Localizing the Origin of Idiopathic Ventricular Arrhythmia from ECG Using an Attention-Based Recurrent Convolutional Neural Network

TL;DR

This paper introduces a novel attention-based recurrent convolutional neural network that automatically localizes the origin of idiopathic ventricular arrhythmias from ECG signals, offering a noninvasive alternative to current invasive methods.

Contribution

The study presents a new deep learning model combining spatial fusion, temporal modeling, and attention mechanisms for accurate IVA origin localization from ECG data.

Findings

Achieved 94% accuracy in localizing IVA origins

Outperformed existing automatic and semi-automatic algorithms

Validated on data from 334 patients with high sensitivity and F1 score

Abstract

Idiopathic ventricular arrhythmia (IVAs) is extra abnormal heartbeats disturbing the regular heart rhythm that can become fatal if left untreated. Cardiac catheter ablation is the standard approach to treat IVAs, however, a crucial prerequisite for the ablation is the localization of IVAs' origin. The current IVA localization techniques are invasive, rely on expert interpretation, or are inaccurate. In this study, we developed a new deep-learning algorithm that can automatically identify the origin of IVAs from ECG signals without the need for expert manual analysis. Our developed deep learning algorithm was comprised of a spatial fusion to extract the most informative features from multichannel ECG data, temporal modeling to capture the evolving pattern of the ECG time series, and an attention mechanism to weigh the most important temporal features and improve the model interpretability. The algorithm was validated on a 12-lead ECG dataset collected from 334 patients (230 females) who experienced IVA and successfully underwent a catheter ablation procedure that determined IVA's exact origins. The proposed method achieved an area under the curve of 93%, an accuracy of 94%, a sensitivity of 97%, a precision of 95%, and an F1 score of 96% in locating the origin of IVAs and outperformed existing automatic and semi-automatic algorithms. The proposed method shows promise toward automatic and noninvasive evaluation of IVA patients before cardiac catheter ablation.