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Simulation of ECG for Cardiac Diseases Using Generative Adversarial Networks

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Universal Access in Human-Computer Interaction. Novel Design Approaches and Technologies (HCII 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13308))

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Abstract

Every year, the fundamental technology related to deep learning evolves. Recently, remarkable progress has been made not only in the fields of classification and regression, but also in the field of generation. To date, various models have been proposed for generative models using deep learning, including generative adversarial networks (GAN) and variational auto-encoder (VAE). In this study, we attempted to simulate an electrocardiogram (ECG) using GAN. In addition, ECG may have various states simultaneously, such as AV block and WPW syndrome. Therefore, we propose a method for generating ECGs that considers the fact that multiple states exist simultaneously. The generated ECG validated the basic elements that compose an ECG, such as R and T waves. We demonstrated that AI system can be applied to numerical simulations of bio-signals such as time sequences measured by 3D motion capture, ECGs, and electrogastrograms (EGGs). Furthermore, we conducted experiments to study the effects of stereoscopic video clips on the elderly.

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Acknowledgments

This work was supported in part by the TAKEUCHI Scholarship Foundation, Japan Society for the Promotion of Science, Grant-in-Aid for Research Activity Start-up Number 15H06711, Grant-in-Aid for Young Scientists (B) Number 16K16105, and Grant-in-Aid for Scientific Research (C) Number 17K00715.

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Authors and Affiliations

  1. University of Fukui, 3-9-1 Bunkyo, Fukui-shi, Fukui, Japan

    Kohki Nakane, Tatsuki Kawai, Rintaro Sugie & Hiroki Takada

Authors
  1. Kohki Nakane
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  2. Tatsuki Kawai
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  3. Rintaro Sugie
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  4. Hiroki Takada
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Corresponding author

Correspondence to Kohki Nakane .

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Editors and Affiliations

  1. Foundation for Research and Technology - Hellas (FORTH), Heraklion, Crete, Greece

    Margherita Antona

  2. University of Crete and Foundation for Research and Technology - Hellas (FORTH), Heraklion, Crete, Greece

    Constantine Stephanidis

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Nakane, K., Kawai, T., Sugie, R., Takada, H. (2022). Simulation of ECG for Cardiac Diseases Using Generative Adversarial Networks. In: Antona, M., Stephanidis, C. (eds) Universal Access in Human-Computer Interaction. Novel Design Approaches and Technologies. HCII 2022. Lecture Notes in Computer Science, vol 13308. Springer, Cham. https://doi.org/10.1007/978-3-031-05028-2_30

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  • DOI: https://doi.org/10.1007/978-3-031-05028-2_30

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-05027-5

  • Online ISBN: 978-3-031-05028-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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