A Multi-View Learning Approach to Enhance Automatic 12-Lead ECG Diagnosis Performance

Jae-Won Choi, Dae-Yong Hong, Chan Jung, Eugene Hwang, Sung-Hyuk Park, Seung-Young Roh

Abstract: The performances of commonly used electrocardiogram (ECG) diagnosis models have recently improved with the introduction of deep learning (DL). However, the impact of various combinations of multiple DL components and/or the role of data augmentation techniques on the diagnosis have not been sufficiently investigated. This study proposes an ensemble-based multi-view learning approach with an ECG augmentation technique to achieve a higher performance than traditional automatic 12-lead ECG diagnosis methods. The data analysis results show that the proposed model reports an F1 score of 0.840, which outperforms existing state-ofthe-art methods in the literature.