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

2022/07/30 by Jae-Won Choi, Choi, Jae-Won, Dae-Yong Hong +9
Medicine · Social Sciences · #Advanced Computing and Algorithms #Artificial Intelligence (cs.AI) #ECG Monitoring and Analysis #FOS: Computer and information sciences #FOS: Electrical engineering #Machine Learning (cs.LG) #Signal Processing (eess.SP) #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.2208.00323

openalex publication_date 2022/07/30 · openalex created_date 2022/08/03 · openalex updated_date 2026/07/28

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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