Multi-class Arrhythmia detection from 12-lead varied-length ECG using Attention-based Time-Incremental Convolutional Neural Network

Automatic arrhythmia detection from Electrocardiogram (ECG) plays an important role in early prevention and diagnosis of cardiovascular diseases. Convolutional neural network (CNN) is a simpler, more noise-immune solution than traditional methods in multi-class arrhythmia classification. However, suffering from lack of consideration for temporal feature of ECG signal, CNN couldn’t accept varied-length ECG signal and had limited performance in detecting paroxysmal arrhythmias. To address these issues, we proposed attention-based time-incremental convolutional neural network (ATI-CNN), a deep neural network model achieving both spatial and temporal fusion of information from ECG signals by integrating CNN,…

• NS
  Natural Science Foundation of Guangdong Province

  Award: 2018A030310006
• ST
  Shenzhen Technical Project

  Award: JCYJ20170413161515911
• NM
  National Major Science and Technology Projects of China

  Award: 2017B030308007
• OE
  Overseas Expertise Introduction Project for Discipline Innovation

  Award: KQJSCX20170731165939298