A Deep Learning Approach Toward Differentiating Left versus Right for Idiopathic Ventricular Arrhythmia Originated from Outflow Tract

Abstract

AbstractBackground: 

Idiopathic ventricular arrhythmia (VA) is among the common cardiac diseases, ranging from benign conditions to those requiring immediate medical intervention. Many VAs originate from the heart’s outflow tract (OT). However, this area’s complexity and small size, along with other influencing external factors, pose significant challenges to accurate diagnosis. The similarity of the features of VAs on the electrocardiogram (ECG) originating from the right or left side of the OT may lead to misdiagnosis. This study aims to detect the site of origin for VAs originating from the OT, which is important as a key precognition for treatment during catheter ablation.

Methods: 

We perform this diagnosis using the standard 12-lead ECG and deep learning (DL) techniques without additional equipment. First, inspired by next-generation sequencing in genetics, we created one-dimensional (1D) streams of premature beats from a public dataset of 334 patients. Then, to compare the performance of common 1D DL models, the data were presented to various models, including long short-term memory, gated recurrent unit, and 1D convolutional neural network (1D-CNN).

Results: 

Experimental results show that the 1D-CNN network achieves the best performance, with an accuracy of 93.4% and an F1-score of 0.9313.

Conclusions: 

The findings demonstrate the effectiveness of DL in a higher level of applications, specifically in the treatment process, compared to conventional ECG analysis applications based on computerized methods. This represents a promising prospect for use in treatment processes without relying on complex and multifaceted diagnostic methods in the future.