Abstract

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Title:: Class-aware hybrid Mixup method for imbalanced electrocardiogram signal classification

Author(s):: TIAN Yuepeng1; 2; XU Lin2; LI Yuwen1; 1. School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China 2. National Key Laboratory of Information Systems Engineering, Nanjing 210096, China

Keywords:: Keywords: electrocardiogram signal classification class imbalance data augmentation Mixup CutMix1d Manifold Mixup

Abstract:: Abstract: A class-aware hybrid Mixup method customized for electrocardiogram (ECG) analysis is proposed to alleviate class imbalance in ECG signal classification. The proposed method leverages the strengths of CutMix1d and Manifold Mixup: the former generates minority-class samples in the original space that better preserve realistic signal structures, while the latter smoothes decision boundaries through feature-space interpolation, thereby simultaneously enhancing minority-class representation and overall discriminative capability. In addition, a minority-class-aware mechanism is introduced to enable more targeted optimization for rare classes. Experiments are conducted on two public ECG datasets, including 3 imbalanced tasks from PTB-XL and a CPSC2018 task. Results show that the proposed method achieves an average Macro-AUC improvement of nearly 0.01 over the baseline across 4 tasks, demonstrating consistent and stable gains. On the Sub-diagnostic task, the Macro-F1 score improves by 0.075 compared with the baseline, indicating more pronounced improvements at the class level. These findings confirm that the proposed approach can effectively alleviate class imbalance in ECG data, providing an efficient data augmentation solution for clinical ECG analysis.