Systematic review of computational techniques, dataset utilization, and feature extraction in electrocardiographic imaging

作     者：Mayorca-Torres, Dagoberto Leon-Salas, Alejandro J. Peluffo-Ordonez, Diego H. 

作者机构：Univ Granada Dept Software Syst & Programming Languages C Periodista Daniel Saucedo Aranda S-N Granada 18071 Spain Univ Mariana Fac Engn Cl 18 34-104 Pasto 52001 Colombia Corp Univ Autonoma Narino Fac Engn Pasto 520001 Colombia Mohammed VI Polytech Univ Coll Comp Lot 660 Ben Guerir 43150 Morocco SDAS Res Grp Ben Guerir 43150 Morocco 

出 版 物：《MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING》 (Med. Biol. Eng. Comput.)

年 卷 期：2025年第63卷第5期

页      面：1289-1317页

核心收录：

学科分类：0831[工学-生物医学工程（可授工学、理学、医学学位）] 0710[理学-生物学] 1001[医学-基础医学(可授医学、理学学位)] 0812[工学-计算机科学与技术（可授工学、理学学位）] 10[医学] 

基　　金：Universidad de Granada (UGR) Universidad Mariana 

主　　题：ECG imaging Systematic review Regularization methods Computational techniques 

摘      要：This study aimed to analyze computational techniques in ECG imaging (ECGI) reconstruction, focusing on dataset identification, problem-solving, and feature extraction. We employed a PRISMA approach to review studies from Scopus and Web of Science, applying Cochrane principles to assess risk of bias. The selection was limited to English peer-reviewed papers published from 2010 to 2023, excluding studies that lacked computational technique descriptions. From 99 reviewed papers, trends show a preference for traditional methods like the boundary element and Tikhonov methods, alongside a rising use of advanced technologies including hybrid techniques and deep learning. These advancements have enhanced cardiac diagnosis and treatment precision. Our findings underscore the need for robust data utilization and innovative computational integration in ECGI, highlighting promising areas for future research and advances. This shift toward tailored cardiac care suggests significant progress in diagnostic and treatment methods.