  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.
