体外心脏电测量及智能标测房颤基质的研究进展  

Developments of ex vivo cardiac electrical mapping and intelligent labeling of atrial fibrillation substrates

作　　者：常益 董明[1] 王彬(综述)[1] 范力宏(审校)[2] CHANG Yi;DONG Ming;WANG Bin;FAN Lihong(State Key Library of Electrical Insulation and Power Equipment,Xi’an Jiaotong University,Xi’an 710049,P.R.China;The First Affiliated Hospital of Xi’an Jiaotong University,Xi’an 710061,P.R.China)

机构地区：[1]西安交通大学电工材料电气绝缘国家重点实验室,西安710049 [2]西安交通大学第一附属医院,西安710061

出　　处：《生物医学工程学杂志》2024年第1期184-190,共7页Journal of Biomedical Engineering

基　　金：国家自然科学基金(62176208)。

摘　　要：心脏三维电生理标测是房颤消融手术开展的前提与基础,侵入式标测是临床现行方法,但存在创伤大、手术时程长、成功率低等诸多不足。体外标测技术因无创、易操作等特点与优势,近年来成为电生理标测技术的发展趋势与新方向。随着计算机软硬件水平快速发展和临床数据库的增长,深度学习技术在心电数据方面的应用日趋广泛且取得了巨大进步,为体外心脏测量与智能标测房颤基质研究提供了新思路。本文综述了心电正问题、心电逆问题以及深度学习在房颤标测中的应用等领域的研究进展,探讨了体外智能标测房颤基质存在的问题以及可能的解决途径,对体外心脏电生理标测面临的挑战和未来的发展方向进行了展望。Cardiac three-dimensional electrophysiological labeling technology is the prerequisite and foundation of atrial fibrillation(AF)ablation surgery,and invasive labeling is the current clinical method,but there are many shortcomings such as large trauma,long procedure duration,and low success rate.In recent years,because of its noninvasive and convenient characteristics,ex vivo labeling has become a new direction for the development of electrophysiological labeling technology.With the rapid development of computer hardware and software as well as the accumulation of clinical database,the application of deep learning technology in electrocardiogram(ECG)data is becoming more extensive and has made great progress,which provides new ideas for the research of ex vivo cardiac mapping and intelligent labeling of AF substrates.This paper reviewed the research progress in the fields of ECG forward problem,ECG inverse problem,and the application of deep learning in AF labeling,discussed the problems of ex vivo intelligent labeling of AF substrates and the possible approaches to solve them,prospected the challenges and future directions for ex vivo cardiac electrophysiology labeling.

关 键 词：心房颤动 电生理标测 心电正问题 心电逆问题 深度学习 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] R541.75[自动化与计算机技术—控制科学与工程]