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RandECG: Data Augmentation for Deep Neural Network Based ECG Classification

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Advances in Artificial Intelligence (JSAI 2021)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1423))

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Abstract

In the medical field, it is quite expensive to obtain labeled data that are essential to train deep neural networks (DNNs). One way to tackle this issue is to apply data augmentation, a technique to improve classification accuracy by increasing diversity of data through random but realistic transformations. Data augmentation have shown promising results in visual domain, however, transformations applied to image data cannot be directly applied to ECG data. Here we propose RandECG, a data augmentation method tailored for electrocardiogram (ECG) data classification with deep neural networks (DNNs). We explored various transformation methods and selected suitable transformations for ECG. We tested efficacy of RandECG on two different datasets, and found that the classification accuracy of atrial fibrillation can be improved up to 3.51%, without changing an architecture of DNNs.

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Authors and Affiliations

  1. Advanced Data Science Project, RIKEN Information R&D and Strategy Headquarters, Tokyo, Japan

    Naoki Nonaka & Jun Seita

Authors
  1. Naoki Nonaka
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  2. Jun Seita
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Corresponding author

Correspondence to Jun Seita .

Editor information

Editors and Affiliations

  1. Graduate School of System Design, Tokyo Metropolitan University, Hino, Tokyo, Japan

    Yasufumi Takama

  2. Graduate School of Economics, Osaka University, Toyonaka, Osaka, Japan

    Naohiro Matsumura

  3. Faculty of Business and Commerce, Kansai University, Osaka, Japan

    Katsutoshi Yada

  4. Faculty of Informatics, Kansai University, Takatsuki, Osaka, Japan

    Mitsunori Matsushita

  5. Department of Applied Computer Science, Tokyo Polytechnic University, Atsugi, Kanagawa, Japan

    Daisuke Katagami

  6. Division of Behavioral Science, Chiba University, Chiba, Japan

    Akinori Abe

  7. Department of Intelligence Science and Technology, Kyoto University, Kyoto, Japan

    Hisashi Kashima

  8. Institute of Industrial Science, The University of Tokyo, Meguro-ku, Tokyo, Japan

    Toshihiro Hiraoka

  9. Department of Computer Science and Engineering, Nagoya Institute of Technology, Nagoya, Japan

    Takahiro Uchiya

  10. Graduate School of Information Science and Technology Sapporo Hokkaido Japan, Hokkaido University, Sapporo, Hokkaido, Japan

    Rafal Rzepka

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Nonaka, N., Seita, J. (2022). RandECG: Data Augmentation for Deep Neural Network Based ECG Classification. In: Takama, Y., et al. Advances in Artificial Intelligence. JSAI 2021. Advances in Intelligent Systems and Computing, vol 1423. Springer, Cham. https://doi.org/10.1007/978-3-030-96451-1_16

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