Percept U-Net: Percept Attention-based Convolutional Neural Network for Atrial Fibrillation Episode Localization

Early detection of paroxysmal atrial fibrillation (AF) is valuable in determining treatment options and diagnosing complications, and identifying the number and duration of AF episodes in the dynamic electrocardiogram (ECG) may help advance the study of pathological AF mechanisms. Current computer-aided ECG signal diagnosis methods can be divided into two categories: (a) methods based on every single heartbeat split from ECG records, which cannot use information from adjacent heartbeats, and (b) methods based on a segment of ECG records that contain multiple heartbeats, which can only give a classification of the whole segment but cannot identify the categories of each heartbeat in the record at a fine granularity. Few methods can exploit the relationship between heartbeats as well as identify the onset and end of disease episodes. To fill this gap, we designed an improved U-Net model named Percept U-Net to detect AF episodes from the ECG records. In this model, we propose the percept attention to replace skip connections in U-Net to integrate low-level features with high-level features, thereby enhancing the ability to detect and discriminate between normal and abnormal heartbeats. Experimental results show that Percept U-Net achieves higher classification and localization accuracy with less computational overhead and paraments than other comparative methods.