Mixup Asymmetric Tri-Training for Heartbeat Classification Under Domain Shift

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.