A Multi-View learning approach to enhance automatic 12-Lead ECG diagnosis performance

Objectives: The electrocardiogram (ECG) has important clinical value for the early diagnosis of cardiovascular diseases. Recently, the performance of existing diagnosis models based on ECG data has improved with the introduction of deep learning (DL). However, the impact of various combinations of multiple DL components and/or the role of augmentation techniques on the diagnosis has not been sufficiently investigated in this field. In this sense, this study aims to design an integrated model consisting of diverse DL-based modules. Methods: Here, an ensemble-based multi-view learning approach with an ECG augmentation technique is proposed to achieve higher performance than traditional automatic 12-lead ECG diagnosis methods. Results: Accordingly, several experiments have been conducted with CPSC2018 dataset for evaluation. The proposed model reports an F1 score of 0.840, which outperforms existing state-of-the-art methods in the literature. Conclusion: Thus, this study provides quantitative evidence demonstrating that the multi-view learning approach can be used as a unified algorithmic method in the field of automatic ECG diagnosis. Significance: In this work, we reviewed ensembles of models consisting of multiple DL components for automatic ECG diagnosis and proposed the ECG-specific augmentation technique.