A High-Precision Deep Learning Algorithm to Localize Idiopathic Ventricular Arrhythmias

Ting Yung Chang, Ke Wei Chen, Chih Min Liu, Shih Lin Chang*, Yenn Jiang Lin, Li Wei Lo, Yu Feng Hu, Fa Po Chung, Chin Yu Lin, Ling Kuo, Shih Ann Chen

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Background: An accurate prediction of ventricular arrhythmia (VA) origins can optimize the strategy of ablation, and facilitate the procedure. Objective: This study aimed to develop a machine learning model from surface ECG to predict VA origins. Methods: We obtained 3628 waves of ventricular premature complex (VPC) from 731 patients. We chose to include all signal information from 12 ECG leads for model input. A model is composed of two groups of convolutional neural network (CNN) layers. We chose around 13% of all the data for model testing and 10% for validation. Results: In the first step, we trained a model for binary classification of VA source from the left or right side of the chamber with an area under the curve (AUC) of 0.963. With a threshold of 0.739, the sensitivity and specification are 90.7% and 92.3% for identifying left side VA. Then, we obtained the second model for predicting VA from the LV summit with AUC is 0.998. With a threshold of 0.739, the sensitivity and specificity are 100% and 98% for the LV summit. Conclusions: Our machine learning algorithm of surface ECG facilitates the localization of VPC, especially for the LV summit, which might optimize the ablation strategy.

Original languageEnglish
Article number764
JournalJournal of Personalized Medicine
Volume12
Issue number5
DOIs
StatePublished - May 2022

Keywords

  • catheter ablation
  • localization
  • machine learning
  • ventricular arrhythmia

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