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Online Augmentation for Quality Improvement of Neural Networks for Classification of Single-Channel Electrocardiograms

Conference paper
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Part of the Communications in Computer and Information Science book series (CCIS, volume 1086)

Abstract

Currently, on the market, there are mobile devices that are capable of reading a person’s single-lead electrocardiogram (ECG). These ECGs can be used to solve problems of determining various diseases. Neural networks are onearameters of augmentations of the approaches to solving such problems. In this paper, the usage of online augmentation during the training of neural networks was proposed to improve the quality of the ECGs classification. The possibility of using various types of online augmentations was explored. The most promising methods were highlighted. Experimental studies showed that the quality of the classification was improved for various tasks and various neural network architectures.

Keywords

Deep learning Neural networks Single-lead ECG classification Online augmentation 

Notes

Acknowledgements

This article contains the results of a project carried out within the implementation of the Program of the Center for Competence of the National Technology Initiative “Center for Storage and Analysis of Big Data”, supported by the Ministry of Science and Higher Education of the Russian Federation under the Lomonosov Moscow State University Project Support Fund 13/1251/2018 from 11.12.2018.

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Lomonosov Moscow State UniversityMoscowRussia

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