Multimodality Data Augmentation Network for Arrhythmia Classification

Arrhythmia is a prevalent cardiovascular disease, which has garnered widespread attention due to its age-related increases in mortality rates. In the analysis of arrhythmia, the electrocardiogram (ECG) plays an important role. Arrhythmia classification often suffers from a significant data imbalance issue due to the limited availability of data for certain arrhythmia categories. This imbalance problem significantly affects the classification performance of the model. To address this challenge, data augmentation emerges as a viable solution, aiming to neutralize the adverse effects of imbalanced datasets on the model. To this end, this paper proposes a novel Multimodality Data Augmentation Network (MM-DANet) for arrhythmia classification. The MM-DANet consists of two modules: the multimodality data matching-based data augmentation module and the multimodality feature encoding module. In the multimodality data matching-based data augmentation module, we expand the underrepresented arrhythmia categories to match the size of the largest category. Subsequently, the multimodality feature encoding module employs convolutional neural networks (CNN) to extract the modality-specific features from both signals and images and concatenate them for efficient and accurate classification. The MM-DANet was evaluated on the MIT-BIH Arrhythmia Database and achieving an accuracy of 98.83%, along with an average specificity of 98.87%, average sensitivity of 92.92%, average precision of 91.05%, and average F1_score of 91.96%. Furthermore, its performance was also assessed on the St. Petersburg INCART arrhythmia database and the MIT-BIH supraventricular arrhythmia database, yielding AUC values of 81.98% and 90.93%, respectively. These outstanding results not only underscore the effectiveness of MM-DANet but also indicate its potential for facilitating reliable automated analysis of arrhythmias.