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

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  • First Online:
Analysis of Images, Social Networks and Texts (AIST 2019)

Part of the book series: Communications in Computer and Information Science ((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.

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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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Authors and Affiliations

  1. Lomonosov Moscow State University, Leninskie Gory 1, Moscow, 119991, Russia

    Valeriia Guryanova

Authors
  1. Valeriia Guryanova
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Correspondence to Valeriia Guryanova .

Editor information

Editors and Affiliations

  1. RWTH Aachen University, Aachen, Germany

    Wil M. P. van der Aalst

  2. University of Ljubljana, Ljubljana, Slovenia

    Vladimir Batagelj

  3. National Research University Higher School of Economics, Moscow, Russia

    Dmitry I. Ignatov

  4. Institute of Mathematics and Mechanics Yekaterinburg, Yekaterinburg, Russia

    Michael Khachay

  5. National Research University Higher School of Economics, Moscow, Russia

    Valentina Kuskova

  6. University of Oslo, Oslo, Norway

    Andrey Kutuzov

  7. National Research University Higher School of Economics, Moscow, Russia

    Sergei O. Kuznetsov

  8. National Research University Higher School of Economics, Moscow, Russia

    Irina A. Lomazova

  9. Moscow State University, Moscow, Russia

    Natalia Loukachevitch

  10. Loria, Vandoeuvre lès Nancy, France

    Amedeo Napoli

  11. University of Florida, Gainesville, USA

    Panos M. Pardalos

  12. Ca' Foscari University of Venice, Venezia Mestre, Italy

    Marcello Pelillo

  13. National Research University Higher School of Economics, Nizhny Novgorod, Russia

    Andrey V. Savchenko

  14. Kazan Federal University, Kazan, Russia

    Elena Tutubalina

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Guryanova, V. (2020). Online Augmentation for Quality Improvement of Neural Networks for Classification of Single-Channel Electrocardiograms. In: van der Aalst, W., et al. Analysis of Images, Social Networks and Texts. AIST 2019. Communications in Computer and Information Science, vol 1086. Springer, Cham. https://doi.org/10.1007/978-3-030-39575-9_5

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  • DOI: https://doi.org/10.1007/978-3-030-39575-9_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-39574-2

  • Online ISBN: 978-3-030-39575-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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