Interpatient ECG Arrhythmia Detection by Residual Attention CNN. (8th April 2022)

• Record Type: Journal Article
• Authors: Xu, Pengyao; Liu, Hui; Xie, Xiaoyun; Zhou, Shuwang; Shu, Minglei; Wang, Yinglong
• Other Names: Bosnic Zoran Academic Editor.
• Is Part Of: Computational and mathematical methods in medicine. Volume 2022(2022)
• Publication Date: 2022-04-08
• Subjects: Medicine -- Computer simulation -- Periodicals; Medicine -- Mathematical models -- Periodicals; 610.11
• DOI: 10.1155/2022/2323625
• Languages: English
• ISSNs: 1748-670X

Abstract

Abstract : The precise identification of arrhythmia is critical in electrocardiogram (ECG) research. Many automatic classification methods have been suggested so far. However, efficient and accurate classification is still a challenge due to the limited feature extraction and model generalization ability. We integrate attention mechanism and residual skip connection into the U-Net (RA-UNET); besides, a skip connection between the RA-UNET and a residual block is executed as a residual attention convolutional neural network (RA-CNN) for accurate classification. The model was evaluated using the MIT-BIH arrhythmia database and achieved an accuracy of 98.5% and F 1 scores for the classes S and V of 82.8% and 91.7%, respectively, which is far superior to other approaches.