Department of Medicine, Division of Cardiovascular Diseases, Washington University School of Medicine in St. Louis, 660 S. Euclid Ave, CB 8086, St. Louis, MO 63110, United States of America.
Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, United States of America.
J Electrocardiol. 2023 Nov-Dec;81:44-50. doi: 10.1016/j.jelectrocard.2023.07.008. Epub 2023 Jul 23.
Accurate differentiation of wide complex tachycardias (WCTs) into ventricular tachycardia (VT) or supraventricular wide complex tachycardia (SWCT) using non-invasive methods such as 12‑lead electrocardiogram (ECG) interpretation is crucial in clinical practice. Recent studies have demonstrated the potential for automated approaches utilizing computerized ECG interpretation software to achieve accurate WCT differentiation. In this review, we provide a comprehensive analysis of contemporary automated methods for VT and SWCT differentiation. Our objectives include: (i) presenting a general overview of the emergence of automated WCT differentiation methods, (ii) examining the role of machine learning techniques in automated WCT differentiation, (iii) reviewing the electrophysiology concepts leveraged existing automated algorithms, (iv) discussing recently developed automated WCT differentiation solutions, and (v) considering future directions that will enable the successful integration of automated methods into computerized ECG interpretation platforms.
使用非侵入性方法（如 12 导联心电图 [ECG] 解读）准确区分宽 QRS 心动过速（WCT）为室性心动过速（VT）或室上性宽 QRS 心动过速（SWCT）在临床实践中至关重要。最近的研究表明，利用计算机 ECG 解读软件的自动化方法具有实现准确 WCT 区分的潜力。在这篇综述中，我们对用于 VT 和 SWCT 区分的现代自动化方法进行了全面分析。我们的目标包括：（i）介绍自动化 WCT 区分方法的出现概况，（ii）检查机器学习技术在自动化 WCT 区分中的作用，（iii）审查利用现有自动化算法的电生理学概念，（iv）讨论最近开发的自动化 WCT 区分解决方案，以及（v）考虑未来的方向，使自动化方法能够成功集成到计算机 ECG 解读平台中。
