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International Conference on Health Information Science

HIS 2021: Health Information Science pp 171–178Cite as

Multi-label Anomaly Classification Based on Electrocardiogram

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 13079))

Abstract

Under the background of 5G and AI, it is particularly important to use cloud computing, Internet of things and big data technology to analyze massive physiological signals of patients in real time. Arrhythmia can cause some major diseases, such as heart failure, atrial fibrillation and so on. It’s difficult to analysis them quickly. In this paper, a deep learning model of multi-label classification based on optimized temporal convolution network is proposed to detect abnormal electrocardiogram. The experimental results show that the accuracy of the model is 0.960, and the Micro F1 score is 0.87.

Supported in part by the National Natural Science Foundation of China (Grants No 61702274) and PAPD.

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Author information

Authors and Affiliations

  1. Engineering Research Center of Digital Forensics, Ministry of Education, and the Department of Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science and Technology, Nanjing, 210044, China

    Chenyang Li & Le Sun

Authors
  1. Chenyang Li
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  2. Le Sun
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Corresponding author

Correspondence to Le Sun .

Editor information

Editors and Affiliations

  1. Victoria University, Footscray, VIC, Australia

    Siuly Siuly

  2. Victoria University, Footscray, VIC, Australia

    Hua Wang

  3. Swinburne University of Technology, Melbourne, VIC, Australia

    Lu Chen

  4. University of Illinois at Springfield, Springfield, NY, USA

    Yanhui Guo

  5. Tsinghua University, Beijing, China

    Chunxiao Xing

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Li, C., Sun, L. (2021). Multi-label Anomaly Classification Based on Electrocardiogram. In: Siuly, S., Wang, H., Chen, L., Guo, Y., Xing, C. (eds) Health Information Science. HIS 2021. Lecture Notes in Computer Science(), vol 13079. Springer, Cham. https://doi.org/10.1007/978-3-030-90885-0_16

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  • DOI: https://doi.org/10.1007/978-3-030-90885-0_16

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-90884-3

  • Online ISBN: 978-3-030-90885-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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