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

arXiv: https://arxiv.org/abs/2208.00323 · HTML: https://mlsanity.com/paper/2208.00323/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 46 references that are in the map.
- First posted on arXiv 30 Jul 2022.

## Builds on (most cited first, 10 of 10)

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- [Cardiologist-Level Arrhythmia Detection with Convolutional Neural Networks](https://mlsanity.com/paper/1707.01836/) (2017)
- [Less Is More: A Comprehensive Framework for the Number of Components of Ensemble Classifiers](https://mlsanity.com/paper/1709.02925/) (2017)
- [Interpretable Deep Learning for Automatic Diagnosis of 12-lead Electrocardiogram](https://mlsanity.com/paper/2010.10328/) (2020)
- [Classification of 12-Lead ECG Signals with Bi-directional LSTM Network](https://mlsanity.com/paper/1811.02090/) (2018)
- [Fully Automated Myocardial Infarction Classification using Ordinary Differential Equations](https://mlsanity.com/paper/1410.6984/) (2014)

## Abstract (arXiv)

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.