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D-ECG: A Dynamic Framework for Cardiac Arrhythmia Detection from IoT-Based ECGs

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Web Information Systems Engineering – WISE 2018 (WISE 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11234))

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Abstract

Cardiac arrhythmia has been identified as a type of cardiovascular diseases (CVDs) that causes approximately \(12\%\) of all deaths globally. The current progress on arrhythmia detection based on ECG recordings is facing a bottleneck for adopting single classifier and static ensemble methods. Besides, most of the work tend to use a static feature set for characterizing all types of heartbeats, which may limit the classification performance. To fill in the gap, a novel framework called D-ECG is proposed to introduce dynamic ensemble selection (DES) technique to provide accurate detection of cardiac arrhythmia. In addition, the proposed D-ECG develops a result regulator that use different features to refine the classification result from the DES technique. The results reported in this paper have shown visible improvement on the overall heartbeat classification accuracy as well as the sensitivity of disease heartbeats.

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Author information

Authors and Affiliations

  1. Institute of Sustainable Industries and Liveable Cities, Victoria University, Melbourne, Australia

    Jinyuan He, Jia Rong, Hua Wang & Yanchun Zhang

  2. School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, China

    Le Sun

  3. College of Science and Engineering, James Cook University, Queensland, Australia

    Jiangang Ma

Authors
  1. Jinyuan He
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  2. Jia Rong
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  3. Le Sun
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  4. Hua Wang
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  5. Yanchun Zhang
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  6. Jiangang Ma
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Corresponding author

Correspondence to Jia Rong .

Editor information

Editors and Affiliations

  1. Zayed University, Dubai, United Arab Emirates

    Hakim Hacid

  2. Poznan University of Economics, Poznan, Poland

    Wojciech Cellary

  3. University of Victoria, Footscray, VIC, Australia

    Hua Wang

  4. University of New South Wales, Sydney, NSW, Australia

    Hye-Young Paik

  5. Swinburne University of Technology, Hawthorn, VIC, Australia

    Rui Zhou

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He, J., Rong, J., Sun, L., Wang, H., Zhang, Y., Ma, J. (2018). D-ECG: A Dynamic Framework for Cardiac Arrhythmia Detection from IoT-Based ECGs. In: Hacid, H., Cellary, W., Wang, H., Paik, HY., Zhou, R. (eds) Web Information Systems Engineering – WISE 2018. WISE 2018. Lecture Notes in Computer Science(), vol 11234. Springer, Cham. https://doi.org/10.1007/978-3-030-02925-8_6

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  • DOI: https://doi.org/10.1007/978-3-030-02925-8_6

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