ECG-signal multi-classification model based on squeeze-and-excitation residual neural networks

Junsang Park, Jin Kook Kim, Sunghoon Jung, Yeongjoon Gil, Jong Il Choi, Ho Sung Son

Research output: Contribution to journalArticlepeer-review

26 Citations (Scopus)


Accurate electrocardiogram (ECG) interpretation is crucial in the clinical ECG workflow because it is most likely associated with a disease that can cause major problems in the body. In this study, we proposed an ECG-signal multi-classification model using deep learning. We used a squeeze-and-excitation residual network (SE-ResNet), which is a residual network(ResNet) with a squeeze-and-excitation block. Experiments were performed for seven different types of lead-II ECG data obtained from the Korea University Anam Hospital in South Korea. These seven types are normal sinus rhythm, atrial fibrillation, atrial flutter, sinus bradycardia, sinus tachycardia, premature ventricular contraction and first-degree atrioventricular block. We compared the SE-ResNet with a ResNet, as a baseline model, for various depths of layer (18/34/50/101/152). We confirmed that the SE-ResNet had better classification performance than the ResNet, for all layers. The SE-ResNet classifier with 152 layers achieved F1 scores of 97.05% for seven-class classifications. Our model surpassed the baseline model, ResNet, by +1.40% for the seven-class classifications. For ECG-signal multi-classification, considering the F1 scores, the SE-ResNet might be better than the ResNet baseline model.

Original languageEnglish
Article number6495
JournalApplied Sciences (Switzerland)
Issue number18
Publication statusPublished - 2020 Sept

Bibliographical note

Publisher Copyright:
© 2020 by the authors.


  • Arrhythmia
  • Convolutional neural network
  • Deep learning
  • ECG signal multi-classification

ASJC Scopus subject areas

  • General Materials Science
  • Instrumentation
  • General Engineering
  • Process Chemistry and Technology
  • Computer Science Applications
  • Fluid Flow and Transfer Processes


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