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Abstract Deep learning (DL) models have demonstrated strong performance in electrocardiography (ECG) arrhythmia classification. However, the criteria and processes for selecting ECG samples for analysis are often unclear. This lack of transparency, together with the challenge of interpreting the rationale behind model predictions, may limit clinical acceptance.
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