A Multi-View Learning Approach to Enhance Automatic 12-Lead ECG Diagnosis Performance
This work addresses the need for improved automatic 12-lead ECG diagnosis in medical applications, representing an incremental advancement over prior methods.
The study tackled the problem of insufficient investigation into the impact of multiple deep learning components and data augmentation on ECG diagnosis by proposing an ensemble-based multi-view learning approach with ECG augmentation, achieving an F1 score of 0.840 that outperforms existing state-of-the-art methods.
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.