This paper addresses the challenge of improving both the performance and interpretability of deep learning models for arrhythmia classification using 12-lead electrocardiogram (ECG) signals. While deep neural networks have demonstrated high accuracy in medical signal analysis, their black-box nature often limits clinical trust and adoption. We propose an explanation-guided learning framework that incorporates expert-driven explanations into the training process, thereby enhancing the model’s ability to focus on clinically relevant features. The approach leverages saliency maps and domain knowledge to guide the model’s attention during classification tasks. Experimental results on CPSP-2018 dataset demonstrates that the proposed method not only achieves superior classification performance compared to previous studies but also produces more interpretable decision rationales, as validated by quantitative and qualitative assessments. This study highlights the potential of explanation-guided learning to bridge the gap between model accuracy and interpretability in critical healthcare applications.