Automated Identication of Atrial Fibrillation from Single-lead ECGs Using Multi-branching ResNet

TL;DR

This paper introduces a novel deep learning approach using multi-branch ResNet architecture and wavelet-based feature extraction for accurate automated atrial fibrillation detection from single-lead ECG signals.

Contribution

It presents an innovative combination of wavelet transform and multi-branch ResNet CNN to improve AF classification from ECG data, addressing data imbalance issues.

Findings

Outperforms traditional deep learning models in AF detection accuracy.

Effective handling of imbalanced data with multi-branch architecture.

Validated on two real-world ECG datasets.

Abstract

Atrial fibrillation (AF) is the most common cardiac arrhythmia, which is clinically identified with irregular and rapid heartbeat rhythm. AF puts a patient at risk of forming blood clots, which can eventually lead to heart failure, stroke, or even sudden death. It is of critical importance to develop an advanced analytical model that can effectively interpret the electrocardiography (ECG) signals and provide decision support for accurate AF diagnostics. In this paper, we propose an innovative deep-learning method for automated AF identification from single-lead ECGs. We first engage the continuous wavelet transform (CWT) to extract time-frequency features from ECG signals. Then, we develop a convolutional neural network (CNN) structure that incorporates ResNet for effective network training and multi-branching architectures for addressing the imbalanced data issue to process the 2D time-frequency features for AF classification. We evaluate the proposed methodology using two real-world ECG databases. The experimental results show a superior performance of our method compared with traditional deep learning models.