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Life-threatening ventricular arrhythmia detection challenge in implantable cardioverter–defibrillators

Nature Machine Intelligence volume 5, pages 554–555 (2023)Cite this article

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The organizers of the TinyML Design Contest describe the top machine-learning-based real-time detection algorithms for ventricular arrhythmia.

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Fig. 1: Comprehensive performance metrics and the final scores of the top eight teams in TDC’22.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN, USA

    Zhenge Jia & Yiyu Shi

  2. South Central Minzu University, Wuhan, Hubei, China

    Dawei Li

  3. Guangdong Provincial Key Laboratory of South China Structural Heart Disease, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), South Medical University, Guangzhou, China

    Xiaowei Xu

  4. Singular Medical Co., Ltd, Suzhou, Jiangsu, China

    Na Li, Feng Hong & Lichuan Ping

Authors
  1. Zhenge Jia
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  2. Dawei Li
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  3. Xiaowei Xu
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  4. Na Li
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  5. Feng Hong
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  6. Lichuan Ping
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  7. Yiyu Shi
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Correspondence to Zhenge Jia, Dawei Li, Xiaowei Xu or Yiyu Shi.

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Jia, Z., Li, D., Xu, X. et al. Life-threatening ventricular arrhythmia detection challenge in implantable cardioverter–defibrillators. Nat Mach Intell 5, 554–555 (2023). https://doi.org/10.1038/s42256-023-00659-9

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