HCTNet: An experience-guided deep learning network for inter-patient arrhythmia classification on imbalanced dataset

Abstract

The automatic diagnosis of arrhythmia using machine learning has been a hot topic and extensively researched recently. A common problem is class imbalance that could make the deep learning models easily trapped into biased learning towards the majority class while ignoring rare classes during reasoning. When conducting inter-patient experiments, the inherent individual difference makes the condition even worse. Current deep learning methods generally take elaborate data modification strategies like data augmentation that complicate the training process. This paper, however, presents a special Hybrid Convolutional Transformer Network (HCTNet) that could effectively extract decisive patterns by drawing on doctors’ diagnosis experience in structure design. Meanwhile, a novel logit adjusted loss is applied to enlarge the pairwise margin between different classes so that the HCTNet could be highly sensitive to rare anomalies. In the experiments, the proposed method has outperformed most state-of-the-arts on the benchmark of the MIT-BIH database: the F1 scores for the three primary arrhythmias (N, S, V) are 97.5%, 61.5%, and 88.3%, respectively under the inter-patient paradigm.