A machine learning approach to differentiate wide QRS tachycardia: distinguishing ventricular tachycardia from supraventricular tachycardia.

Journal: Journal of interventional cardiac electrophysiology : an international journal of arrhythmias and pacing
PMID: 38246906

Abstract

BACKGROUND: Differential diagnosis of wide QRS tachycardia (WQCT) has been a challenging issue. Published algorithms to distinguish ventricular tachycardia (VT) and supraventricular tachycardia (SVT) have limited diagnostic capabilities.

Authors

  • Zhen-Zhen Li
    Second Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou, China.
  • Wei Zhao
    Key Laboratory of Synthetic and Biological Colloids, Ministry of Education, Jiangnan University, Wuxi 214122, Jiangsu Province, P. R. China. lxy@jiangnan.edu.cn zhuye@jiangnan.edu.cn.
  • YangMing Mao
    Section of Pacing and Electrophysiology, Division of Cardiology, First Affiliated Hospital of Nanjing Medical University, Guangzhou Road 300, Nanjing, 210006, Jiangsu, China.
  • Dan Bo
    Section of Pacing and Electrophysiology, Division of Cardiology, First Affiliated Hospital of Nanjing Medical University, Guangzhou Road 300, Nanjing, 210006, Jiangsu, China.
  • QiuShi Chen
    Section of Pacing and Electrophysiology, Division of Cardiology, First Affiliated Hospital of Nanjing Medical University, Guangzhou Road 300, Nanjing, 210006, Jiangsu, China.
  • Pipin Kojodjojo
    Heart Center, National University, Singapore, Singapore.
  • FengXiang Zhang
    Section of Pacing and Electrophysiology, Division of Cardiology, First Affiliated Hospital of Nanjing Medical University, Guangzhou Road 300, Nanjing, 210006, Jiangsu, China. njzfx6@njmu.edu.cn.

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