A novel practical algorithm using machine learning to differentiate outflow tract ventricular arrhythmia origins
A novel practical algorithm using machine learning to differentiate outflow tract ventricular arrhythmia origins
复制标题
DOI：
10.1111/jce.15823
复制
发表时间：
2023-01
影响因子：
2.7
通讯作者：
M. Shimojo;Y. Inden;Satoshi Yanagisawa;Noriyuki Suzuki;Naoki Tsurumi;Ryo Watanabe;Toshifumi Nakagomi;T. Okajima;K. Suga;Yukiomi Tsuji;T. Murohara
中科院分区：
文献类型：
--
作者：
M. Shimojo;Y. Inden;Satoshi Yanagisawa;Noriyuki Suzuki;Naoki Tsurumi;Ryo Watanabe;Toshifumi Nakagomi;T. Okajima;K. Suga;Yukiomi Tsuji;T. Murohara
通过心电图复合波诊断流出道室性心律失常（OTVA）定位是成功导管消融OTVA的关键。然而，诊断具有导联V3（V3TZ）中的心前区过渡的OTVA的起源是具有挑战性的。本研究旨在使用机器学习创建最佳实用心电图算法，以区分OTVA起源的左心室流出道（LVOT）和右心室流出道（RVOT）。
Diagnosis of outflow tract ventricular arrhythmia (OTVA) localization by an electrocardiographic complex is key to successful catheter ablation for OTVA. However, diagnosing the origin of OTVA with a precordial transition in lead V3 (V3TZ) is challenging. This study aimed to create the best practical electrocardiogram algorithm to differentiate the left ventricular outflow tract (LVOT) from the right ventricular outflow tract (RVOT) of OTVA origin with V3TZ using machine learning.