A High Precision Machine Learning-Enabled System for Predicting Idiopathic Ventricular Arrhythmia Origins.

ABSTRACT:

Background

Radiofrequency catheter ablation (CA) is an efficient antiarrhythmic treatment with a class I indication for idiopathic ventricular arrhythmia (IVA), only when drugs are ineffective or have unacceptable side effects. The accurate prediction of the origins of IVA can significantly increase the operation success rate, reduce operation duration and decrease the risk of complications. The present work proposes an artificial intelligence-enabled ECG analysis algorithm to estimate possible origins of idiopathic ventricular arrhythmia at a clinical-grade level accuracy.

Method

A total of 18,612 ECG recordings extracted from 545 patients who underwent successful CA to treat IVA were proportionally sampled into training, validation and testing cohorts. We designed four cla

SUBMITTER: Zheng J 

PROVIDER: S-EPMC8962834 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature