DenseTransformer: A generalizable and robust deep learning method for atrial fibrillation prediction from short electrocardiogram signals

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.