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

2023/06/26 by Jianxin Xie, Xie, Jianxin, Stavros Stavrakis +3 · 3 citations
Medicine · #ECG Monitoring and Analysis #FOS: Electrical engineering #Signal Processing (eess.SP) #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2306.15096

openalex publication_date 2023/06/26 · openalex created_date 2023/06/29 · openalex updated_date 2026/07/28

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.

Cited by

• Robust and Generalizable Atrial Fibrillation Detection from ECG Using Time-Frequency Fusion and Supervised Contrastive Learning
• Physics-augmented Multi-task Gaussian Process for Modeling Spatiotemporal Dynamics
• Manifold-Aware Diffusion-Augmented Contrastive Learning for Noise-Robust Biosignal Representation

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