计算机科学与探索2025，Vol.19Issue(4)：1011-1020,10.DOI:10.3778/j.issn.1673-9418.2405065
频域mixup增广和logit补偿的自监督多标记不平衡心电图分类
Frequency Domain mixup Augmentation and logit Compensation for Self-Supervised Multi-label Imbalanced Electrocardiogram Classification
摘要
Abstract
Self-supervised contrastive learning has been proven effective in learning good feature representations by con-trasting views through data augmentation,followed by fine-tuning for downstream(classification)tasks.Thus,it obtains wide applications.Electrocardiogram(ECG),as a non-invasive,low-risk,and low-cost signal source for cardiovascular diseases,its classification aids in early prevention and precise treatment of conditions like arrhythmia.However,most existing methods for ECG representation learning only perform contrastive learning through temporal perturbation augmentation of examples,overlooking the potential utilization of frequency-domain information,leaving room for further improve-ment in representation quality.Therefore,a frequency domain mixup augmentation strategy is designed for ECG samples,which generates augmented samples by exchanging frequency domain information between samples to achieve contras-tive learning,thus addressing the shortcomings of existing ECG representation learning.In the downstream fine-tuning stage,considering that ECG classification inherently involves a multi-label class imbalance problem,this paper proposes mitigating this issue by incorporating label frequencies into binary cross-entropy(BCE)loss as logit compensation.Finally,model evaluation is conducted on the CPSC2018 and Chapman datasets.Experimental results demonstrate that integrating the proposed method as an independent module into multiple baseline models improves performance in terms of AUC and mAP metrics.Particularly,significant enhancements are observed in the performance of certain rare disease indicators,thereby validating the effectiveness of this approach.关键词
心电图分类/心率失常/自监督对比学习/多标记/类不平衡Key words
electrocardiogram classification/arrhythmia/self-supervised contrastive learning/multi-label/class imbalance分类
信息技术与安全科学引用本文复制引用
操思源,陈松灿..频域mixup增广和logit补偿的自监督多标记不平衡心电图分类[J].计算机科学与探索,2025,19(4):1011-1020,10.基金项目
国家自然科学基金(62076124,62376126).This work was supported by the National Natural Science Foundation of China(62076124,62376126). （62076124,62376126）
