Supraventricular ectopic beats and ventricular ectopic beats detection based on improved U-net.

Objective. Supraventricular ectopic beats (SVEB) or ventricular ectopic beats (VEB) are common arrhythmia with uncertain occurrence and morphological diversity, so realizing their automatic localization is of great significance in clinical diagnosis. Methods. We propose a modified U-net network: U SV -net, it can simultaneously realize the automatic positioning of VEB and SVEB. The improvement consists of three parts: firstly, we reconstruct part of the convolutional layer in U-net using group convolution to reduce the expression of redundant features. Secondly, a plug-and-play multi-scale 2D deformable convolution module is designed to extract cross-channel features of different scales. Thirdly, in addition to conventional output of U-net, we also compress and output the bottom feature map of U-net, the dual-output is trained through Dice-loss to take into account the learning of high/low resolution features of the model. We used the MIT-BIH arrhythmia database for patient-specific training, and used Sensitivity, Positive prediction rate and F1-scores to evaluate the effectiveness of our method. Main Result. The F1-scores of SVEB and VEB achieve the best results compared with other studies in different testing records. It is worth noting that the F1-scores of SVEB and VEB reached 81.3 and 95.4 in the 24 testing records. Moreover, our method is also at the forefront in Sensitivity and Positive prediction rate. Significance. The method proposed in this paper has great potential in the detection of SVEB and VEB. We anticipate efficiency and accuracy of clinical detection of ectopic beats would be improved.