LAUSR

Electrocardiogram (ECG) signals have been widely used to detect cardiac arrhythmia. Visual inspection is not only time consuming, but also may lead to misdiagnosis and affect the prevention or treatment of the disease. Therefore, automatic diagnosis which can greatly improve the efficiency and accuracy of diagnosis is needed to assist doctors with arrhythmia diagnosis. Due to its capacity for high resolution, HRNet has attracted extensive attention for classification in recent years. However, HRNet is only designed for two-dimensional images, and thus is not suitable for ECG signal classification. In this paper, we propose an arrhythmia classification scheme which is based on a modified HRNet and efficient channel attention (ECA) to classify five arrhythmia types. The proposed scheme first divides the original ECG signal into 5 s segments of 1800 sampling points. Then, the segments are fed into the improved HRNet network for automatic learning and classification. Extensive simulations have been performed on the MIT-BIH database to validate the effectiveness of the proposed scheme. Experimental results have shown that the proposed scheme achieves an average accuracy of 99.86%, which is superior to the benchmarking methods.