Diagnosis of atrial fibrillation using self-complementary attentional convolutional neural network

Automatic recognition of wearable dynamic electrocardiographic (ECG) signals is a difficult problem in biomedical signal processing. However, with the widespread use of long-range ambulatory ECG, a large number of real-time ECG signals are generated in the clinic, and it is very difficult for clinicians to perform timely atrial fibrillation (AF) diagnosis. Therefore, developing a new AF diagnosis algorithm can relieve the pressure on the healthcare system and improve the efficiency of AF screening.

In this study, a self-complementary attentional convolutional neural network (SCCNN) was designed to accurately identify AF in wearable dynamic ECG signals. First, a 1D ECG signal was converted into a 2D ECG matrix using the proposed Z-shaped signal reconstruction method. Then, a 2D convolutional network was used to extract shallow information from adjacent sampling points at close distances and interval sampling points at distant distances in the ECG signal. The self-complementary attention mechanism (SCNet) was used to focus and fuse channel information with spatial information. Finally, fused feature sequences were used to detect AF.

The accuracies of the proposed method on the three public databases were 99.79%, 95.51%, and 98.80%. The AUC values were 99.79%, 95.51%, and 98.77%, respectively. The sensitivity on the clinical database was as high as 99.62%.

These results show that the proposed method can accurately identify AF and has good generalization.