 􀀚􀀂􀀐􀀃􀀉􀀍􀀐􀀄 w􀀔􀀑 􀀝􀀍􀀊􀀉􀀏w􀀉􀀍􀀐 12-􀀅􀀉􀀂􀀊 E􀀕G b􀀉􀀐w􀀉􀀉􀀍 J􀀂􀀍􀀝􀀂􀀏􀀈 2010 􀀂􀀍􀀊 D􀀉􀀛􀀉􀀙b􀀉􀀏 2021 w􀀉􀀏􀀉 􀀛􀀅􀀂􀀄􀀄􀀃fi􀀉􀀊 􀀃􀀍􀀐􀀑 􀀐w􀀑 g􀀏􀀑􀀝􀀖􀀄 b􀀂􀀄􀀉􀀊 􀀑􀀍 􀀂 􀀏􀀉􀀎􀀃􀀉w 􀀑􀀒 􀀐􀀔􀀉􀀃􀀏 􀀙􀀉􀀊􀀃􀀛􀀂􀀅 􀀏􀀉􀀛􀀑􀀏􀀊􀀄 􀀂􀀍􀀊 􀀊􀀃􀀂g􀀍􀀑􀀄􀀐􀀃􀀛 􀀛􀀑􀀊􀀉􀀄: 􀀐􀀔􀀉 A􀀁 g􀀏􀀑􀀝􀀖 􀀂􀀍􀀊 􀀐􀀔􀀉 􀀍􀀑􀀏􀀙􀀂􀀅 g􀀏􀀑􀀝􀀖 . A􀀍 􀀘 􀀙􀀑􀀊􀀉􀀅 w􀀂􀀄 􀀛􀀏􀀉􀀂􀀐􀀉􀀊 􀀝􀀄􀀃􀀍g 􀀄􀀃􀀍g􀀅􀀉-􀀅􀀉􀀂􀀊 E􀀕G 􀀊􀀂􀀐􀀂􀀋 􀀉x􀀛􀀅􀀝􀀊􀀃􀀍g 􀀐􀀔􀀏􀀉􀀉􀀂􀀝g􀀙􀀉􀀍􀀐􀀉􀀊 􀀅􀀉􀀂􀀊􀀄􀀋 􀀂􀀍􀀊 􀀃􀀍􀀛􀀑􀀏􀀖􀀑􀀏􀀂􀀐􀀃􀀍g 60 􀀛􀀂􀀅􀀛􀀝􀀅􀀂􀀐􀀉􀀊 􀀄􀀐􀀂􀀐􀀃􀀄􀀐􀀃􀀛􀀂􀀅 􀀎􀀂􀀏􀀃􀀂b􀀅􀀉􀀄 􀀒􀀑􀀏􀀉􀀂􀀛􀀔 􀀑􀀒 􀀐􀀔􀀉 􀀏􀀉􀀙􀀂􀀃􀀍􀀃􀀍g 􀀄􀀃􀀍g􀀅􀀉 􀀅􀀉􀀂􀀊􀀄 . 􀀜􀀔􀀉 􀀙􀀑􀀊􀀉􀀅’􀀄 􀀖􀀉􀀏􀀒􀀑􀀏􀀙􀀂􀀍􀀛􀀉 w􀀂􀀄 􀀂􀀄􀀄􀀉􀀄􀀄􀀉􀀊􀀝􀀄􀀃􀀍g 􀀄􀀉􀀎􀀉􀀏􀀂􀀅 􀀙􀀉􀀐􀀏􀀃􀀛􀀄􀀋 􀀃􀀍􀀛􀀅􀀝􀀊􀀃􀀍g 􀀐􀀔􀀉 􀀂􀀏􀀉􀀂 􀀝􀀍􀀊􀀉􀀏 􀀐􀀔􀀉 􀀏􀀉􀀛􀀉􀀃􀀎􀀉􀀏 􀀑􀀖􀀉􀀏􀀂􀀐􀀃􀀍g 􀀛􀀔􀀂􀀏􀀂􀀛􀀐􀀉􀀏􀀃􀀄􀀐􀀃􀀛 􀀛􀀝􀀏􀀎􀀉 (A􀀌RO􀀕)􀀋 􀀄􀀉􀀍􀀄􀀃􀀐􀀃􀀎􀀃􀀐􀀈􀀋 􀀄􀀖􀀉􀀛􀀃fi􀀛􀀃􀀐􀀈􀀋 􀀂􀀛􀀛􀀝􀀏􀀂􀀛􀀈􀀋 􀀂􀀍􀀊 􀀁1 􀀄􀀛􀀑􀀏􀀉 . W􀀉 􀀐􀀏􀀂􀀃􀀍􀀉􀀊 􀀐􀀔􀀉 􀀘 􀀙􀀑􀀊􀀉􀀅 􀀑􀀍 248􀀋612 E􀀕G􀀄 􀀛􀀑􀀅􀀅􀀉􀀛􀀐􀀉􀀊 􀀒􀀏􀀑􀀙 106􀀋606 􀀖􀀂􀀐􀀃􀀉􀀍􀀐􀀄􀀋􀀑􀀒 w􀀔􀀑􀀙 11􀀋810 􀀔􀀂􀀊 􀀊􀀉fi􀀍􀀃􀀐􀀉 A􀀁 . A􀀙􀀑􀀍g 􀀐􀀔􀀉 􀀄􀀃􀀍g􀀅􀀉-􀀅􀀉􀀂􀀊 􀀙􀀂􀀛􀀔􀀃􀀍􀀉 􀀅􀀉􀀂􀀏􀀍􀀃􀀍g 􀀙􀀑􀀊􀀉􀀅􀀄 􀀊􀀉􀀎􀀉􀀅􀀑􀀖􀀉􀀊 􀀒􀀏􀀑􀀙 􀀉􀀂􀀛􀀔 􀀑􀀒 􀀐􀀔􀀉 􀀍􀀃􀀍􀀉 􀀃􀀍􀀊􀀃􀀎􀀃􀀊􀀝􀀂􀀅 􀀅􀀉􀀂􀀊􀀄􀀋 􀀅􀀉􀀂􀀊􀀞 􀀊􀀉􀀙􀀑􀀍􀀄􀀐􀀏􀀂􀀐􀀉􀀊 􀀐􀀔􀀉 b􀀉􀀄􀀐 􀀖􀀉􀀏􀀒􀀑􀀏􀀙􀀂􀀍􀀛􀀉 . 􀀜􀀔􀀉 A􀀌RO􀀕 􀀑􀀒 􀀐􀀔􀀉 􀀄􀀃􀀍g􀀅􀀉-􀀅􀀉􀀂􀀊 E􀀕G 􀀘􀀙􀀑􀀊􀀉􀀅 􀀝􀀄􀀃􀀍g 􀀅􀀉􀀂􀀊 􀀞 w􀀂􀀄 0.801􀀋 w􀀔􀀃􀀅􀀉 􀀐􀀔􀀉 A􀀌RO􀀕 􀀑􀀒 􀀐􀀔􀀉 12-􀀅􀀉􀀂􀀊 E􀀕G 􀀘􀀙􀀑􀀊􀀉􀀅 w􀀂􀀄 0 .816.  \n􀀋􀀚􀀜􀀖l􀀛si􀀚􀀜􀀞 􀀜􀀔􀀉 􀀄􀀃􀀍g􀀅􀀉-􀀅􀀉􀀂􀀊 E􀀕G 􀀘 􀀙􀀑􀀊􀀉􀀅 􀀔􀀂􀀄 􀀄􀀔􀀑w􀀍 􀀖􀀏􀀑􀀙􀀃􀀄􀀉 􀀃􀀍 􀀖􀀏􀀉􀀊􀀃􀀛􀀐􀀃􀀍g 􀀍􀀉w-􀀑􀀍􀀄􀀉􀀐 􀀂􀀐􀀏􀀃􀀂􀀅 fib􀀏􀀃􀀅􀀅􀀂􀀐􀀃􀀑􀀍 (A􀀁)􀀋 􀀖􀀂􀀏􀀐􀀃􀀛􀀝􀀅􀀂􀀏􀀅􀀈 w􀀃􀀐􀀔 􀀅􀀉􀀂􀀊 􀀞 . 􀀞􀀐􀀄 􀀖􀀉􀀏􀀒􀀑􀀏􀀙􀀂􀀍􀀛􀀉 􀀃􀀄􀀛􀀑􀀙􀀖􀀂􀀏􀀂b􀀅􀀉 􀀐􀀑 􀀐􀀔􀀂􀀐 􀀑􀀒 􀀐􀀔􀀉 12-􀀅􀀉􀀂􀀊 􀀙􀀑􀀊􀀉􀀅 .  \nK􀀁􀀇􀀊􀀌􀀈􀀂􀀍  \n􀀕t􀀙i􀀕l fib􀀙ill􀀕ti􀀚􀀜, ele􀀖t􀀙􀀚􀀖􀀕􀀙􀀝i􀀚􀀘􀀙􀀕m, 􀀕􀀙tifi􀀖i􀀕l i􀀜telli􀀘e􀀜􀀖e, m􀀕􀀖hi􀀜e le􀀕􀀙􀀜i􀀜􀀘, si􀀜􀀘le-le􀀕􀀝, we􀀕􀀙􀀕ble 􀀝evi􀀖es, p􀀙e􀀝i􀀖ti􀀚􀀜  \nI􀀆􀀍􀀊􀀌􀀒􀀕􀀃􀀍􀀅􀀌􀀆  \n􀀁􀀂􀀃􀀄􀀅􀀆 􀀈􀀉􀀃􀀄􀀆􀀆􀀅􀀂􀀄􀀊􀀋 􀀌􀀁􀀍􀀎 􀀄􀀏 􀀅 􀀐􀀊􀀑􀀑􀀊􀀋 􀀅􀀃􀀃􀀒􀀓􀀂􀀒􀀑􀀄􀀅 􀀂􀀒􀀅􀀂 􀀄􀀋􀀐􀀃􀀔􀀅􀀏􀀔􀀏 􀀂􀀒􀀔 􀀃􀀄􀀏􀀕􀀏 􀀊􀀖 􀀏􀀂􀀃􀀊􀀕􀀔 􀀅􀀋􀀗􀀒􀀔􀀅􀀃􀀂 􀀖􀀅􀀄􀀆􀀘􀀃􀀔 􀀅􀀋􀀗 􀀄􀀑􀀙􀀊􀀏􀀔􀀏 􀀅 􀀏􀀘􀀉􀀏􀀂􀀅􀀋􀀂􀀄􀀅􀀆 􀀒􀀔􀀅􀀆􀀂􀀒􀀐􀀅􀀃􀀔 􀀉􀀘􀀃􀀗􀀔􀀋 􀀌􀀚􀀛􀀜􀀎􀀝 􀀁􀀆􀀂􀀒􀀊􀀘􀀞􀀒 􀀁􀀍 􀀄􀀏􀀂􀀃􀀅􀀗􀀄􀀂􀀄􀀊􀀋􀀅􀀆􀀆􀀓 􀀗􀀄􀀅􀀞􀀋􀀊􀀏􀀔􀀗 􀀄􀀂􀀒 􀀚2-􀀆􀀔􀀅􀀗 􀀔􀀆􀀔􀀐􀀂􀀃􀀊􀀐􀀅􀀃􀀗􀀄","cbCaisrBjhUSDUYV","https://ap.wps.com/l/cbCaisrBjhUSDUYV","pdf",1121815,1,8,"English","en",105,"# Overview\n## Problem and motivation\n## Data and single-lead ECG representation\n## Modeling approaches\n## Evaluation and results\n## ROC analysis and performance metrics","[{\"question\":\"What is the document mainly about?\",\"answer\":\"It focuses on using machine learning to predict atrial fibrillation from single-lead ECG data and reporting model performance.\"},{\"question\":\"What type of input data is used for prediction?\",\"answer\":\"Single-lead ECG data derived from ECG signals is used as the main input for the predictive models.\"},{\"question\":\"How is model performance evaluated?\",\"answer\":\"Performance is assessed using metrics such as ROC-based analysis, including AUC values, to compare different methods.\"}]","Machine learning algorithms for predicting atrial fibrillation using single-lead data derived from | 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