Key result
TCN-ResNet model detects AF in single-lead ECGs with ~97% accuracy.
Why the study?
Atrial fibrillation is a common persistent cardiac arrhythmia, and this study sought to integrate temporal convolutional network and residual network frameworks to classify it in single-lead ECGs.
Does a temporal convolutional network (TCN) and residual network (ResNet) framework accurately classify atrial fibrillation in single-lead ECGs?
Does a temporal convolutional network (TCN) and residual network (ResNet) framework accurately classify atrial fibrillation in single-lead ECGs?
A combined TCN and ResNet framework can accurately detect atrial fibrillation from single-lead ECGs, offering a promising tool for automated diagnosis.
May support single-lead ECG AF screening tools; leaves open prospective clinical validation before practice adoption.
Atrial fibrillation, one of the most common persistent cardiac arrhythmias globally, is known for its rapid and irregular atrial rhythms. This study integrates the temporal convolutional network (TCN) and residual network (ResNet) frameworks to effectively classify atrial fibrillation in single-lead ECGs, thereby enhancing the application of neural networks in this field. Our model demonstrated significant success in detecting atrial fibrillation, with experimental results showing an accuracy rate of 97% and an F1 score of 87%. These figures indicate the model's exceptional performance in identifying both majority and minority classes, reflecting its balanced and accurate classification capability. This research offers new perspectives and tools for diagnosis and treatment in cardiology, grounded in advanced neural network technology.
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Zhao et al. (2024) studied Atrial fibrillation. Temporal convolutional network (TCN) and residual network (ResNet) framework was evaluated on Classification of atrial fibrillation in single-lead ECGs. A temporal convolutional network and residual network (TCN-ResNet) model successfully detected atrial fibrillation in single-lead ECGs with an accuracy rate of 97% and an F1 score of 87%.
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