The classification of electrocardiogram (ECG) plays a crucial role in the development of an automatic cardiovascular diagnostic system. However, considerable variances in ECG signals between individuals is a significant challenge. Changes in data distribution limit cross-domain utilization of a model. In this study, we propose a solution to classify ECG in an unlabeled dataset by leveraging knowledge obtained from labeled source domain. We present a domain-adaptive deep network based on cross-domain feature discrepancy optimization. Our method comprises three stages: pre-training, cluster-centroid computing, and adaptation. In pre-training, we employ a Distributionally Robust Optimization (DRO) technique to deal with the vanishing worst-case training loss. To enhance the richness of the features, we concatenate three temporal features with the deep learning features. The cluster computing stage involves computing centroids of distinctly separable clusters for the source using true labels, and for the target using confident predictions. We propose a novel technique to select confident predictions in the target domain. In the adaptation stage, we minimize compacting loss within the same cluster, separating loss across different clusters, inter-domain cluster discrepancy loss, and running combined loss to produce a domain-robust model. Experiments conducted in both cross-domain and cross-channel paradigms show the efficacy of the proposed method. Our method achieves superior performance compared to other state-of-the-art approaches in detecting ventricular ectopic beats (V), supraventricular ectopic beats (S), and fusion beats (F). Our method achieves an average improvement of 11.78% in overall accuracy over the non-domain-adaptive baseline method on the three test datasets.

翻译：心电图分类在自动心血管诊断系统的开发中起着关键作用。然而，个体间心电信号的显著差异构成重大挑战，数据分布的变化限制了模型的跨域应用。本研究提出一种解决方案，通过利用标注源域的知识对未标注数据集中的心电图进行分类。我们提出了一种基于跨域特征差异优化的域自适应深度网络。该方法包含三个阶段：预训练、聚类质心计算与自适应。在预训练阶段，我们采用分布鲁棒优化技术应对最差情况训练损失消失的问题。为增强特征丰富性，我们将三种时序特征与深度学习特征进行拼接。聚类计算阶段涉及为源域数据使用真实标签、为目标域数据使用高置信度预测计算清晰可分离聚类的质心，我们提出了一种在目标域中选择高置信度预测的新技术。在自适应阶段，我们通过最小化簇内紧凑损失、簇间分离损失、跨域簇差异损失及联合损失，构建域鲁棒模型。跨域与跨通道实验验证了所提方法的有效性，该方法在检测室性早搏（V）、室上性早搏（S）及融合搏动（F）方面优于现有先进方法。与无域自适应的基线方法相比，本方法在三个测试数据集上的总体准确率平均提升11.78%。