Related Experiment Video
Updated: Jun 28, 2025
Estimating Bilateral Atrial Function by Cardiovascular Magnetic Resonance Feature Tracking in Patients with Paroxysmal Atrial Fibrillation
Published on: July 20, 2022
Automated identification of atrial fibrillation from single-lead ECGs using multi-branching ResNet
Jianxin Xie1, Stavros Stavrakis2, Bing Yao3
1School of Data Science, University of Virginia, Charlottesville, VA, United States.
Insights
This study introduces a deep learning model for automated atrial fibrillation (AF) detection using electrocardiograms (ECG). The novel method accurately identifies AF, improving diagnostic efficiency and reliability.
Area of Science:
- Cardiology
- Biomedical Engineering
- Artificial Intelligence in Medicine
Background:
- Atrial fibrillation (AF) is a common arrhythmia increasing stroke risk.
- Current AF detection via ECG is time-consuming and prone to human error.
- Automated diagnostic tools are crucial for timely and accurate AF identification.
Purpose of the Study:
- To develop an advanced deep learning model for automated AF detection from single-lead ECGs.
- To improve the accuracy and efficiency of AF diagnosis.
- To provide a reliable decision support system for medical professionals.
Main Methods:
- Utilized continuous wavelet transform (CWT) for time-frequency feature extraction from ECG signals.
- Employed residual learning-enhanced convolutional neural networks (ReNet) for feature interpretation.
- Incorporated a multi-branching structure into ResNet to handle class imbalance in ECG datasets.
Main Results:
- The proposed CWT-MB-Resnet achieved an F1 score of 0.8865 on the PhysioNet dataset.
- The model demonstrated an F1 score of 0.7369 on the OUHSC dataset.
- Exhibited robust performance, effectively balancing precision and recall for reliable medical diagnoses.
Conclusions:
- The CWT-MB-Resnet model offers a promising solution for automated AF detection.
- The deep learning approach enhances diagnostic accuracy and efficiency for atrial fibrillation.
- This method provides a valuable tool for supporting clinical decision-making in cardiology.
Abstract:
Introduction: 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. Electrocardiography (ECG), which involves acquiring bioelectrical signals from the body surface to reflect heart activity, is a standard procedure for detecting AF. However, the occurrence of AF is often intermittent, costing a significant amount of time and effort from medical doctors to identify AF episodes. Moreover, human error is inevitable, as even experienced medical professionals can overlook or misinterpret subtle signs of AF. As such, it is of critical importance to develop an advanced analytical model that can automatically interpret ECG signals and provide decision support for AF diagnostics. Methods: In this paper, we propose an innovative deep-learning method for automated AF identification using single-lead ECGs. We first extract time-frequency features from ECG signals using continuous wavelet transform (CWT). Second, the convolutional neural networks enhanced with residual learning (ReNet) are employed as the functional approximator to interpret the time-frequency features extracted by CWT. Third, we propose to incorporate a multi-branching structure into the ResNet to address the issue of class imbalance, where normal ECGs significantly outnumber instances of AF in ECG datasets. Results and Discussion: We evaluate the proposed Multi-branching Resnet with CWT (CWT-MB-Resnet) with two ECG datasets, i.e., PhysioNet/CinC challenge 2017 and ECGs obtained from the University of Oklahoma Health Sciences Center (OUHSC). The proposed CWT-MB-Resnet demonstrates robust prediction performance, achieving an F1 score of 0.8865 for the PhysioNet dataset and 0.7369 for the OUHSC dataset. The experimental results signify the model's superior capability in balancing precision and recall, which is a desired attribute for ensuring reliable medical diagnoses.
