Key result
DenseTransformer deep learning model achieves ~97% accuracy predicting AF from short ECG signals.
Why the study?
AF incidence and prevalence are increasing globally, prompting growing attention toward predicting AF to prevent disease progression.
Does the DenseTransformer deep learning model accurately predict atrial fibrillation from short ECG signals?
Does the DenseTransformer deep learning model accurately predict atrial fibrillation from short ECG signals?
The DenseTransformer deep learning model demonstrates high accuracy (97.2%) in predicting atrial fibrillation from short ECG signals, showing potential for clinical auxiliary diagnosis.
May facilitate AI-assisted AF screening from brief ECGs; leaves open generalizability and prospective outcome validation.
Atrial fibrillation (AF) is one of the most common kind of arrhythmia and its incidence and prevalence are increasing worldwide, with significant public health implications. As a result, increasing attention has been paid to predicting AF in order to prevent further progression of the disease. In this paper, we introduce a novel deep learning classification method that combines DenseNet and Transformer, namely DenseTransformer, to implement classification for AF prediction using short electrocardiogram (ECG) signals. DenseNet architecture is employed to extract local features and encourage feature reuse. Our model incorporates a novel bidirectional transformation mechanism, capturing global features and temporal dependency from both preceding and following context. We develop and evaluate the proposed method based on LTAFDB and NSRDB, which are available from the PhysioNet. The proposed method achieved $97.2 %$ accuracy in predicting AF. The results indicate that the proposed method can accurately predict AF from short ECG signals. It can be foreseen that the proposed model shows great potential for use in clinical auxiliary diagnosis based on short ECG signals.
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Gao et al. (2025) studied Atrial fibrillation. DenseTransformer (DenseNet and Transformer) was evaluated on Accuracy in predicting AF. The DenseTransformer deep learning model, combining DenseNet and a bidirectional Transformer mechanism, achieved 97.2% accuracy in predicting atrial fibrillation from short ECG signals.
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