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  • Title: Interpatient ECG Arrhythmia Detection by Residual Attention CNN.
    Author: Xu P, Liu H, Xie X, Zhou S, Shu M, Wang Y.
    Journal: Comput Math Methods Med; 2022; 2022():2323625. PubMed ID: 35432590.
    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.
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