HPRNet: a hierarchical pyramidal residual network for ECG arrhythmia classification

Accurate classification of electrocardiogram (ECG) signals plays a critical role in the automated diagnosis of cardiac arrhythmias. However, ECG recordings are often non-stationary and susceptible to various types of noise, which makes robust feature extraction challenging for many existing deep learning models. To address these challenges, this paper proposes a hierarchical pyramidal residual network (HPRNet) for ECG arrhythmia classification. HPRNet incorporates a hierarchical pyramidal REB-ba