HIGHLIGHTS
SUMMARY
The arrhythmia heartbeats classification work is mainly divided into four stages: preprocessing, heartbeat segmentation, feature extraction, and classification. In the feature extraction step, some useful features related to arrhythmia heartbeats are extracted from ECGs, such as RR intervals, wavelets, and local binary pattern (LBP). The SWT feature reflects the characteristics of the time and frequency domains, and the RR interval feature relates a single beat to other surrounding beats. The combination of SWT and RR interval features can effectively improve classification performance (ii) The reparameterization technology was utilized to lightweight the designed multibranch convolutional . . .
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