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

• URL: http://arxiv.org/abs/2208.00323v1
• Date: Sat, 30 Jul 2022 23:43:01 GMT
• Title: A Multi-View Learning Approach to Enhance Automatic 12-Lead ECG Diagnosis Performance
• Authors: Jae-Won Choi, Dae-Yong Hong, Chan Jung, Eugene Hwang, Sung-Hyuk Park, and Seung-Young Roh
• Abstract summary: 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 proposed model reports an F1 score of 0.840, which outperforms existing state-ofthe-art methods in the literature.
• Score: 0.240137930000971
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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