The First China ECG Intelligent Competition launched ECG challenge to classify 8 kinds of abnormalities from uneven 12-lead ECGs. These abnormalities can be classified into two categories according to morphology and rhythm, four in each group. In this paper, for morphology tasks neural network is applied mainly with input median wave extracted from raw data, while traditional methods are executed and promoted by machine learning to achieve rhythm classification. Non-coexistence relationship is taken into consideration to fit in clinical significance better. The final average F1 score is 0.886 on test set, which certificates these are effective methods for ECG auto detection.


ECG abnormalities Deep learning Machine learning 



This work is supported by program ykj-2018-00393 of Technology foundation of Beijing University of Technology.


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Guanghong Bin
    • 1
  • Yongyue Sun
    • 1
  • Jiao Huang
    • 1
  • Guangyu Bin
    • 1
    Email author
  1. 1.Beijing University of TechnologyBeijingChina

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