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Automatic Multi-label Classification in 12-Lead ECGs Using Neural Networks and Characteristic Points

  • Zhourui Xia
  • Zhenhua SangEmail author
  • Yutong Guo
  • Weijie Ji
  • Chenguang Han
  • Yanlin Chen
  • Sifan Yang
  • Long Meng
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11794)

Abstract

Electrocardiogram (ECG) signals are widely used in the medical diagnosis of heart disease. Automatic extraction of relevant and reliable information from ECG signals is a tough challenge for computer systems. This study proposes a novel 12-lead electrocardiogram (ECG) multi-label classification algorithm using a combination of Neural Network (NN) and the characteristic points. The proposed model is an end-to-end model. CNN extracts the morphological features of each ECG. Then the features of all the beats are considered in the context via BiRNN. The proposed method was evaluated on the dataset offered by The First China Intelligent Competition, and results were measured using the macro F1 score of all nine classes. Our proposed method obtained a macro F1 score of 0.878, which is excellent among the competitors.

Keywords

ECG Classification Characteristic points Multi-label 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Zhourui Xia
    • 1
  • Zhenhua Sang
    • 2
    Email author
  • Yutong Guo
    • 3
  • Weijie Ji
    • 2
  • Chenguang Han
    • 2
  • Yanlin Chen
    • 1
  • Sifan Yang
    • 1
  • Long Meng
    • 4
  1. 1.Shenzhen International Graduate SchoolTsinghua UniversityShenzhenChina
  2. 2.Beijing Tsinghua Changgung HospitalBeijingChina
  3. 3.School of Information and ElectronicsBeijing Institute TechnologyBeijingChina
  4. 4.Shandong Mingjia Technology Co., Ltd.Tai’anChina

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