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Edge2Analysis: A Novel AIoT Platform for Atrial Fibrillation Recognition and Detection
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  • Wanqing Wu ,
  • Jiarong Chen ,
  • yingfang zheng ,
  • Yingshan Liang ,
  • Zehui Zhan ,
  • Mingzhe Jiang ,
  • Xianbin Zhang ,
  • Daniel S. da Silva ,
  • Victor Hugo C. Albuquerque
Wanqing Wu
the School of Biomedical Engineering

Corresponding Author:[email protected]

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Jiarong Chen
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yingfang zheng
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Yingshan Liang
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Zehui Zhan
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Mingzhe Jiang
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Xianbin Zhang
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Daniel S. da Silva
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Victor Hugo C. Albuquerque
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Processing signals on edge nodes has become a popular trend, which requires data to be stored in terminals and processed, so it has the advantages of low latency, low power consumption and privacy protection. However, due to the limited resources of embedded devices, few studies have made relevant attempts. Our task was to implement a task for atrial fibrillation based on edge devices, using data from the open dataset CinC2017 and CPSC. The classification accuracy is about 90% and the model training can be completed on the edge platform. It is a feasible method to retrain the model by changing the training dataset of equipment.