Due to the significant variability in waveforms and characteristics of ECG signals, developing fully automatic (i.e., requires no expert assistance) heartbeat classification algorithms with satisfactory performance on domain-shifted data remains challenging. In this letter, we propose a novel Mixup Asymmetric Tri-training (MIAT) method to improve the generalization ability of heartbeat classifiers in domain shift scenarios. First, we develop an ECG-based tri-branch CNN model, including one shared feature encoder followed by three branch networks. Next, to obtain target-discriminative features progressively, the tri-branch CNN is trained asymmetrically in each domain adaptation cycle, where two branches are used to assign pseudo-labels to the target domain samples and the third branch is trained on these pseudo-labeled target samples. Moreover, three kinds of mixup regularizations are incorporated into the training process. Experimental results on MITDB and SVDB show that the proposed MIAT outperforms the state-of-the-art methods in terms of F1-macro score and demonstrate the effectiveness of each mixup regularization.

中文翻译：

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域转移下心跳分类的混合非对称三重训练


由于心电图信号的波形和特征存在显着的变化，开发在域移位数据上具有令人满意的性能的全自动（即不需要专家协助）心跳分类算法仍然具有挑战性。在这封信中，我们提出了一种新颖的混合非对称三训练（MIAT）方法，以提高心跳分类器在域转移场景中的泛化能力。首先，我们开发了一种基于心电图的三分支 CNN 模型，包括一个共享特征编码器和后面的三个分支网络。接下来，为了逐步获得目标区分特征，三分支 CNN 在每个域适应周期中进行不对称训练，其中两个分支用于为目标域样本分配伪标签，第三个分支在这些伪标签上进行训练目标样本。此外，训练过程中纳入了三种混合正则化。 MITDB 和 SVDB 上的实验结果表明，所提出的 MIAT 在 F1 宏分数方面优于最先进的方法，并证明了每个混合正则化的有效性。